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Recurrent Model Predictive Control

This paper proposes an off-line algorithm, called Recurrent Model Predictive Control (RMPC), to solve general nonlinear finite-horizon optimal control problems. Unlike traditional Model Predictive Control (MPC) algorithms, it can make full use of the current computing resources and adaptively select the longest model prediction horizon. Our algorithm employs a recurrent function to approximate the optimal policy, which maps the system states and reference values directly to the control inputs. The number of prediction steps is equal to the number of recurrent cycles of the learned policy function. With an arbitrary initial policy function, the proposed RMPC algorithm can converge to the optimal policy by directly minimizing the designed loss function. We further prove the convergence and optimality of the RMPC algorithm thorough Bellman optimality principle, and demonstrate its generality and efficiency using two numerical examples.

I. INTRODUCTION
M ODEL Predictive Control (MPC) is a well-known method to solve finite-horizon optimal control problems online, which has been extensively investigated in various fields [1]- [3]. However, existing MPC algorithms still suffer from a major challenge: relatively low computation efficiency [4].
One famous approach to tackle this issue is the moving blocking technique, which assumes constant control input in a fixed portion of the prediction horizon. It increases the computation efficiency by reducing the number of variables to be optimized [5]. However, this solution cannot guarantee the system stability and constraint satisfaction. In addition, Wang and Boyd (2009) proposed an early termination interiorpoint method to reduce the calculation time by limiting the maximum number of iterations per time step [6].
However, these methods are still unable to meet the online computing requirement for nonlinear and large-scale systems. Some control algorithms choose to calculate an near-optimal explicit policy offline, and then implement it online. Bemporad et al. (2002) first proposed the explicit MPC method to increase the computation efficiency, which partitioned the constrained state space into several regions and calculated explicit feedback control laws for each region [7]. During online implementation, the on-board computer only needs to choose the corresponding state feedback control law according to the current system state, thereby reducing the burden of online calculation to some extent. Such algorithms are only suitable for small-scale systems, since the required storage capacity grows exponentially with the state dimension [8].
Furthermore, significant efforts have been devoted to approximation MPC algorithms, which can reduce polyhedral state regions and simplify explicit control laws. Geyer et al. (2008) provided an optimal merging approach to reduce partitions via merging regions with the same control law [9]. Jones et al. (2010) proposed a polytopic approximation method using double description and barycentric functions to estimate the optimal policy, which greatly reduced the partitions and could be applied to any convex problem [10]. Wen et al. (2009) proposed a piecewise continuous grid function to represent explicit MPC solution, which reduced the requirements of storage capacity and improve online computation efficiency [11]. Borrelli et al. (2010) proposed an explicit MPC algorithm which can be executed partially online and partially offline [12]. In addition, some MPC studies employed a parameterized function to approximate the MPC controller. They updated the function parameters by minimizing the MPC cost function with a fixed prediction horizon through supervised learning or reinforcement learning [13]- [16].
Noted that the policy performance and the computation time for each step usually increase with the number of prediction steps. The above-stated algorithms usually have to make a trade-off between control performance and computation time constraints, and select a conservative fixed prediction horizon. While the on-board computation resources are often changing dynamically. These algorithms thus usually lead to calculation timeouts or resources waste. In other words, these algorithms cannot adapt to the dynamic allocation of computing resources and make full use of the available computing time to select the longest model prediction horizon.
In this paper, we propose an offline MPC algorithm, called Recurrent MPC (RMPC), for finite-horizon optimal control problems with large-scale nonlinearities and nonaffine inputs. Our main contributions can be summarized as below: 1) A recurrent function is employed to approximate the optimal policy, which maps the system states and reference values directly to the control inputs. Compared to previous algorithms employing non-recurrent functions (such as multi-layer NNs), which must select a fixed prediction horizon previously [13]- [16], the use of recurrent structure makes the algorithm be able to select appropriate model prediction horizon according to current computing resources. In particular, the output of the learned policy function after N recurrent cycles corresponds to the nearly optimal solution of N -step MPC. 2) A policy optimization objective is designed by decomposing the MPC cost function according to the Bellman's principle of optimality. The optimal recurrent policy can be obtained by directly minimizing the designed objective function. Therefore, unlike the traditional explicit MPC algorithms [7]- [12] that can only handle linear systems, the proposed algorithm is applicable for general nonlinear and non input-affine systems. Meanwhile, the proposed RMPC algorithm utilizes the recursiveness of Bellman's principle. When the cost function of the longest prediction is optimized, the cost function of short prediction will automatically be optimal. Thus the proposed algorithm can deals with different shorter prediction horizons problems while only training with an objective function with respect to a long prediction horizons. Other MPC algorithms [7]- [16]do not consider the recursiveness of Bellman's principle, when the prediction horizons changes, the optimization problem must be reconstructed and the training or computing process must be re-executed to deal with the new problem.
3) The proposed RMPC algorithm calculates the optimal control policy previously so only needs to eval the forward inferencing process while using online.Expriments shows that it is over 5 times faster than the traditional MPC algorithms [17], [18] under the same problem scale. The paper is organized as follows. In Section II, we provide the formulation of the MPC problem. Section III presents RMPC algorithm and proves its convergence. In Section IV, we present simulation demonstrations that show the generalizability and effectiveness of the RMPC algorithm. Section V concludes this paper.

II. PRELIMINARIES
Consider general time-invariant discrete-time dynamic system x with state x i ∈ X ⊂ R n , control input u i ∈ U ⊂ R m and the system dynamics function f : R n × R m → R n . We assume that f (x i , u i ) is Lipschitz continuous on a compact set X , and the system is stabilizable on X . Define the cost function V (x 0 , r 1:N , N ) of the N -step Model Predictive Control (MPC) problem where x 0 is initial state, N is length of prediction horizon, is the N -step cost function of state x 0 with reference r 1:N , u N i−1 is the control input of the ith step in N -step prediction, and l ≥ 0 is the utility function. The purpose of MPC is to find the optimal control sequence to minimize the objective V (x 0 , r 1:N , N ), which can be denoted as where the superscript * represents optimal.

A. Recurrent Policy Function
In practical applications, we only need to execute the first control input u N 0 * (x 0 , r 1:N ) of the optimal sequence in (3) at each step. Given a control problem, assume that N max is the maximum feasible prediction horizon. Our aim is to make full use of computation resources and adaptively select the longest prediction horizon k ∈ [1, N max ], which means that we need to calculate and store the optimal control input u k 0 * (x, r 1:k ) of ∀x ∈ X , ∀r 1:k and ∀k ∈ [1, N max ] in advance. This requires us to find an efficient way to represent the policy and solve it offline.
We firstly introduce a recurrent function, denoted as π c (x 0 , r 1:c ; θ), to approximate the control input u c 0 * (x 0 , r 1:c ), where θ is the vector of function parameters and c is the number of recurrent cycles of the policy function. The goal of the proposed Recurrent MPC (RMPC) algorithm is to find the optimal parameters θ * , such that The structure of the recurrent policy function is illustrated in Fig. 1. All recurrent cycles share the same parameters θ, where h c ∈ R q is the vector of hidden states. Each recurrent cycle is mathematically described as where h 0 = 0, σ h and σ y are activation functions of hidden layer and output layer, respectively. As shown in Fig. 1, the recurrent policy function calculates and outputs a control input at each recurrent cycle. Assuming that we have found the optimal parameters θ * , it follows that the output of the cth cycle π c (x 0 , r 1:c ; θ * ) = u c 0 * (x 0 , r 1:c ) for ∀c ∈ [1, N max ]. This indicates that the more cycles, the longer the prediction horizon. In practical applications, the calculation time of each cycle t c is different due to the dynamic change of computing resource allocation (see Fig. 2). At each time step, the total time assigned to the control input calculation is assumed to be T . Denoting the total number of the recurrent cycles at each time step as k, then the control input is π k (x 0 , r 1: Therefore, the recurrent policy is able to make full use of computing resources and adaptively select the longest prediction step k. In other word, the more computing resources allocated, the longer prediction horizon will be selected, which usually would lead to the better control performance.
Remark 1. Previous MPC algorithms employs non-recurrent form neural networks [13]- [16], which must select a fix prediction horizon previously. RMPC employes recurrent function to approximate the optimal policy, which maps the system states and reference values directly to the control inputs. The use of recurrent structure makes the algorithm be able to select appropriate model prediction horizon according to current computing resources. The output of the learned policy network after N recurrent cycles corresponds to the nearly optimal solution of N -step MPC.

B. Objective Function
To find the optimal parameters θ * offline, we first need to represent the MPC cost function in (2) in terms of θ, denoted by V (x 0 , r 1:N , N ; θ). From (2) and the Bellman's principle of optimality, the global minimum V * (x 0 , r 1:N , N ) can be expressed as:

It follows that
Therefore, for the same x 0 and r 1:N , it is clear that This indicates that the ith optimal control input u N i−1 * (x 0 , r 1:N ) in (3) can be regarded as the optimal control input of the N -i+1-step MPC control problem with initial state x i−1 . Hence,by replacing all ). Then, we can obtain the N -step cost function in terms of θ: To find the optimal parameters θ * that make (4) hold, we can construct the following objective function: Therefore, we can update θ by directly minimizing J(θ). The policy update gradients can be derived as where dV (x 0 , r 1: Denoting π Nmax−i+1 (x i−1 , r i:Nmax ; θ) as π Nmax−i+1 and By defining two immediate variables, φ i = dxi dθ and ψ i = dπ Nmax −i+1 dθ , we have their recursive formula and the details of gradient backpropagation are shown in Fig.3.
Therefore, the gradient formula can be simplified as: Taking the Gradient Descent (GD) method as an example, the updating rules of the policy function are where α θ denotes the learning rate and K indicates Kth iteration.
The framework and pseudo-code of the proposed RMPC algorithm is shown in Algorithm 1 and Fig. 4.

