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Task Scheduling for Mobile Edge Computing Using Genetic Algorithm and Conflict Graphs
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DOI：10.1109/TVT.2020.2995146.png)
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Abstract
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In this paper, we consider parallel and sequential task offloading to multiple mobile edge computing servers. The task consists of a set of inter-dependent sub-tasks, which are scheduled to servers to minimize both offloading latency and failure probability. Two algorithms are proposed to solve the scheduling problem, which are based on genetic algorithm and conflict graph models, respectively. Simulation results show that these algorithms provide performance close to the optimal solution, which is obtained through exhaustive search. Furthermore, although parallel offloading uses orthogonal channels, results demonstrate that the sequential offloading yields a reduced offloading failure probability when compared to the parallel offloading. On the other hand, parallel offloading provides less latency. However, as the dependency among sub-tasks increases, the latency gap between parallel and sequential schemes decreases.
Keywords:
Servers
Task analysis
Mobile handsets
Delays
Computational modeling
Processor scheduling
Energy consumption
Conflict graphs
genetic algorithms
mobile edge computing
parallel offloading
sequential offloading
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