Revisiting Computation Partitioning in Future 5G-Based Edge Computing Environments

Abstract

Edge computing recently attracts the industry and academic attentions due to its advantage of providing low latency services in a much closer place to the end users. This paper studies the problem of computation partitioning in future 5G-based edge computing environments. Although the problem has been studied a lot in (mobile) cloud computing, the problem in this paper is different with previous works. Traditional partitioning approaches in cloud computing aim to achieve an optimal tradeoff between the network transmission cost and the local computation cost, because the data transmission to cloud is very costly. However, in future 5G-based edge computing, the high bandwidth and low latency will overcome the data transmission challenge. Instead the constrained computation capability of the edge will greatly affect the performance of an partitioned execution of the application. As the challenge changes, we propose a new partitioning model, which parallelizes the computations and fully utilizes the computational resources on the edge and end devices. We develop an off-line solution for partitioning and scheduling the computation to the resources. We prove in theory that our off-line solution achieves the optimal performance. Based on the off-line solution, we further develop a set of online algorithms, and conduct extensive simulations to show that our proposed online algorithms significantly outperform the benchmark algorithms.