Algorithm 1 RMPC algorithm
Given an appropriate learning rate α θ and any arbitrarily small positive number . Initial with arbitrary θ 0 repeat Randomly select x 0 , r 1:Nmax ∈ X Calculate dJ(θ K ) dθ K using (10) Update policy function using (11) until |J(θ K+1 ) − J(θ K )| ≤ Remark 2. Traditional explicit MPC algorithms [7]- [12] can only handle linear systems.The proposed RMPC algorithm uses an optimization objective designed by decomposing the MPC cost function according to the Bellman's principle of optimality. The optimal recurrent policy can be obtained by directly minimizing the designed objective function without restrictions on the form of systems. Meanwhile, the proposed algorithm utilizes the recursiveness of Bellman's principle. When the cost function of the longest prediction is optimized, the cost function of short prediction will automatically be optimal. Thus the proposed algorithm can deals with different shorter prediction horizons problems while only training with an objective function with respect to a long prediction horizons. Other MPC algorithms [7]- [16]do not consider the recursiveness of Bellman's principle, when the prediction horizons changes, the optimization problem must be reconstructed and the training or computing process must be re-executed to deal with the new problem.

C. Convergence and Optimality
There are many types of recurrent functions belonging to the structure defined in (5), and recurrent neural networks (RNN) are the most commonly used. In recent years, deep RNNs have been successfully implemented in many fields, such as natural language processing and system control, attributing to their ability to process sequential data [19], [20]. Next, we will show that as the iteration index K → ∞, the optimal policy π c (x 0 , r 1:c ; θ * ) that make (4) hold can be achieved using Algorithm 1, as long as π c (x 0 , r 1:c ; θ) is an overparameterized RNN. The over-parameterization means that the number of hidden neurons is sufficiently large. Before the main theorem, the following lemma and assumption need to be introduced.
where c is the number of recurrent cycles, W = W h ∪ W y , b = b h ∪ b y and U h are parameters, σ h and σ y are activation functions. Supposing G c (y c ; W, b) is over-parameterized, for where ∈ R + is an arbitrarily small error.
The reported experimental results and theoretical proofs have shown that the straightforward optimization methods, such as GD and Stochastic GD (SGD), can find global minima of most training objectives in polynomial time if the approximate function is an over-parameterized neural network or RNN [24], [25]. Based on this fact, we make the following assumption.
Assumption 1. If the approximate function is an overparameterized RNN, the global minimum of objective function in (9) can be found using an appropriate optimization algorithm such as SGD [26].
We now present our main result.
Thus, we have proven that RMPC algorithm can converge to θ * . In other words, it can find the nearly optimal policy of MPC with different prediction horizon, whose output after cth recurrent cycles corresponds to the nearly optimal solution of c-step MPC.

IV. ALGORITHM VERIFICATION
In order to evaluate the performance of the proposed RMPC algorithm, we choose the vehicle lateral control problem in path tracking task as an example [27].

A. Overall Settings
The policy network is trained offline on the PC, and then deployed to the industrial personal computer (IPC). The vehicle dynamics used for policy training are different from the controlled plant. For online applications, the IPveC-controller gives the control signal to the plant according to the state information and the reference trajectory. The plant feeds back the state information to the IPC-controller, so as to realize the closed-loop control process. The feedback scheme of the HIL experiment is depicted in Fig 5. The type of IPC-controller is ADLINK MXC-6401, equipped with Intel i7-6820EQ CPU and 8GB RAM, which is used as a vehicle on-board controller [28]. The plant is a real-time system, simulated by the vehicle dynamic model of CarSim [29]. The longitudinal speed is assumed to be constant, v x = 16m/s, and the expected trajectory is shown in Fig. 10. The system states and control inputs of this problem are listed in Table I, and the vehicle parameters are listed in Table II.

B. Problem Description
The offline policy is trained based on the nonliner and non input-affine vehicle dynamics: where F yf and F yr are the lateral tire forces of the front and rear tires respectively [30]. The lateral tire forces can be approximated according to the Fiala tire model: where α # is the tire slip angle, F z# is the tire load, µ # is the friction coefficient, and the subscript # ∈ {f, r} represents the front or rear tires. The slip angles can be calculated from the relationship between the front/rear axle and the center of gravity (CG): The loads on the front and rear tires can be approximated by: The utility function of this problem is set to be u i−1 Therefore, the policy optimization problem of this example can be formulated as: where V (x 0 , r 1:

C. Algorithm Details
The policy function is represented by a variant of RNN, called GRU (Gated Recurrent Unit). The input layer is composed of the states, followed by 4 hidden layers using rectified linear unit (RELUs) as activation functions with 128 units per layer, and the output layer is set as a tanh layer, multiplied by 0.2 to confront bounded control. We use Adam method to update the network with the learning rate of 2 × 10 −4 and the batch size of 256.

D. Result Analysis
For nonlinear MPC problems, we can solve it with some optimization solvers, such as ipopt [17] and bonmin [18], which can be approximately regarded as the numerical optimal solution. Fig. 6 compares the calculation efficiency of RMPC and the optimization solvers based on the symbolic framework CasADi [31] under different prediction steps for online applications. It is obvious that the calculation time of the optimization solvers is much longer than RMPC, and the gap increases with the number of prediction steps. Specifically, when N = c = 15, the fastest optimization solver ipopt is over 5 times slower than RMPC (ipopt for 26.2ms, RMPC for 4.7ms). This demonstrates the effectiveness of the RMPC method. We run Algorithm 1 for 10 times and calculate the policy error e N between the solution of ipopt solver and RMPC at each iteration for N ∈ [1,15], where u N max * and u N min * are respectively the maximum and minimum value of u N 0 * (x 0 , r 1:N ) for ∀x 0 , r 1:N ∈ X , ∀N ∈ [1,15], N is the number of prediction steps. e N indicates the relative error of control quantity π N (·) from cycle network respect to the optimum u N * 0 (·) in N step prediction control problem . In Fig. 7, we plot policy error curves during training with different prediction steps N . It is clear that all the policy errors decrease rapidly to a small value during the training process. In particular, after 10 4 iterations, policy errors for all N ≥ 5 reduce to less than 2%. This indicates that Algorithm 1 has the ability to find the near-optimal policy of MPC problems with different prediction horizons N . Fig. 8 shows the policy performance of the ipopt solver solution and learned policy π c (x 0 , r 1:c ; θ) with different prediction horizons. The policy performance is measured by the lost function of 200 steps (10s) during the simulation period staring from random initialized state, i.e., For all prediction domains N , the learned policy performs as well as the solution of ipopt solver. More recurrent cycles (or long prediction steps) help reduce the accumulated cost L.  compares the control output between the learned policy(after 15 recurrent cycles) with ipopt controller.Obviously, the trajectory controlled by RMPC controller almost overlaps with the ipopt controller. The more recurrent cycles of the learned policy, the smaller the trajectory tracking error.This is why we want to adaptively select the optimal law with longest prediction horizon in real applications.
To summarize, the example demonstrates the optimality, efficiency and generality of the RMPC algorithm.

V. CONCLUSION
This paper proposes the Recurrent Model Predictive Control (RMPC) algorithm to solve general nonlinear finite-horizon optimal control problems. Unlike traditional MPC algorithms, it can make full use of the current computing resources and adaptively select the longest model prediction horizon. Our algorithm employs an RNN to approximate the optimal policy, which maps the system states and reference values directly to the control inputs. The output of the learned policy network after N recurrent cycles corresponds to the nearly optimal solution of N -step MPC. A policy optimization objective is designed by decomposing the MPC cost function according to the Bellman's principle of optimality.The optimal recurrent policy can be obtained by directly minimizing the designed objective function, which is applicable for general nonlinear and non input-affine systems. The convergence and optimality of RMPC is further proved. We demonstrate its optimality, generality and efficiency using a HIL experiment. Results show that RMPC is over 5 times faster than the traditional MPC algorithm. The control performance of the learned policy can be further improved as the number of recurrent cycles increases.


OpenSSLNTRU: Faster post-quantum TLS key exchange

Google's CECPQ1 experiment in 2016 integrated a post-quantum key-exchange algorithm, newhope1024, into TLS 1.2. The Google-Cloudflare CECPQ2 experiment in 2019 integrated a more efficient key-exchange algorithm, ntruhrss701, into TLS 1.3. This paper revisits the choices made in CECPQ2, and shows how to achieve higher performance for post-quantum key exchange in TLS 1.3 using a higher-security algorithm, sntrup761. Previous work had indicated that ntruhrss701 key generation was much faster than sntrup761 key generation, but this paper makes sntrup761 key generation much faster by generating a batch of keys at once. Batch key generation is invisible at the TLS protocol layer, but raises software-engineering questions regarding the difficulty of integrating batch key exchange into existing TLS libraries and applications. This paper shows that careful choices of software layers make it easy to integrate fast post-quantum software, including batch key exchange, into TLS with minor changes to TLS libraries and no changes to applications. As a demonstration of feasibility, this paper reports successful integration of its fast sntrup761 library, via a lightly patched OpenSSL, into an unmodified web browser and an unmodified TLS terminator. This paper also reports TLS 1.3 handshake benchmarks, achieving more TLS 1.3 handshakes per second than any software included in OpenSSL.

