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A Multiobjective Computation Offloading Algorithm for Mobile-Edge Computing
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DOI：10.1109/JIOT.2020.2996762.png)
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Abstract
En 中文
In mobile-edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption of SMDs. Nevertheless, offloading tasks to the edge incurs additional transmission time and thus higher execution delay. This article studies the tradeoff between the completion time of applications and the energy consumption of SMDs in MEC networks. The problem is formulated as a multiobjective computation offloading problem (MCOP), where the task precedence, i.e., ordering of tasks in SMD applications, is introduced as a new constraint in the MCOP. An improved multiobjective evolutionary algorithm based on decomposition (MOEA/D) with two performance enhancing schemes is proposed: 1) the problem-specific population initialization scheme uses a latency-based execution location (EL) initialization method to initialize the EL (i.e., either local SMD or MEC server) for each task and 2) the dynamic voltage and frequency scaling-based energy conservation scheme helps to decrease the energy consumption without increasing the completion time of applications. The simulation results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art heuristics and metaheuristics in terms of the convergence and diversity of the obtained nondominated solutions.
Keywords:
Task analysis
Energy consumption
Heuristic algorithms
Servers
Delays
Cloud computing
Optimization
Computation offloading
dynamic voltage and frequency scaling (DVFS)
mobile-edge computing (MEC)
multiobjective evolutionary algorithm (MOEA)
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W
Organization
Cited Papers
Joint Optimization of Energy Consumption and Latency in Mobile Edge Computing for Internet of Things
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