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Dependent tasks offloading based on particle swarm optimization algorithm in multi-access edge computing
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DOI：10.1016/j.asoc.2021.107790.png)
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The proliferation of emerging applications such as augmented reality, face recognition, and autonomous driving have stimulated the growth of demand for low-latency services, while the traditional cloud computing paradigm inevitably increases end-to-end latency. Multi-access edge computing deploys computing and storage resources to user terminals, which is expected to become an effective solution. Most of the existing researches on multi-access edge computing focus on the offloading of independent tasks, which cannot meet the challenge of a real scenario in which a task is composed of multiple interdependent subtasks. To bridge the gap, we formulate the problem of offloading multi-dependent tasks in multi-access edge computing considering both task completion time and execution cost. Since this offloading problem is NP-hard, a queue-based improved multi-objective particle swarm optimization is proposed. During the optimization process, we introduce the Pareto optimal relationship and define a transition probability to obtain the optimal solution. A large number of simulation results show that compared with other alternatives, the performance of our algorithm can be improved by about 3%-25%. (C) 2021 Published by Elsevier B.V.
Keyword:
Multi-access edge computing
Task offloading
Pareto optimal relationship
Dependency
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6.6
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1.4W
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4.8W
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