Introduction
The urgency of upgrading TLS to post-quantum encryption has prompted a tremendous amount of work. There were already 69 proposals for post-quantum cryptography (PQC) submitted to NIST's Post-Quantum Cryptography Standardization Project in 2017, including 49 proposals for post-quantum encryption. Each proposal included complete software implementations of the algorithms for key generation, encryption, and decryption. Given the cryptographic agility of TLS, one might imagine that TLS software can simply pick a post-quantum algorithm and use it. Constraints that make this more difficult than it sounds include the following: • Performance: Post-quantum algorithms can send much more data than elliptic-curve cryptography (ECC), and can take many more CPU cycles. Performance plays a "large role" [27] in the NIST standardization project. • Integration: Many assumptions about how cryptography works are built into the TLS protocol and existing TLS software. These range from superficial assumptions about the sizes of objects to more fundamental structural assumptions such as the reliance of TLS 1.3 upon "Diffie-Hellman"-a key-exchange data flow not provided by any of the proposals for NIST standardization. • Security: 30 of the 69 proposals were broken by the end of 2019 [9]. New attacks continue to appear: e.g., [6] uses under a single second of CPU time to break any ciphertext sent by the "Round2" lattice-based proposal.
In July 2020, the NIST project began its third round [1], selecting 4 "finalist" and 5 "alternate" encryption proposals to consider for standardization at the end of the round and after a subsequent round. Meanwhile, there have been various experiments successfully integrating post-quantum encryption systems into TLS. The proposals that have attracted the most attention, and that are also the focus of this paper, are "small" lattice proposals. These include • three of the finalist proposals (Kyber [4], NTRU [17], and SABER [5]), although NIST says it will standardize at most one of these three; • one of the alternate proposals (NTRU Prime); • the newhope1024 algorithm [2] used inside Google's CECPQ1 experiment in 2016; and • the ntruhrss701 algorithm (a variant of one of the algorithms in the NTRU proposal) used inside the Google-Cloudflare CECPQ2 experiment in 2019.
These are called "small" because they use just a few kilobytes for each key exchange-much more traffic than ECC, but much less than many other post-quantum proposals. Table 1: Cryptographic features of the post-quantum components of CECPQ2 (previous work) and OpenSSLNTRU (this paper). Core-SVP in the table is pre-quantum Core-SVP (see [12,Section 6]); post-quantum Core-SVP has 10% smaller exponents. See [13] regarding cyclotomic concerns. The ntruhrss701 cycle counts are from supercop-20210423 [10] on hiphop (Intel Xeon E3-1220 V3). The sntrup761 cycle counts are old→new, where "old" shows the best sntrup761 results before our work and "new" shows results from this paper's freely available software; Appendix A presents the slight enc and dec speedups, and Section 3 presents the large keygen speedup.

Contributions of this paper
This paper introduces OpenSSLNTRU, an improved integration of post-quantum key exchange into TLS 1.3. OpenSSLNTRU improves upon the post-quantum portion of CECPQ2 in two ways: key-exchange performance and TLS software engineering. These are linked, as explained below. OpenSSLNTRU offers multiple choices of key sizes; for concreteness we emphasize one option, sntrup761 [12], to compare to CECPQ2's ntruhrss701.
Each of ntruhrss701/sntrup761 is a "key-encapsulation mechanism" (KEM) consisting of three algorithms: a keygeneration algorithm generates a public key and a corresponding secret key; an "encapsulation" algorithm, given a public key, generates a ciphertext and a corresponding session key; a "decapsulation" algorithm, given a secret key and a ciphertext, generates the corresponding session key. The key exchange at the beginning of a TLS session involves one keygen, one enc, and one dec. Before our work, both KEMs already had software optimized for Intel Haswell using AVX2 vector instructions; keygen was 3.03× slower for sntrup761 than for ntruhrss701, making total keygen+enc+dec 2.57× slower.
One can remove keygen cost by reusing a key for many TLS sessions (see Section 2.5.2). This paper instead directly addresses the speed problem with sntrup761 key generation, by making sntrup761 key generation much faster. Our sntrup761 software outperforms the latest ntruhrss701 software, and at the same time sntrup761 has a higher security level than ntruhrss701. See Table 1.
The main bottleneck in sntrup761 key generation is computation of certain types of inverses. This paper speeds up those inversions using "Montgomery's trick", the simple idea of computing two independent inverses 1/a and 1/b as br and ar respectively, where r = 1/ab. Repeating this trick converts, e.g., 32 inversions into 1 inversion plus 93 multiplications. This paper generates a batch of 32 independent keys, combining independent reciprocals across the batch. This batch size is large enough for inversion time to mostly disappear, and yet small enough to avoid creating problems with latency, cache misses, etc. We designed new algorithms and software to optimize sntrup761 multiplications, since the multiplications used previously were "big×small" multiplications while Montgomery's trick needs "big×big" multiplications; see Section 3.
A new key sent through TLS could have been generated a millisecond earlier, a second earlier, or a minute earlier; this does not matter for the TLS protocol. However, for TLS software, batching keys is a more interesting challenge, for two reasons. First, key generation is no longer a pure stateless subroutine inside one TLS session, but rather a mechanism sharing state across TLS sessions. Second, the TLS software ecosystem is complicated (and somewhat ossified), with many different applications using many different libraries, so the same state change needs to be repeated in many different pieces of TLS software.
To address the underlying problem, this paper introduces a new choice of software layers designed to decouple the fast-moving post-quantum software ecosystem from the TLS software ecosystem. The point of these layers is that optimization of post-quantum software does not have to worry about any of the complications of TLS software, and vice versa. As a case study demonstrating the applicability of these layers, this paper describes successful integration of its new sntrup761 library, including batch key generation, into an existing web browser communicating with an existing TLS terminator, using OpenSSL on both ends. This demo involves no changes to the web browser, no changes to the TLS terminator, and very few changes to OpenSSL.
The integration of OpenSSLNTRU into TLS means that, beyond microbenchmarks, we can and do measure full TLS handshake performance. The bottom line is that, in a controlled and reproducible end-to-end lab experiment, sntrup761 completes more sessions per second than commonly deployed pre-quantum NIST P-256, and even completes more sessions per second than commonly deployed pre-quantum X25519 (see Section 4.4). This remains true even when we replace sntrup761 with higher-security sntrup857.

Montgomery's trick for batch inversion
In this section, we review Montgomery's trick for batch inversion [24] as applied to many inputs. The algorithm batchInv takes n elements (a 1 , a 2 , . . . , a n ) in a ring, and outputs their multiplicative inverses (a −1 1 , a −1 2 , . . . , a −1 n ). Montgomery's trick for batch inversion proceeds as follows: After n − 1 multiplications, we obtain

Output
In summary, the algorithm uses 3n − 3 multiplications and one inversion to compute n inverses.

NTT-based multiplication
This section reviews techniques for polynomial multiplication commonly used in lattice-based cryptography. We adopt terminology from [7]. The number theoretic transform (NTT) algorithm maps an element in a polynomial ring into values by lifting the ring element to a polynomial and evaluating the polynomial on a particular set. An NTT-based multiplication algorithm applies NTTs to two input elements in the polynomial ring, performs component-wise multiplication for the transformed values, and applies an inverse NTT, converting the multiplied values back to the product in the same form of inputs.
Computing a size-n NTT, where n is a power of 2, comprises log 2 n stages of the radix-2 FFT trick. Given a polynomial ring ). Due to the Chinese remainder theorem (CRT), the mapping is invertible when 2b is invertible. Specifically, with n multiplications by b, n/2 additions, and n/2 subtractions. Setting b = 1, by recursively applying the FFT trick, an NTT transforms f into a listf = (f 0 , . . . ,f j , . . . ,f n−1 ) ∈ (Z/q) n wheref j = f mod (x − ψ j ) = ∑ n−1 i=0 f i ψ i j , and ψ ∈ Z/q is a primitive n-th root of unity, i.e., ψ n/2 = −1.

The AVX2 instruction set
Since NIST specified Intel Haswell CPU as its highest priority platform for performance evaluation [26], we optimize sntrup for the Haswell architecture in this work.
Specifically, we target the Advanced Vector Extensions 2 (AVX2) instruction set. AVX is a single-instruction-multipledata (SIMD) instruction set in modern (decade or less) x86 CPUs. It provides sixteen 256-bit ymm registers; each ymm register splits into two 128-bit xmm lanes. The instruction set treats data in ymm registers as lanes (independent partitions) of 32×8-bit, 16×16-bit, 8×32-bit, etc.; every instruction operates simultaneously on the partitioned data in the ymm registers. In 2013, the Haswell architecture extended AVX to AVX2 for enhanced integer operations.

Related works 2.5.1 NTT-based multiplication in other PQC finalists
Among the lattice based KEM of NIST's finalists, Kyber [4] operates in a radix-2 NTT friendly polynomial ring and implements NTT-based multiplication in the proposal. SABER [5] and NTRU [17] operate in polynomial rings with a powerof-two modulus which are considered NTT-unfriendly. The earlier implementations of two schemes used a combination of Toom-4 and Karatsuba based polynomial multiplication.
Recently, [18] showed that NTT-based multiplication outperforms previous Toom-Cook multiplication for implementing NTT-unfriendly SABER and most parameters of NTRU. To use NTT-based multiplication in an NTT-unfriendly ring, they raise the coefficients to a combination of several NTTfriendly polynomial rings, perform several NTT-based multiplications, and map back to original ring with CRT. For NTRU on the AVX2 platform, they reported significant improvement for parameters with polynomials of degree greater then 700. For Saber, they also reported a pronounced performance gain although the degree of polynomials are only 255. It is because the matrix-vector multiplication allows them to save the input NTT transforms for the elements in the common vector which performs inner products with different rows in the matrix.

