Efficient multi-user for task offloading and server allocation in mobile edge computing systems

Abstract

Mobile edge computing has emerged as a new paradigm to enhance computing capabilities by offloading complicated tasks to nearby cloud server. To conserve energy as well as maintain quality of service, low time complexity algorithm is proposed to complete task offloading and server allocation. In this paper, a multi-user with multiple tasks and single server scenario is considered for small network, taking full account of factors including data size, bandwidth, channel state information. Furthermore, we consider a multi-server scenario for bigger network, where the influence of task priority is taken into consideration. To jointly minimize delay and energy cost, we propose a distributed unsupervised learning-based offloading framework for task offloading and server allocation. We exploit a memory pool to store input data and corresponding decisions as key-value pairs for model to learn to solve optimization problems. To further reduce time cost and achieve near-optimal performance, we use convolutional neural networks to process mass data based on fully connected networks. Numerical results show that the proposed algorithm performs better than other offloading schemes, which can generate near-optimal offloading decision timely.