A Sample Average Approximation-Based Parallel Algorithm for Application Placement in Edge Computing Systems

Mobile Edge Computing (MEC) is a new paradigm that aims at decreasing the response time of running mobile applications by offloading the component of the applications on the servers located at the edge of the network instead of on the cloud servers. In this paper, we address a very important problem in the management of MEC systems, that is, the problem of finding an efficient application placement on the edge servers such that the cost of execution is minimized. We develop a multi-stage stochastic programming model for the application placement problem in edge computing systems and design a novel parallel greedy algorithm based on the Sample Average Approximation method to solve it. We evaluate the performance of the proposed algorithm by conducting extensive experimental analysis using data extracted from a real-world dataset. The experimental results show that the proposed algorithm can solve the problem efficiently.