Integrating cryptographic primitives
Related to OpenSSLNTRU, several previous works studied integrations between post-quantum implementation and real world applications and protocols.
The Open Quantum Safe (OQS) project [34] includes a library of quantum-resistant cryptographic algorithms, and prototype integrations into protocols and applications. It also includes (and requires) a fork of the OpenSSL project. Conversely, in our contribution we apply a minimal patchset, striving to maintain API and ABI compatibility with the OpenSSL version available to the end-users. This avoids the need of recompiling existing applications to benefit from the new library capabilities. While [34] focused primarily on key agreement, the OQS OpenSSL fork does also support signatures and certificates using post-quantum algorithms, and their negotiation in TLS. See [29] for a study, conducted using OQS, benchmarking post-quantum TLS authentication. We also note that the end-to-end experiment we present in this paper is limited to one candidate and two sets of parameters (sntrup761 and sntrup857), while the OQS project provides implementations for all finalists.
Similarly, the PQClean project [22] collects a number of implementations for the candidates. However, it does not aim to include integration into higher-level applications or protocols.
CECPQ2 actually included two experiments: CECPQ2a used ntruhrss701, while CECPQ2b used an isogeny-based proposal. Compared to ntruhrss701, the isogeny-based pro-posal had smaller keys and smaller ciphertexts, but used much more CPU time, so it outperformed CECPQ2a only on the slowest network connections.
In general, the importance of a few kilobytes depends on the network speed and on how often the application creates new TLS sessions. A typical multi-megabyte web page is unlikely to notice a few kilobytes, even if it retrieves resources from several TLS servers. A session that encrypts a single DNS query is handling far less data, making the performance of session establishment much more important. Similar comments apply to CPU time.
Schwabe, Stebila, and Wiggers [32] present an alternative to the TLS 1.3 handshake to solve both key exchange and authentication using post-quantum KEM. In contrast, for our experiment we aimed at full compatibility with the TLS 1.3 ecosystem, focusing exclusively on the key exchange. This ensures post-quantum confidentiality, but does not address the post-quantum authentication concerns. Therefore, showcasing how at the protocol level our experiment does not alter the TLS 1.3 message flow, in Figure 1 we only highlight the cryptographic operations and material involved in the key exchange-carried in the ClientHello and ServerHello messages-while keys and signatures used for authentication-as part of the Certificate and CertificateVerify messages-do not address post-quantum concerns.
Our approach to OpenSSL integration via an ENGINE module is based on the methodology suggested in [36], where the authors instantiated libsuola. In this context, an ENGINE module is a a dynamically loadable module. Using a dedicated API, such a module is capable of injecting new algorithms or overriding existing ones. The implementations it provides can be backed by a hardware device, or be entirely software based. Our new ENGINE, engNTRU, builds upon libbecc [15], which is itself derived from libsuola. Both previous works applied the ENGINE framework to integrate alternative ECC implementations. The latter is particularly close to engNTRU, as it also featured a transparent mechanism to handle batch key generation. Section 4.2 details how engNTRU evolved from these works and the unique features it introduces.
Shacham and Boneh [33] integrated RSA batching to improve SSL handshake performance already in 2001. However, their methodology required integrating changes directly in the server application. In contrast, OpenSSLNTRU acts on the middleware level, transparent to client and server applications.
Comparison table. Based on the previous discussions in this section, Table 2 compares select TLS integration experiments regarding post-quantum algorithms.
The "Hybrid" criterion tracks approaches that simultaneously protect the key agreement with "traditional" (usually ECC) and post-quantum encryption (see, e.g., [14,35]). This paper does not make recommendations for or against hybrids; our performance and software-engineering contributions are equally applicable to hybrid and non-hybrid scenarios.   3 yes yes yes yes PQ-sec. auth. 4 opt. no yes no TLS 1.3 compat. 5 yes yes no yes Binary compat. 6 no no no yes 1 Key-agreement uses ECC and post-quantum encryption. 2 Key-agreement provides Perfect Forward Secrecy. 3 Post-quantum security over TLS key agreement. 4 Post-quantum security over TLS authentication, inherently limited by access to PQ PKI. 5 Requires no breaking changes to the TLS 1.3 message flow. 6 ABI compatible, to easily integrate into the existing software ecosystem. 7 [34] presents experimental results for both post-quantum and hybrid KEMs. Using the OQS fork of OpenSSL, the choice of supported KEMs and order of preference is left to developers and system administrators. ure 1 illustrates how any NIKE system can be transformed into an equivalent KEM construction; a protocol that supports key exchange via KEM can support hybrid handshakes by simply composing two or more underlying KEMs to obtain a hybrid KEM. The "PFS" criterion tracks approaches that provide the traditional notion of Perfect Forward Secrecy w.r.t. the key agreement phase of the handshake. Different experiments take post-quantum security into consideration at different cryptosystem components, tracked by the "key agreement" and "authentication" criteria. The latter comes with the caveat that the extent to which PQ authentication is achieved is inherently limited by access to a fully post-quantum Public Key Infrastructure (PKI). In the specific case of the Internet Web PKI, client and server need to share a chain of certificates up to a common root of trust, entirely signed with PQ algorithms. Some experiments require breaking changes to the TLS 1.3 message flow, depicted in Figure 1; "compatibility" tracks this criterion. Lastly, our work is the only experiment we are aware of that achieves ABI compatibility ("Binary") to easily integrate into the existing software ecosystem.

Batch key generation for sntrup
This section presents batch key generation for sntrup and its optimization. Section 3.1 shows the batch key generation algorithm with Montgomery's inversion-batching trick. Section 3.2 and Section 3.3 present our polynomial multiplication and its optimization in (Z/3)[x] and (Z/q)[x], respectively. Section 3.4 shows the benchmark results.

Batch key generation
The sntrup key generation algorithm KeyGen outputs an sntrup key pair. It proceeds as follows:  1. Generate a uniform random small element g ∈ R . Repeat this step until g is invertible in R /3. 2. Compute 1/g in R /3.

3.
Generate a uniform random f ∈ Short.

Compute
Algorithm 1 (BatchKeyGen) batches sntrup key generation. We use two lists for storing n batches of g ∈ R and f ∈ Short, then process the n batches of computation in one subroutine. The key idea is to replace the 2n inversions by two batchInv for R /3 and R /q, respectively. As seen in Section 2.2, batchInv turns n inversions into 3n − 3 multiplications and one inversion. Considering performance, ring multiplication then becomes the critical part. Hence, Section 3.2 and Section 3.3 present optimized ring multiplication implementations, used in batchInv.
Another difference is the invertibility check in R /3 for the element g. Previous NTRU Prime software checks invertibility as a side effect of computing 1/g with a constant-time algorithm [11] for extended GCD. Calling batchInv removes this side effect and requires a preliminary check for invertibility of each g. In Section 3.1.1 we optimize an isInvertible subroutine for this test.

Algorithm 1 BatchKeyGen
Input : an integer n Output: n key pairs of sntrup an empty list 2: F ← [·] 3: while len(G) < n do 4: G.append(g) 8: F.append( f ) 9: end while 10: At a high level, we check the invertibility of an element g ∈ R /3 by computing its remainder of division by the irreducible factors of x p − x − 1 modulo 3, as suggested in [12, p. 8]. This section optimizes this computation.
For convenience, we always lift the ring element g to its polynomial form g ∈ (Z/3)[x] in this section. In a nutshell, if g mod f i = 0 for any factor f i of x p − x − 1, then g is not We calculate the remainder of g mod f i with Barrett reduction [23]. Suppose the polynomial we calculate the reminder r = g mod f i as follows. In the pre-computation step, choose D g > deg(g) and D f i > deg( f i ), and calculate q x as the quotient of the division x D g / f i , i.e., q x = x D g / f i , where the floor function · removes the negative-degree terms from a series. In the online step, compute h = g · q x /x D g = g/ f i , i.e., the quotient of the division g · q x /x D g . Finally, return the remainder r = g − h · f i . We show this gives the correct r in Appendix C.
Some observations about the degree of polynomials help to accelerate the computation. While computing h = g · q x /x D g , we compute only terms with degree in the interval [0, D f ), since r = g − h · f i uses terms exclusively from this interval for deg(r) < deg( f i ).
In the case of sntrup761, the polynomial has three factors, with degrees deg( f 1 ) = 19, deg( f 2 ) = 60, and deg( f 3 ) = 682, respectively. We choose D f 1 = 32, D f 2 = 64, and D g = 768 for computing g mod f 0 and g mod f 1 . For computing g mod f 3 , we note the pre-computed quotient q x = x 768 /(x 682 + · · · ) satisfies deg(q x ) = 88. Hence, the multiplication h = g · q x /x 768 involves deg(g) = 768 and deg(q x ) = 88 polynomials. By partitioning the longer polynomial into several shorter segments, we perform the multiplication by several polynomial multiplications of length equal to the shorter polynomial (less than 128). Therefore, to check invertibility, we use polynomial multiplications in (Z/3)[x] with lengths in {32, 64, 128}.

