Efficient Resource Allocation for Relay Assisted Computation Offloading in Mobile Edge Computing
In this article, relay assisted computation offloading (RACO) is investigated, where user <inline formula> <tex math notation="LaTeX"$A$ /tex math>/inline formula> wishes to share the results of computational tasks with another user <inline formula> <tex math notation="LaTeX"$B$ /tex math>/inline formula> with the assistance of a mobile edge relay server (MERS) To enable this computation offloading, we propose a hybrid relaying (HR) approach employing a pair of orthogonal frequency bands, which are, respectively, used for the amplify forward relaying of computational results and the decode forward relaying of the unprocessed raw tasks. The motivation here is to adapt the allocation of computing and communication resources both to dynamic user requirements and to diverse computational tasks. Using this framework, we seek to minimize the weighted sum of the execution delays and the energy consumption in the RACO system by jointly optimizing the computation offloading ratio, the bandwidth allocation, the processor speeds, as well as the transmit power levels of both user <inline formula> <tex math notation="LaTeX"$A$ /tex math>/inline formula> and the MERS, under some practical constraints. By adopting a series of transformations, we first recast this problem into a form amenable to optimization and then develop an efficient iterative algorithm for its solution based on the concave convex procedure (CCCP) By virtue of the particular problem structure in our case, we propose furthermore a simplified algorithm based on the inexact block coordinate descent (IBCD) method, which leads us to much lower computational complexity. Finally, our numerical results demonstrate the advantages of the proposed algorithms over the state of the art benchmark schemes.
IEEE Internet of Things Journal