Abstract

The extensive use of mobile intelligent devices, such as smart phones and tablets, induces new opportunity and challenge for computation offloading. Task offloading is an important issue in a system consisting of multiple types of devices, such as mobile intelligent devices, local edge hosts and a remote cloud server. In this paper, we study the offloading assignment of multiple applications, each one comprising several dependent tasks, in such a system. To evaluate the total cost in the offloading process, a new metric is introduced to take into account features of different devices. The remote server and local hosts are more concerned about their processors utilization, while mobile devices pay more attention to their energy. Therefore, this metric uses relative energy consumption to denote the cost of mobile devices, and evaluates the cost of the remote server and local hosts by the processor cycle number of task execution. We formulate the offloading problem to minimize the system cost of all applications within each application's completed time deadline. Since this problem is NP-hard, the heuristic algorithm is proposed to offload these dependent tasks. At first, our algorithm arranges all tasks from different applications in a priority queue considering both completed time deadline and task-dependency requirements. Then, based on the priority queue, all tasks are initially assigned to devices to protect mobile devices with low energy and make them survive in the assignment process as long as possible. At last, to obtain a better schedule realizing lower system cost, based on the relative remaining energy of mobile devices, we reassign tasks from high-cost devices to low-cost devices to minimize the system cost. Simulation results show that our proposed algorithm increases the successfully completed probability of whole applications and reduces the system cost effectively under time and energy constraints.

Highlights

  • In the recent few years, with the extensive use of mobile intelligent devices, such as smart phones and tablets, computationally intensive applications including image/video processing, augmented reality, face recognition and interactive gaming are becoming popular on these mobile devices

  • We study the offloading assignment of multiple applications in a system, which consists of different computing devices such as mobile intelligent devices, local edge hosts and a remote cloud server

  • The different features of all computing devices are considered in this new metric, where relative energy consumption is used to evaluate mobile devices and processor cycle number is proposed for local edge hosts and the remote cloud server

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Summary

INTRODUCTION

In the recent few years, with the extensive use of mobile intelligent devices, such as smart phones and tablets, computationally intensive applications including image/video processing, augmented reality, face recognition and interactive gaming are becoming popular on these mobile devices. We study the offloading assignment of multiple applications in a system, which consists of different computing devices such as mobile intelligent devices, local edge hosts and a remote cloud server. The different features of all computing devices are considered in this new metric, where relative energy consumption is used to evaluate mobile devices and processor cycle number is proposed for local edge hosts and the remote cloud server. Based on the realistic model and the new metric, the offloading problem is formulated to minimize the system cost of all applications while satisfying each application’s completed deadline Since this problem is NP-hard, the heuristic algorithm is proposed to offload these dependent tasks. A new metric, considering relative energy consumption of mobile devices and processor cycle number of local edge hosts and the remote server, is proposed to evaluate the offloading effect.

RELATED WORKS
SYSTEM MODEL
OFFLOADING ALGORITHM
SIMULATIONS
Findings
CONCLUSION
Full Text
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