Polynomial multiplication in (Z/3)[x]
In this section, we describe our multiplication in (Z/3)[x] for sntrup, and its optimization in the AVX2 instruction set.
Based on the polynomial lengths, we implement polynomial multiplication with different algorithms. We build a 16 × 16 polynomial multiplier as a building block for schoolbook multiplication. We then use Karatsuba to build longer multipliers, such as 32 × 32, 64 × 64, and further 2 i × 2 i . For 3 · 256 × 3 · 256 multiplications, we start from Bernstein's 5way recursive algorithm [8] for (Z/2)[x] and optimize the same idea for (Z/3)[x].

Base polynomial multiplier
For representing (Z/3)[x] polynomials, we adjust the values of coefficients to unsigned form and store polynomials as byte arrays, with one coefficient per byte. For example, we store the polynomial a 0 + · · · + a 15 x 15 ∈ (Z/3)[x] as a byte array (a 0 , a 1 , . . . , a 15 ) in a 16-byte xmm register.
Besides a byte array, we can view a polynomial as an integer by translating the monomial x = 256. For example, a degree-3 polynomial a 0 + a 1 x + a 2 x 2 + a 3 x 3 maps to the 32bit integer a 0 + a 1 · 2 8 + a 2 · 2 16 + a 3 · 2 24 .
In this 32-bit format, we can perform a 4 × 4 → 8 polynomial multiplication using a 32 × 32 → 64 integer multiplication, taking care to control the coefficient values. While calculating the polynomial product fitting in a byte. Hence, we use 4 × 4 polynomial multiplication (i.e., 32 × 32 → 64 integer multiplication), as our building block to implement 16 × 16 polynomial multiplication with the schoolbook algorithm.

Multiplying polynomials of length 3n
This section reduces a multiplication of 3n-coefficient polynomials in (Z/3)[x] to 5 multiplications of ≈n-coefficient polynomials, while optimizing the number of additions using techniques analogous to Bernstein's optimizations [8] for . This section also streamlines the computation for ≤(3n − 1)-coefficient polynomials, as in sntrup.
Take two polynomials F 0 + F 1 t + F 2 t 2 and G 0 + Here are the only five polynomial multiplications in the algorithm. These polynomials expand from n to 2n terms, except H(x).
H simplifies to where There are two tricky issues while computing V . First, deg(H(x)) ≤ 2n + 2, introducing extra complexity since all other polynomials have degree less than 2n. By requiring deg(F 2 ) ≤ n − 2 and deg(G 2 ) ≤ n − 2, we force deg(H(x)) ≤ 2n. Since H(x) is only used as H(x)/x in V , we can always process polynomials with degree less than 2n.
The other issue concerns computing divisions by Since long division is a sequential process and not efficient in SIMD settings, we now present a divide-andconquer method for it.

Division by
Dividing a polynomial f by x 2 − 1 means producing a representation of f = q · (x 2 − 1) + r, where q and r = r 1 x + r 0 are the quotient and remainder, respectively. Assume that we have recursively divided two 2m-coefficient polynomials f and g by x 2 − 1, obtaining f = q · (x 2 − 1) + r and g = s · (x 2 − 1) +t. Then We carry out these divisions in place as follows: recursively overwrite the array of f coefficients with q and r, recursively overwrite the array of g coefficients with s and t, and then simply add the lowest two coefficients from the f array into every coefficient pair in the g array. Because the recursive computations for f and g are independent, this computation parallelizes. The overall parallel computation for dividing a length-n polynomial by x 2 − 1, assuming n = 2 l , proceeds as follows. The computation comprises l − 1 steps. The first step splits the polynomial into n/4 separate sub-polynomials; each sub-polynomial has degree less than four. We divide a length-four sub-polynomial by x 2 − 1 by adding two coefficients of higher degrees to the lower two coefficients. We perform these divisions in parallel. In each subsequent step, we double the sub-polynomial sizes, and divide sub-polynomials by x 2 − 1 by adding two coefficients of lower degree from the higher degree parts to the lower parts of the polynomials as in Equation 2. Since each step performs n/2 additions, the whole computation costs n(log 2 (n) − 1)/2 additions.

AVX2 optimization for the R /3 multiplier
Since we use integer arithmetic for Z/3 and integers grow, we must control the values to prevent overflow. From the AVX2 instruction set, we use the vpshufb instruction to reduce the values. The instruction reads the lower nibbles as indexes from single-byte lanes of a register, then replaces the lane values with those from a 16-entry table, using the four-bit indexes. Thus, we use vpshufb to reduce integers in [0, 16) to integers in [0, 3). We also reduce adjacent nibbles by moving them to lower positions using bit-shift instructions.
Our software for 16 × 16 polynomial multiplication actually performs two independent 16 × 16 multiplications in the two xmm lanes of ymm registers, respectively. The approach avoids the high latency for moving data between different xmm lanes in Haswell CPUs (see [21, p. 237] for the vperm2i128, vextracti128, and vinserti128 instructions). Specifically, our AVX2 multiplier takes two ymm registers as input and outputs products in two ymm registers. A ymm register comprises two polynomials (a, c) where a, c ∈ (Z/3)[x] are stored in different xmm lanes. Given two ymm inputs (a, c) and (b, d), the multiplier outputs (ab l , cd l ) and (ab h , cd h ) in two ymm registers, where a · b = ab l + ab h · x 16 and c · d = cd l + cd h · x 16 . Thus, we avoid the data exchange between xmm lanes.

Polynomial multiplication in (Z/q)[x]
Problem description and related multiplication. While applying NTT-based multiplication, NTRU Prime faces two issues. First, recalling Section 2.1, NTRU Prime works on ; hence, there is no way to apply FFT tricks on the ring. The standard workaround is to lift ring elements in R /q to (Z/q)[x], and multiply the lifted polynomials with an NTT-based multiplication in Since two input polynomials have degree less than p, their product will not overflow the degree N. After the polynomial multiplication, the product is reduced with a division by x p − x − 1 for the result in R /q. Secondly, q from the NTRU Prime parameter set is not a radix-2 NTT friendly prime. For example, q = 4591 in sntrup761, and since 4591 − 1 = 2 · 3 3 · 5 · 17, no simple root of unity is available for recursive radix-2 FFT tricks. Alkim, Cheng, Chung, Evkan, Huang, Hwang, Li, Niederhagen, Shih, Wälde, and Yang [3] presented a non-radix-2 NTT implementation on (Z/4591)[x]/(x 1530 − 1) for embedded systems. They performed radix-3, radix-5, and radix-17 NTT stages in their NTT. We instead use a radix-2 algorithm that efficiently utilizes the full ymm registers in the Haswell architecture.
The fastest Haswell sntrup software before our work dealt with the radix-2-NTT-unfriendly q by lifting the coefficients to Z and then multiplying in and (Z/12289)[x], but this means 50% more NTTs, plus extra reductions since 12289 is larger than 10753. We take a different approach described below.
Our polynomial multiplication. In this section, we present a multiplication for polynomials in (Z/q)[x] with degree less than 1024. We first map polynomials to (Z/q)[x]/(x 2048 − 1). Rather than switching from q to an NTT-friendly prime, we use Schönhage's trick (Section 2.3) to manufacture roots of unity for radix-2 NTTs. Specifically, define K as the ring (Z/q)[x]/(x 64 + 1). We map (Z/q) We use NTTs to multiply in K[y]/(y 64 − 1), using x as a primitive 128-th root of unity in K. NTT-based multiplication applies two NTTs for the input polynomials, performs component-wise multiplication for the transformed values, and applies one inverse NTT for the final product. Each NTT converts one input element in K[y]/(y 64 − 1) into 64 elements in K, using additions, subtractions, and multiplications by powers of x. Multiplication by a power of x simply raises the degree of the polynomial in (Z/q)[x], and then replaces x 64+i by −x i , using negations without any multiplications in Z/q.
After transforming the input polynomials into a list of elements in K, we perform the component-wise multiplication for the transformed vectors. The problem now is to multiply two elements of K = (Z/q)[x]/(x 64 + 1).
We use Nussbaumer's trick (Section 2. We use Montgomery multiplication [25] to calculate modular products in Z/q. For sntrup761 and sntrup653, the input polynomials have degree less than 768, so we truncate some computations in the NTT algorithm: we apply NTT on the ring K[y]/((y 32 + 1)(y 16 − 1)) instead of the original K[y]/(y 64 − 1). We map the input polynomials to degree-24 polynomials in K[y], and calculate the product with a truncated inverse NTT of 48 values. Our NTT sizes are within 18%, 1%, and 20% of optimal for 653, 761, and 857 respectively; further truncation is possible at the expense of some complication in the calculations.
AVX2 optimization for the R /q multiplier. Since the component-wise multiplication step comprises 48 or 64 multiplications on K, we perform the multiplications simultaneously in different 16-bit lanes of ymm registers. Our software stores the first Z/q coefficient of 16 elements in K in a ymm register, stores their second coefficients in a second register, and so on. In this way, we avoid data movement between the 16-bit lanes inside a ymm register.
To apply this optimization, we first rearrange the coefficients of a polynomial to different registers with a 16 × 16 matrix transposition. Given sixteen degree-15 polynomials (a 0 ), · · · , (a (15) 15 , · · · , a 15 ). Thus, we can fetch a specific coefficient by accessing its corresponding ymm register, while parallelizing 16 polynomial multiplications for the transposed data.
We use the method in [37] for matrix transposition. The technique transposes a 2 × 2 matrix by swapping its two offdiagonal components. For transposing matrices with larger dimensions, e.g. 4 × 4, it first swaps data between two 2 × 2 off-diagonal sub-matrices, and then performs matrix transpose for all its four sub-matrices.

