Game-Based Task Offloading of Multiple Mobile Devices with QoS in Mobile Edge Computing Systems of Limited Computation Capacity

Abstract

Mobile edge computing (MEC) is becoming a promising paradigm of providing computing servers, like cloud computing, to Edge node. Compared to cloud servers, MECs are deployed closer to mobile devices (MDs) and can provide high quality-of-service (QoS; including high bandwidth, low latency, etc) for MDs with computation-intensive and delay-sensitive tasks. Faced with many MDs with high QoS requirements, MEC with limited computation capacity should consider how to allocate the computing resources to MDs to maximize the number of served MDs. Besides, for each MD, he/she wants to minimize the energy consumption within an acceptance delay range. To solve these issues, we propose a Game-based Computation Offloading (GCO) algorithm including a task offloading profile of MEC and the transmission power controlling of each MD. Specifically, we propose a Greedy-Pruning algorithm to determine the MDs that can offload the tasks to MEC. Meanwhile, each MD competes the computing resources by using his/her transmission power-controlling strategy. We illustrate the problem of task offloading for multi-MD as a non-cooperative game model, in which the information of each player (MDs) is incomplete for others and each player wishes to maximize his/her own benefit. We prove the existence of the Nash equilibrium solution of our proposed game model. Then, it is proved that the transmission power solution sequence obtained from GCO algorithm converges to the Nash equilibrium solution. Extensive simulated experiments are shown and the comparison experiments with the state-of-the-art and benchmark solutions validate and show the feasibility of the proposed method.