Computation Offloading with Instantaneous Load Billing for Mobile Edge Computing

Abstract : Mobile edge computing (MEC) is a promising approach that can reduce the latency of task processing by offloading tasks from user equipments (UEs) to MEC servers. Existing works always assume that the MEC server is capable of executing the offloaded tasks, without considering the impact of improper load on task processing efficiency. In this paper, we present a two-stage computing offloading scheme to minimize the task processing delay while managing the server load properly. To minimize the task processing delay, each UE optimizes how much workload to be offloaded to the MEC server. To improve the task processing efficiency of the server, we arrange the processing order of offloading tasks by introducing an aggregative game with an instantaneous load billing mechanism. The proposed game can obtain the optimal task offloading and processing strategy with limited information and a small number of iterations. Simulation results show that our scheme approaches the optimal offloading strategy in terms of minimizing task processing delay for each UE and improving processing efficiency for the server.

 EXISTING SYSTEM :

 Mobile phone applications have been rapidly growing and emerging with the Internet of Things (IoT) applications in augmented reality, virtual reality, and ultra-clear video due to the development of mobile Internet services in the last three decades. These applications demand intensive computing to support data analysis, real-time video processing, and decision making for optimizing the user experience. Mobile smart devices play a significant role in our daily life, and such an upward trend is continuous. Nevertheless, these devices suffer from limited resources such as CPU, memory, and energy. Computation offloading is a promising technique that can promote the lifetime and performance of smart devices by offloading local computation tasks to edge servers. In light of this situation, the strategy of computation offloading has been adopted to solve this problem.

 DISADVANTAGE :

 ? The studies in focused on the effects of channel reuse and channel competition on offloading decisions. ? The effect of server resources used in computation offloading performance was not considered.

 PROPOSED SYSTEM :

 We propose a computation offloading strategy under a scenario of multi-user and multi-mobile edge servers that considers the performance of intelligent devices and server resources. The strategy contains three main stages. In the offloading decision making stage, the basis of offloading decision-making is put forward by considering the factors of computing task size, computing requirement, computing capacity of server, and network bandwidth. In the server selection stage, the candidate servers are evaluated comprehensively by multi objective decision-making, and the appropriate servers are selected for the computation offloading. In the task scheduling stage, a task scheduling model based on the improved auction algorithm has been proposed by considering the time requirement of the computing tasks and the computing performance of the mobile edge computing server. Extensive simulations have demonstrated that the proposed computation offloading strategy could effectively reduce service delay and the energy consumption of intelligent devices, and improve user experience.

 ADVANTAGE :

 The proposed offloading strategy can effectively reduce service delay, reduce the energy consumption of smart devices, and improve the user experience. This policy also applies a priority queue in terms of delay requirements of applications.