Microbenchmarks: arithmetic
We benchmark our implementation on an Intel Xeon E3-1275 v3 (Haswell), running at 3.5 GHz, with Turbo Boost disabled. The numbers reported in this section are medians of 3 to 63 measurements, depending on the latency of the operation under measurement. We omit benchmarks here for sntrup653 because it actually uses the same multiplier as sntrup761.
Benchmarks for R /3. We compare cycle counts for R /3 multiplication between our implementation and the best previous sntrup implementation, round2 in [10], in the following We can see the cost of three multiplications and one invertibility check is less than half of a single inversion in R /3. It is clear that batch inversion costs less than pure ring inversion, even for the smallest possible batch size of two.
Benchmarks for R /q. The following table shows the cycle counts of big×big multiplication and big×small multiplication in R /q, comparing with the previous best software [10]. The results show the absolute cycle count of big×big is larger than big×small multiplication. To evaluate the efficiency of big×big multiplication, consider if we extend the big×small multiplication to big×big multiplication, by applying more internal NTT multiplications. It will result in multiplications of roughly 3/2 times the current cycle counts, i.e., slower than big×big multiplication presented in this work. Since big×small multiplication is faster than big×big, we use the former as much as possible in batchInv for R /q. Recall that Montgomery's trick for batch inversion replaces one inversion in R /q by roughly three ring multiplications and one amortized ring inversion. From the batchInv algorithm in Section 2.2, we can see the three ring multiplications are a i · b i−1 , a i · t i , and t i · b i−1 . Since the input a i is a small element, it turns out that only the last is big×big multiplication.
Since the costs for inverting one element in R /q are 576989, 785909, and 973318 cycles for sntrup653, sntrup761, and sntrup857, respectively, the cost of two big×small and one big×big multiplication is clearly much less than one inversion operation.
Benchmarks for batch key generation. We show the benchmark results for batch key generation (BatchKeyGen) in Figure 2. See also Table 3. The figure shows how increasing n, the key generation batch size, amortizes the ring inversion cost. Generating a few dozen keys at once already produces most of the throughput benefit: for example, generating n = 32 keys takes a total of 1.4 milliseconds for sntrup761 at 3.5GHz. Generating n = 128 keys takes a total of 5.2 milliseconds for sntrup761 at 3.5GHz, about 10% better throughput than n = 32.
We adopt BatchKeyGen with batch size n = 32 in our library, resulting in 156317 Haswell cycles per key.

New TLS software layering
At the application level, the goals of our end-to-end experiment are to demonstrate how the new results can be deployed in real-world conditions, transparently for the end users, and meet the performance constraints of ubiquitous systems. For this reason, we developed patches for OpenSSL 1.1.1 to support post-quantum key exchange for TLS 1.3 connections. We designed our patches so that any existing application built on top of OpenSSL 1.1.1 can transparently benefit from the PQC enhancements with no changes, as the patched version of OpenSSL retains API/ABI compatibility with the original version and acts as a drop-in replacement. This works for any application dynamically linking against libssl as the backend to establish TLS 1.3 connections. Among them, for our demonstration, we picked a web browser 1 , a custom application (tls_timer, described later), and a TLS proxy. 2 After installing our patched version of OpenSSL, users can establish secure and fast post-quantum TLS connections. Appendix E provides relevant technical background regarding the OpenSSL software architecture. The rest of this section describes, with more detail, our work to achieve the goals of our experiment, and provides rationale for the most relevant design decisions. Figure 3 depicts a high-level overview of our end-to-end experiment, highlighting the boundary between the unmodified software ecosystem and our novel contributions. This section details, in particular, our OpenSSL patches and our new ENGINE component.  provide the new optimized implementations of sntrup761 and sntrup857 operations (Section 3), through a simple standardized API that is independent from OpenSSL, and reusable by other cryptographic software components. Figure 4 depicts an architecture diagram of our end-to-end experiment, highlighting with red boxes inside the libcrypto and libssl modules, the patched OpenSSL components.

OpenSSL patches
libssl changes. Within libssl, conceptually three elements need to be changed:  combination of public-key encryption (PKE) and noninteractive key exchange (NIKE). 3 We combine the use of EVP_PKEY_encrypt() with NULL input, followed by EVP_PKEY_derive() to mimic Encapsulate(), and EVP_PKEY_decrypt() with NULL output, followed by EVP_PKEY_derive() for Decapsulate(). Due to the structure of the PKE+NIKE workaround, on both sides of the handshake, handling the key_share extension for KEM groups finishes with the call for EVP_PKEY_derive(), before updating the protocol key schedule. This is also the case in the original code that supports traditional NIKE. Therefore, the new code only affects the handling of the opaque key_share content transmitted over the wire.
As depicted in Figure 1, on the server side, traditional NIKE groups generate an ephemeral key pair, sending the encoded public key as the payload of the extension. With our patch, if the group is flagged as a KEM group, instead of key generation we execute EVP_PKEY_encrypt() under the client's public key with NULL input. We then send the resulting ciphertext as the payload of the key_share extension. As a side effect, per our PKE+NIKE workaround, EVP_PKEY_encrypt() also stores the shared secret plaintext within the internal state of the server-side object representing the client's public key. This plaintext is what is ultimately retrieved upon calling EVP_PKEY_derive().
On the client side, for traditional NIKE groups, the payload of the key_share extensions is parsed as the encoding of the peer's public key, to be used in the subsequent EVP_PKEY_derive(). With our patch, if the group is flagged as a KEM group, instead we treat the key_share payload as the ciphertext to be used in EVP_PKEY_decrypt() under the client's secret key, and with NULL output. The resulting plaintext is stored in the internal state of the client-side object representing the client's key pair. The plaintext shared secret is ultimately retrieved via EVP_PKEY_derive().
The last patch alters the libssl static table of supported TLS 1.3 groups. It assigns private NamedGroup codepoints to negotiate sntrup761 or sntrup857 key exchanges, flagged as KEM groups, and links it to static numeric identifiers (NIDs) defined within libcrypto headers. These identify implementations of sntrup761 and sntrup857, as described in the next paragraph.
libcrypto changes. libcrypto 1.1.1 has the ability to generate NIDs dynamically for custom algorithms unknown at OpenSSL build time. In contrast, libssl 1.1.1 defines supported groups in a static table generated during compilation. It is technically possible to inject KEM functionality (using the PKE+NIKE workaround described above) via a custom ENGINE without any change to libcrypto. Yet, the limited support for dynamic customization in libssl adds the requirement for a libcrypto patch to issue static NIDs for sntrup761 and sntrup857. This is so they can be included in the libssl static table at compile time. For each parameter set, this patch uses the internal OpenSSL tooling to issue a novel static NID and associate it with the corresponding sntrup* algorithm and a custom object identifier (OID), 4 required for serializing and deserializing key objects. With this data, the tooling updates the public libcrypto headers, adding the relevant sntrup* definitions.
Additionally, we include an optional patch for libcrypto that adds a reference implementation of sntrup761 as a new libcrypto submodule. Including this patch allows us to test the implementation provided by engNTRU against the reference implementation, and also to test the software stack on the server and the client in absence of the ENGINE. This eases the debug process during the development of engNTRU. For the final users of our end-to-end scenario, this patch is entirely optional, as the dynamic ENGINE injects the optimized implementation for the cryptographic primitive if it is absent. Otherwise, it overrides the default reference implementation if it is already included in libcrypto.

The engNTRU ENGINE
As mentioned in Section 2.5.2 and depicted in Figure 3 and Figure 4, as part of our end-to-end experiment, we introduce a new ENGINE, dubbed engNTRU.
We followed the methodology suggested in [36], and we defer to it for a detailed description of the ENGINE framework, how it integrates with the OpenSSL architecture (partially illustrated in Figure 4), security considerations, and general motivations to use the ENGINE framework for applied research. In this section, we highlight how this choice has two main benefits: it decouples OpenSSL from fast-paced development in the ecosystem of optimized implementations for post-quantum primitives, and at the same time it decouples external libraries implementing novel primitives from the data types and patterns required for OpenSSL compatibility.
engNTRU builds upon libbecc [15], which is itself derived from libsuola [36]. Similar to both previous works, engNTRU is also a shallow ENGINE, i.e., it does not contain actual cryptographic implementations for the supported primitives. Instead, it delegates actual computations to libsntrup761 and libsntrup857. The functionality provided by engNTRU includes: • building as a dynamically loadable module, injecting support for novel cryptographic primitives transparently for existing applications; • supporting generic KEM primitives under the PKE+NIKE workaround; • dynamically injecting/replacing support for sntrup761 at run-time, delegating to libsntrup761 for optimized computation; • dynamically injecting support for sntrup857 at runtime, delegating to libsntrup857 for optimized computation; • mapping the PKE+NIKE workaround back to the standard KEM API adopted by the implementations of NIST PQC KEM candidates, including libsntrup*.
Furthermore, similar to libbecc and libsuola, and using the same terminology, engNTRU supports the notion of multiple providers to interface with the OpenSSL API. Under the serial_lib provider, each Keygen() operation is mapped to crypto_kem_keypair(), generating a new key pair on demand as defined by the NIST PQC KEM API. Alternatively, under the batch_lib provider (which is the default in our experiment), engNTRU supports batch key generation, similar to libbecc. In the case of libsntrup761 and libsntrup857, this allows OpenSSL and applications to transparently take advantage of the performance gains described in Section 3. Under the batch_lib model, while a process is running, each sntrup* parameter set is associated with a thread-safe heap-allocated pool of key pairs. Every time an application thread requests a new sntrup* key pair, engNTRU attempts to retrieve a fresh one from the corresponding pool. For each supported parameter set, it dynamically allocates a pool, initialized the first time a key pair is requested. This includes filling the pool, by calling crypto_kem_sntrup761_keypair_batch() or crypto_kem_sntrup857_keypair_batch(). Otherwise, after the first request, engNTRU serves each request by copying (and then securely erasing from the pool buffer) the next fresh entry in the pool. After this, if consuming the key pair emptied the pool, engNTRU fills it again, by calling the corresponding libsntrup* batch generation function. This happens synchronously, before returning control to the application. Storing keys for deferred use adds security concerns: engNTRU addresses them relying on standard OS guarantees for the protection of memory contents across processes and users. On the other hand, the batch strategy decouples the generation of a key pair from its use in the application (e.g., an attacker's connection request), which complicates many implementation attacks, and results in an overall positive security impact.
In terms of performance, it is easy to see the advantage of batch_lib over serial_lib from our microbenchmarks in Section 3. With serial_lib, each sntrup761 key costs 0.4ms on a 2GHz Haswell core. With batch_lib, within each batch of 32 sntrup761 keys, the first key costs 2.5ms, and the remaining 31 keys each cost 0ms. Note that, according to video-game designers [16], latencies below 20ms are imperceptible. A series of K sntrup761 keys costs 0.4Kms from serial_lib and just (0.08K + 2.5)ms from batch_lib. Similar comments apply to the separate sntrup857 pool.
As long as API/ABI compatibility is maintained in the engNTRU/libsntrup* interfaces, further refinements in the libsntrup* implementations do not require recompiling and reinstalling engNTRU, nor OpenSSL, nor other components of the software ecosystem above. At the same time, libsntrup761 and libsntrup857 are isolated from OpenSSL-specific APIs, so they can easily be reused by alternative stacks supporting the NIST PQC KEM API. Moreover, they can retain a lean and portable API, while details like the handling of pools of batch results, or the sharing model to adopt, are delegated to the middleware layer.

