I. INTRODUCTION

II. RELATED WORK

III. SYSTEM MODEL

IV. ALGORUTHM DESIGN

V. EVALUATE THE PERFORMANCE

VI. CONCLUSION AND FUTURE WORK

REFERENCES

Mobile terminal users applications, such as smartphones or laptops, have frequent computational task demanding but limited battery power. Edge computing is introduced to offload terminals’ tasks to meet the quality of service requirements such as low delay and energy consumption. By offloading computation tasks, edge servers can enable terminals to collaboratively run the highly demanding applications in acceptable delay requirements. However, existing schemes barely consider the characteristics of the edge server, which leads to random assignment of tasks among servers and big tasks with high computational intensity (named as ‘‘big task’’) may be assigned to servers with low ability. In this paper, a task is divided into several subtasks and subtasks are offloaded according to characteristics of edge servers, such as transmission distance and central processing unit (CPU) capacity. With this multi-subtasks-to-multi-servers model, an adaptive offloading scheme based on Hungarian algorithm is proposed with low complexity. Extensive simulations are conducted to show the efficiency of the scheme on reducing the offloading latency with low energy consumption.

INTRODUCTION

Mobile terminal devices are connected through the internet to accomplish many different applications and services, such as smartphones, laptops, sensors, machines, and vehicles, etc[1]. To extract valuable information from the huge amount of users’ data, local computation with terminal devices are no longer provide demanding quality of services such as low latency and energy consumption[2], [3], especially for video image stream data processing[4]–[۶]. In-vehicle networks, tasks with high latency sensitivity require lower processing time. Otherwise, message propagation among vehicles may fail [7]. Therefore, light-weighted servers are deployed on the edge around terminals to bring computation and storage resource from the centralized cloud (CC), which is called as Mobile Edge Computing (MEC) [8]. Tasks generated by terminals can be offloaded and processed on edge servers [9]–[۱۰] instead of being transferred to CC with large delay, and tasks or applications can effectively meet the delay requirements [11]–[۱۳]. As privacy and security become more important in our daily life [14]–[۱۷], a low delay would be particularly important in privacy and security issues for mobile edge computing systems.