Energy-Efficient Task Allocation of Heterogeneous Resources in Mobile Edge Computing

The edge cloud provides heterogeneous resources, such as cores, memory, and storage which are then allocated to mobile applications in mobile edge computing, which require multiple types of resources to execute. While current work on mobile edge computing focuses on tasks needing a single resource t...

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Vydané v:IEEE access Ročník 9; s. 1
Hlavní autori: Liu, Xi, Liu, Jun, Wu, Hong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Applications programs
Approximation
Approximation algorithms
Cloud computing
Computation offloading
Edge computing
Energy consumption
energy efficient
Heuristic methods
Integer programming
Mathematical analysis
Mobile computing
Mobile edge computing
Mobile handsets
polynomial time approximation scheme
Polynomials
Resource allocation
Resource management
Servers
Task analysis
ISSN:2169-3536, 2169-3536
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Shrnutí:The edge cloud provides heterogeneous resources, such as cores, memory, and storage which are then allocated to mobile applications in mobile edge computing, which require multiple types of resources to execute. While current work on mobile edge computing focuses on tasks needing a single resource to run, we investigate the task allocation problem aiming to minimize total energy consumption while considering heterogeneous resource settings. Specifically, we consider the binary computation offloading mode, in which a task executes successfully as a whole. We formulate this as an integer programming problem, and transform that to the two subproblems of the resource allocation and the offloading decision. We propose a heuristic approach to solve the resource allocation subproblem and an approximation algorithm for the offloading decision subproblem.We show that our proposed approximation algorithm is a polynomial time approximation scheme, and hence it achieves a tradeoff between optimality loss and time complexity. To the best of our knowledge, this is the first study of task allocation considering both multiple types of resources and binary computation offloading mode. Experimental results demonstrate that the performance of our proposed algorithm scales very well for multi-resource allocation in different conditions, and it achieves a good balance of performance and speed.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3108342