Reaching applications transparently
Consulting Figure 4, the purpose of this section is to describe the extent of the application layer we explored in our study.
In these experiments, we investigated two paths to reach libssl and libcrypto (and subsequently engNTRU then libsntrup*). Namely, a networking application dynamically linking directly, and a separate shared library against which even higher level applications dynamically link against. More generally, this approach works for any application which supports TLS 1.3 by dynamically linking against libssl 1.1.1, but not for statically linked applications. 5 stunnel. For networking applications that do not natively support TLS, stunnel is an application that provides TLS tunneling. The two most common deployment scenarios for stunnel are client mode and server mode.
In client mode, stunnel listens for cleartext TCP connections, then initiates a TLS session to a fixed server address. A common use case for client mode would be connecting to a fixed TLS service from a client application that does not support TLS. For example, a user could execute the telnet application (with no TLS support) to connect to a client mode instance of stunnel, which would then TLS-wrap the connection to a static SMTPS server to securely transfer email.
In server mode, stunnel listens for TLS connections, then initiates cleartext TCP connections to a fixed server address. A common use case for server mode would be providing a TLS service from a server application that does not support TLS. For example, a user could serve a single static web page over HTTP with the netcat utility, which stunnel would then TLS-wrap to serve the content via HTTPS to incoming connections from e.g. browsers. In this light, stunnel server mode is one form of TLS termination.
stunnel links directly to OpenSSL for TLS functionality, hence the intersection with engNTRU and underlying libsntrup* is immediate. For example, in stunnel server mode, this requires no changes to the server application, which in fact is oblivious to the TLS tunneling altogether. glib-networking. Similar to how the Standard Template Library (STL) and Boost provide expanded functionality for C++ (e.g. data structures, multithreading), Glib is a core C library for GNOME and GTK applications. Bundled as part of Glib, one feature of the Gnome Input/Output (GIO) C library provides an API for networking functionality, including low-level BSD-style sockets. For TLS connections, GIO loads the glib-networking C library, which abstracts away the backend TLS provider, and presents a unified interface to callers. Currently, glib-networking supports two such backends: GnuTLS and OpenSSL. The latter is newer, mainlined in v2.59.90 (Feb 2019) while the current version as of this writing is v2.68.1. This is precisely the place where glib-networking intersects OpenSSL. To summarize, the modularity of glib-networking regarding TLS backends, coupled with the layered approach of GIO, allows any application utilizing glib-networking for TLS functionality to transparently benefit from ENGINE features, including engNTRU. One such application, and one highlight of our experiment, is GNOME Web. Neither Google Chrome nor Mozilla Firefox are capable of this level of modularity. Both browsers link directly to TLS backends at build time (BoringSSL, NSS). These do not support dynamically injecting this level of cryptosystem functionality, necessarily extending to the TLS layer as well. In general, all other popular browser implementations (we are aware of) require source-code changes to add any new TLS cipher suite. In our experiments, we are able to make GNOME Web sntrup761-and sntrup857aware with absolutely no changes to its source code, nor that of glib-networking. Performance-wise, GNOME Web then transparently benefits from the batch key generation in libsntrup* through engNTRU, loaded dynamically by the OpenSSL TLS backend of glib-networking.

Macrobenchmarks: TLS handshakes
To conclude our end-to-end experiment, we investigated the impact of enabling post-quantum key exchanges for TLS 1.3 handshakes, as perceived by end users. We considered an experiment on large-scale deployments like CECPQ1 or CECPQ2 out of scope for this work, as it would be better served by a dedicated study. As an alternative, we decided to evaluate the performance on a smaller and more controlled environment: namely, a client and a server connected over a low-traffic Gigabit Ethernet network. We chose to focus on number of connections per second as the more relevant metric from the point of view of end users, and used easily accessible consumer hardware as the platform, to simulate a small office setup. 6 To exercise full control over the sampling process, we developed a small (about 300 LOC) TLS client built directly on top of libssl (see Appendix D for a discussion about in-browser benchmarks). Referring to the diagram in Figure 3, the end-to-end benchmark replaces epiphany with this new program, that we dubbed tls_timer. In its main loop, tls_timer records a timestamp, sequentially performs a predetermined number of TLS connections, then records a second timestamp, returning the elapsed wall-clock time. In the above loop, for each connection, it performs a full TLS 1.3 handshake. Then, the client properly shuts down the connection, without sending any application data. Hence, the total elapsed time measured by the client covers the computation time required by client and server to generate and parse the content of the exchanged messages. It also includes the time spent due to transit of packets over the network, and through userland/kernelspace transitions. In particular, with respect to cryptographic computations, during the benchmark the client repeatedly performs Keygen() and Decapsulate() for the ephemeral key exchange, and RSA-2048 signature verifications to validate the identity of the server against its certificate. During the client-measured interval, the server respectively performs Encapsulate() for the ephemeral key exchange, and RSA-2048 signature generation for authentication.
As a baseline for comparisons, we used tls_timer to analogously measure the performance of TLS handshakes using the most popular TLS 1.3 groups for key exchange: namely, X25519 and P-256, in their respective ASM-optimized implementations. These are the fastest software implementations of TLS 1.3 key-exchange groups shipped in OpenSSL 1.1.1k, and are widely deployed in production. For these groups, computation on the client and server differs from the description above exclusively on the ephemeral key exchange, as both sides perform their respective NIKE Keygen() and Derive() operations instead of the listed post-quantum KEM operations, as summarized in Figure 1.
On the server side tls_timer connects to an instance of stunnel, configured as described above. Technically stunnel is itself connected to an apache2 HTTP daemon serving static content on the same host, but as tls_server does not send any application data, the connection between stunnel and apache2 is short-lived and does not carry data. Finally, to minimize noise in the measurements, we disabled frequency scaling and Turbo Boost on both platforms, terminated most concurrent services and processes on the client and the server, and isolated one physical core exclusively to each benchmark process (i.e., tls_timer, stunnel and apache2) to avoid biases due to CPU contention. Figure 5 visualizes our experimental results as cumulative distributions for each tested group. The results show that, in our implementation, both the recommended sntrup761 parameter set and the higher security sntrup857 consistently achieve more connections per second than the optimized implementations of pre-quantum alternatives currently deployed at large.
One should not conclude that sntrup761 and sntrup857 cost less than ECC overall. The unloaded high-bandwidth network of our experimental environment masks the higher communication costs of the lattice cryptosystems, whereas reasonable estimates of communication costs (see generally Appendix B) say that every lattice system costs more than ECC. Nevertheless, our results show that, in terms of computational costs, we achieve new records when compared with the fastest implementations of TLS 1.3 key-exchange groups included in OpenSSL 1.1.1k, while providing higher pre-quantum security levels and much higher post-quantum security levels against all known attacks. This significantly reduces the total sntrup* costs, in effect assigning higher decision-making weight to size and, most importantly, security.

