JOURNAL ARTICLE

Joint optimization of task caching, computation offloading and resource allocation for mobile edge computing

Zhixiong ChenZhengchuan ChenZhi RenLiang LiangWanli WenYunjian Jia

Year: 2022 Journal:   China Communications Vol: 19 (12)Pages: 142-159   Publisher: Institute of Electrical and Electronics Engineers

Abstract

Applications with sensitive delay and sizeable data volumes, such as interactive gaming and augmented reality, have become popular in recent years. These applications pose a huge challenge for mobile users with limited resources. Computation offloading is a mainstream technique to reduce execution delay and save energy for mobile users. However, computation offloading requires communication between mobile users and mobile edge computing (MEC) servers. Such a mechanism would difficultly meet users' demand in some data-hungry and computation-intensive applications because the energy consumption and delay caused by transmissions are considerable expenses for users. Caching task data can effectively reduce the data transmissions when users offload their tasks to the MEC server. The limited caching space at the MEC server calls for judiciously decide which tasks should be cached. Motivated by this, we consider the joint optimization of computation offloading and task caching in a cellular network. In particular, it allows users to proactively cache or offload their tasks at the MEC server. The objective of this paper is to minimize the system cost, which is defined as the weighted sum of task execution delay and energy consumption for all users. Aiming at establishing optimal performance bound for the system design, we formulate an optimization problem by jointly optimizing the task caching, computation offloading, and resource allocation. The problem is a challenging mixed-integer non-linear programming problem and is NP-hard in general. To solve it efficiently, by using convex optimization, Karmarkar's algorithm and the proposed fast search algorithm, we obtain an optimal solution of the formulated problem with manageable computational complexity. Extensive simulation results show that in comparison to some representative benchmark methods, the proposed solution can effectively reduce the system cost.

Keywords:
Computer science Computation offloading Cache Mobile edge computing Server Task (project management) Distributed computing Energy consumption Optimization problem Mobile device Enhanced Data Rates for GSM Evolution Computer network Edge computing Operating system Algorithm

Metrics

6
Cited By
1.29
FWCI (Field Weighted Citation Impact)
0
Refs
0.74
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

IoT and Edge/Fog Computing
Physical Sciences →  Computer Science →  Computer Networks and Communications
Caching and Content Delivery
Physical Sciences →  Computer Science →  Computer Networks and Communications
Advanced Wireless Communication Technologies
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
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