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E-AVOA-TS: Enhanced African vultures optimization algorithm-based task scheduling strategy for fog-cloud computing
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DOI：10.1016/j.suscom.2023.100918.png)
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摘要
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In fog computing, inefficient scheduling of user tasks causes more delays. Moreover, how to schedule tasks that need to be offloaded to fog nodes or cloud nodes has not been fully addressed. The task scheduling process needs to be optimized and efficient in order to address the issues of resource utilization, response time, and energy consumption. This paper proposes an Enhanced African Vultures Optimization Algorithm-based Task Scheduling Strategy (E-AVOA-TS) for fog-cloud computing. Through the proposed strategy, each village learns from its neighbors rather than from all of its members. The minimization of makespan, cost, and energy consumption in the proposed algorithm are considered as objective function. To prioritize tasks, the Best Worst Method (BWM) is used to handle the sensitivity of task delays. Latency-sensitive tasks are sent to the fog environment, while latency-tolerant tasks are sent to the cloud. E-AVOA is compared to other state-of-the-art optimizers using classic benchmark functions and ten benchmark tests from CEC-C06. Compared to other competitors, E-AVOA-TS outperforms makespan by 24.2%, cost by 16%, energy consumption by 4.7%, and DST% by 6.2% for large scale tasks. According to the simulation results, makespan shows improvements of 33%, 53%, and 48%, and energy consumption is reduced by 32%, 44%, and 5%, compared with PSG-M, IWC, and DCOHHOTS, respectively.
Keyword:
Fog -cloud computing
Task scheduling
Best Worst Method (BWM)
African Vultures Optimization Algorithm
(AVOA)
Dimension Learning -based Hunting (DLH)
期刊
S
IF:
5.7
论文数:
966
被引数:
2.9K
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