Energy- And Resource-Aware Computation Offloading for Complex Tasks in Edge Environment

14Citations
Citations of this article
12Readers
Mendeley users who have this article in their library.

Abstract

Mobile users typically have a series of complex tasks consisting of time-constrained workflows and concurrent workflows that need to be processed. However, these tasks cannot be performed directly locally due to resource limitations of the mobile terminal, especially for battery life. Fortunately, mobile edge computing (MEC) has been recognized as a promising technology which brings abundant resource at the edge of mobile network enabling mobile devices to overcome the resource and capacity constraints. However, edge servers, such as cloudlets, are heterogeneous and have limited resources. Thus, it is important to make an appropriate offloading strategy to maximize the utility of each cloudlet. In view of this, the time consumption and energy consumption of mobile devices and resource utilization of cloudlets have been taken into consideration in this study. Firstly, a multiconstraint workflow mode has been established, and then a multiobjective optimization mode is formulated. Technically, an improved optimization algorithm is proposed to address this mode based on Nondominated Sorting Genetic Algorithm II. Both extensive experimental evaluations and detailed theoretical analysis are conducted to show that the proposed method is effective and efficiency.

References Powered by Scopus

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

2657Citations
N/AReaders
Get full text

Mobile edge computing, Fog et al.: A survey and analysis of security threats and challenges

1001Citations
N/AReaders
Get full text

Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network

959Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Privacy-preserving quality prediction for edge-based IoT services

66Citations
N/AReaders
Get full text

Cooperative Edge Caching: A Multi-Agent Deep Learning Based Approach

60Citations
N/AReaders
Get full text

End-edge-cloud collaborative computation offloading for multiple mobile users in heterogeneous edge-server environment

29Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Peng, K., Zhao, B., Xue, S., & Huang, Q. (2020). Energy- And Resource-Aware Computation Offloading for Complex Tasks in Edge Environment. Complexity, 2020. https://doi.org/10.1155/2020/9548262

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 5

100%

Readers' Discipline

Tooltip

Computer Science 4

57%

Engineering 2

29%

Physics and Astronomy 1

14%

Save time finding and organizing research with Mendeley

Sign up for free