Research on the improvement of EdgeCloudSim's task scheduling framework

Huaze Lin; Dan Liu; Li Li; Leilei Zhu; Xin Sui

doi:10.1117/12.2668027

20 April 2023 Research on the improvement of EdgeCloudSim's task scheduling framework

Huaze Lin, Dan Liu, Li Li, Leilei Zhu, Xin Sui

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126022F (2023) https://doi.org/10.1117/12.2668027
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

In cloud-edge collaborative working mode, application services are affected by complex components, configuration and deployment conditions and other multi-dimensional factors. Therefore, it’s important to use simulation tools to conduct experiments reasonably and effectively. EdgeCloudSim is a simulation test platform widely used in edge computing. However, EdgeCloudSim's built-in task scheduling algorithm FF(First Fit) has problem of queuing and blocking, which makes some tasks fail to obtain computing resources in time. Besides, the processing time and load are not ideal. Another algorithm LL(Least load) optimizes above problems, but VM list needs to be traversed every time while scheduling. So the decision-making efficiency is low. Furthermore, EdgeCloudSim ignores migration cost of tasks. This paper improves the task scheduling framework of EdgeCloudSim for above problems and proposes a two-stage cloud-edge task scheduling strategy based on MH-LL(Max Heap-Least Load) algorithm and PP-LL(Position Probability-Least Load) algorithm. When resources of scheduled edge cloud are insufficient, task will be migrated to other edge clouds according to the migration cost calculated by distance factor α and distance coefficient d. Comparing with the original platform by experiments, the improved platform has an average optimization of 1.38%, 2.978s, 0.129s, and 19.26% in task failure rate, service time, delay, and load. It can improve work efficiency and system performance.

Citation Download Citation

Huaze Lin, Dan Liu, Li Li, Leilei Zhu, and Xin Sui "Research on the improvement of EdgeCloudSim's task scheduling framework", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126022F (20 April 2023); https://doi.org/10.1117/12.2668027

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KEYWORDS

Clouds

Computer simulations

Data centers

Computer science

Data modeling

Cloud computing

Failure analysis

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