Conclusion
NIST's ongoing Post-Quantum Cryptography Standardization Project poses significant challenges to the cryptology, applied cryptography, and system security research communities, to name a few. These challenges span both the academic and industry arenas. Our work contributes to solving these challenges in two main directions. (1) In Section 3, we propose software optimizations for sntrup, from fast SIMD arithmetic at the lowest level to efficient amortized batch key generation at the highest level. These are an essential part of our new libsntrup761 and libsntrup857 libraries.
(2) In Section 4, we demonstrate how to realize these gains from libsntrup* by developing engNTRU, a dynamicallyloadable OpenSSL ENGINE. We transparently expose it to the application layer through a light fork of OpenSSL, augmented with sntrup support in TLS 1.3 cipher suites. Our experiments reach the Gnome Web (epiphany) browser on the client side and stunnel as a TLS terminator on the server side-both with no source-code changes. Finally, our end-toend macrobenchmarks combine (1) and (2) to achieve more TLS 1.3 handshakes per second than any software included in OpenSSL.
CECPQ1 and CECPQ2 were important proof-of-concept experiments regarding the integration of post-quantum algorithms into selected browser and TLS implementations, but those experiments suffered from poor reproducibility: the capabilities and telemetry are only available to major industry players like Google and Cloudflare, so the cryptographic primitive choice and optimization techniques were dictated by them as well. Our work demonstrates that establishing a research environment to provide reproducible results is not only feasible, but achievable with a reasonable workload distribution, using new TLS software-layering techniques to minimize complexity at the architecture and system levels.
Availability. In support of Open Science, we provide several free and open-source software (FOSS) contributions and research artifacts. 7 We released libsntrup761, libsntrup857, engNTRU, and tls_timer as FOSS. We also contributed our FOSS implementations of enc and dec to SU-PERCOP; its API does not support batch keygen at this time. Lastly, we published our OpenSSL patches and a detailed, step-by-step tutorial to reproduce our full experiment stack.

A Further improvements in NTRU Prime software
This paper emphasizes a big speedup in sntrup key generation, and new software layers integrating this speedup into TLS software. The speedup relies on changing the keygeneration API to generate many keys at once, and providing one key at a time on top of this requires maintaining state, which is enabled by the new software layers. This appendix describes other ways that we have improved the NTRU Prime software without changing the API. The software was already heavily tuned before our work, but some further streamlining turned out to be possible, for example reducing sntrup761 enc from 48892 cycles to 46914 cycles and reducing dec from 59404 cycles to 56241 cycles. More important than these quantitative speedups is the software engineering: we considerably simplified the preexisting optimized code base for NTRU Prime, especially with the new NTT compiler described below. Review of NTRU Prime options. The NTRU Prime proposal specifies various lattice dimensions. Round-1 NTRU Prime specified only dimension 761. Round-2 NTRU Prime specified dimensions 653, 761, and 857. Round-3 NTRU Prime-which appeared after our first announcement of the OpenSSLNTRU results-also specified 953, 1013, and 1277.
NTRU Prime specifies two cryptosystems: Streamlined NTRU Prime (sntrup), an example of Quotient NTRU, and NTRU LPRime (ntrulpr), an example of Product NTRU. For example, dimension 761 has both sntrup761 and ntrulpr761. The two cryptosystems are almost identical in key sizes, ciphertext sizes, and Core-SVP security. The ntrulpr cryptosystems avoid the Quotient NTRU inversions and have much faster keygen than sntrup, but they have slower enc and slower dec than sntrup. Preexisting AVX2-optimized software. The official NTRU Prime "optimized C software" uses Intel AVX2 instructions and supports dimensions 653, 761, 857. Some of the code is shared across sizes except for compile-time selection of q etc. There is less sharing of the multiplier code across sizes: dimensions 653 and 761 use mult768.c, which uses size-512 NTTs to multiply 768-coefficient polynomials; dimension 857 uses mult1024.c, which uses size-512 NTTs to multiply 1024-coefficient polynomials. An underlying ntt.c is shared for computing size-512 NTTs, and the same NTT code is used for each of the NTT-friendly primes r ∈ {7681, 10753}, but multiplication algorithms vary between mult768.c and mult1024.c: for example, mult768.c uses "Good's trick" to reduce a size-1536 NTT to 3 size-512 NTTs, taking advantage of 3 being odd, while mult1024.c uses a more complicated method to reduce a size-2048 NTT to 4 size-512 NTTs. The NTT API allows these 3 or 4 independent size-512 NTTs to be computed with one function call, reducing per-call overheads and also reducing the store-to-load-forwarding overheads in crossing NTT layers. Improvements. We first built a tool to regenerate 653, 761, and 857 in the optimized C implementation from a merged code base. We then added support for 953, 1013, and 1277, which in previous work had only reference code. This meant, among other things, building a new mult1280.c to reduce a size-2560 NTT to 5 size-512 NTTs. Good's trick is applicable here since 5 is odd, but we were faced with a new mini-optimization problem regarding the number of AVX2 instructions needed for 5-coefficient polynomial multiplications modulo r. The best solution we found uses 15 modular multiplications, 2 extra reductions, and 34 additions/subtractions.
We then built a new tool to compile concise descriptions of NTT strategies into optimized NTT software. This tool is analogous to SPIRAL [30], but handles the extra complications of NTTs compared to floating-point FFTs, notably the requirement of tracking ranges of intermediate quantities so as to avoid overflows. Note that one should not confuse automated generation of NTTs with automated generation of multipliers; it remains challenging to automate code generation for the type of multipliers that we consider in Section 3.
Armed with this tool, we searched for efficient size-512 NTT strategies to replace the previous ntt.c. We found a fully vectorizable strategy that avoids all overflows for both r = 7681 and r = 10753; uses just 6656 16-bit multiplications; uses just 6976 16-bit additions (counting subtractions as additions); stores data only every 3 NTT layers; and has only 4 layers of permutation instructions. To put this in perspective, if each of the 9 NTT layers had 256 modular multiplications, 512 additions, and zero extra modular reductions, then in total there would be 6912 16-bit multiplications and 6912 16-bit additions, since each modular multiplication costs 3 16-bit multiplications and 1 16-bit addition.

B Comparing ntruhrss
CECPQ2's ntruhrss701 keygen, like OpenSSLNTRU's sntrup761/sntrup857 keygen, is bottlenecked by inversion. Conceptually, everything this paper does for sntrup can also be done for ntruhrss, starting with converting a batch of 32 ntruhrss inversions into 1 inversion plus 93 multiplications. This appendix explains two factors making this strategy less attractive for ntruhrss compared to sntrup.
First, a reasonable estimate, based on a close look at the underlying algorithms, is that there would be only about a 2× speedup from ntruhrss keygen to batched ntruhrss keygen, much less than the speedup we achieve for sntrup.
The reason is as follows. Unbatched keygen is bottlenecked by inversion, and ntruhrss exploits one of its design decisions-a power-of-2 modulus, which sntrup avoids because of security concerns-for a specialized type of inversion algorithm, a "Hensel lift". Batched keygen is instead bottlenecked by multiplication, and benefits much less from a power-of-2 modulus. The Hensel speedup would still be measurable inside the occasional inversion, but batch size 32 compresses this speedup by a factor 32. One can see some speedup from sntrup761 to ntruhrss701 in multiplications (because of the modulus and the lower ntruhrss701 security level), but the ultimate difference in keygen speeds will be an order of magnitude smaller than the difference in keygen speeds before this paper.
Second, the network-traffic-vs.-security-level trade-off is worse for ntruhrss than for sntrup. For example, Table 1 shows that ntruhrss701 sends 3.6% more traffic than sntrup761, despite having only 89% of the security level (Core-SVP 2 136 vs. 2 153 ).
An existing cost model estimates that 1000 CPU cycles have the same cost as communicating a byte of data: e.g., a quad-core 3GHz server has the same cost as a 100Mbps Internet connection. An easy calculation from Table 1 concludes that higher-security sntrup761 would still cost 1.8% below ntruhrss701 in this model even after a 2× speedup in ntruhrss701 keygen. Making ntruhrss competitive with sntrup, accounting for the security level, would require not just this speedup but also focusing on environments where communication is more than 10× cheaper.

C Barrett reduction correctness
Recalling Section 3.1.1, Barrett reduction estimates g/ f i as h = g/ f i . Then it calculates the remainder as r = g − g · g/ f i · f i . We compute the difference between (g/ f i ) · f i and g/ f i · f i . It is the remainder r if the difference has degree less than deg( f i ).
Using the pre-computation x D g = q x · f i + r x , we have g/ f i = g · (x D g / f i ) · 1/x D g = g · (q x − r x / f i ) · 1/x D g .
We compute the difference Define h = g · q x /x D g = g/ f i and l = g · q x /x D g − h. The term (g · q x /x D g − g · q x /x D g ) · f i = l · f i in Equation 3 has degree less than deg( f i ). The other term g(r x / f i ) f i /x D g also has degree less than deg( f i ), since deg(g) < D g and deg(r x ) < deg( f i ).

D More on benchmarks
Batch key-generation microbenchmarks. Table 3 shows the performance and key pair storage of BatchKeyGen regarding various batch sizes n. In-browser handshake macrobenchmarks. Section 4.4 described how we developed tls_timer, a dedicated handshake benchmarking client, to measure the end-to-end performance of OpenSSLNTRU. The need to fully control the sampling the experiment you should ensure low network traffic and that no other processes (automated updates and other scheduled processes in particular) are executed on the machines running the experiments.
A simpler alternative, that would prove consistent with the results presented in Figure 5 in terms of sorting the groups according to the average connections per second, but not necessarily in absolute values, would be to install the server side and the client side of the demo on the same host, and then use tls_timer over the loopback interface, lowering the number of sequential connections (e.g., to 1024) to reduce the execution time of each experiment.
In any case, it is important to take care of the details listed in Section 4.4 regarding disabling frequency scaling, Turbo boost, concurrent services, and scheduled processes, isolating physical cores exclusively to each of the 3 processes (i.e., tls_timer, stunnel, and apache2) involved in each experiment run, and disabling/reducing logging to console or files, in order to minimize external causes of noise and achieve consistent results.