  Mutual exclusion is one of the most commonly used techniques to handle
contention in concurrent systems. Traditionally, mutual exclusion algorithms
have been designed under the assumption that a process does not fail while
acquiring/releasing a lock or while executing its critical section. However,
failures do occur in real life, potentially leaving the lock in an inconsistent
state. This gives rise to the problem of recoverable mutual exclusion (RME)
that involves designing a mutual exclusion (ME) algorithm that can tolerate
failures, while maintaining safety and liveness properties.
  In this work, we present a framework that transforms any algorithm that
solves the RME problem into an algorithm that can also simultaneously adapt to
(1) the number of processes competing for the lock, as well as (2) the number
of failures that have occurred in the recent past, while maintaining the
correctness and performance properties of the underlying RME algorithm.
Additionally, the algorithm constructed as a result of this transformation adds
certain desirable properties like fairness (a variation of FCFS) and bounded
recovery. Assume that the worst-case RMR complexity of a critical section
request in the underlying RME algorithm is $R(n)$. Then, our framework yields
an RME algorithm for which the worst-case RMR complexity of a critical section
request is given by $\mathcal{O}(\min \{\ddot{c}, \sqrt{F+1}, R(n)\})$, where
$\ddot{c}$ denotes the point contention of the request and $F$ denotes the
number of failures in the recent past of the request.
  We further extend our framework by presenting a novel memory reclamation
algorithm to bound the worst-case space complexity of the RME algorithm. The
memory reclamation techniques maintain the fairness, performance and
correctness properties of our transformation and is general enough to be
employed to bound the space of other RME algorithms.

Distributed, Parallel, and Cluster Computing