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Task Class Partitioning for Mobile Computation Offloading
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AI
DOI：10.1109/JIOT.2023.3294887.png)
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摘要
En 中文
This article introduces algorithms for static task class partitioning in mobile computation offloading (MCO). The objective is to partition a given set of task classes into two sets that are either executed locally by the mobile device (MD) or those classes that are permitted to contend for remote edge server (ES) execution. The goal is to find the task class partition that gives the minimum mean MD power consumption subject to task completion deadlines. This article generates these partitions for both soft and hard task completion deadlines. Two variations of the problem are considered. The first assumes that the wireless and computational capacities are given and the second generates both capacity assignments subject to an additional resource cost budget constraint. The proposed partitioning algorithms are based on heuristic class ordering methods. This article introduces two class ordering methods, a simpler one based on a task latency criterion, and an hierarchical version that first sorts and groups classes based on a mean power consumption criterion and then orders the task classes within each group based on a task completion time criterion. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solutions for both given and optimized network resource assignments.
Keyword:
Cost budget constraints
edge computing
mobile computation offloading (MCO)
power efficiency
task completion deadlines
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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