Abstract
With the rapid development of Internet of Everything (IoE) and the popularization of 4G/5G wireless networks, edge computing has gradually become the mainstream. In actual edge environment, some factors have an uncertain impact on scientific workflow scheduling, such as CPU load and bandwidth fluctuations of servers. Aiming at scientific workflow scheduling under uncertain edge environment, based on fuzzy theory, triangular fuzzy numbers (TFNs) were used to represent task computation time and data transmission time. In addition, an Adaptive Discrete Fuzzy GA-based Particle Swarm Optimization (ADFGA-PSO) is proposed to reduce the fuzzy execution cost while satisfying the scientific workflow’s deadline. The uncertainty of workflow scheduling was introduced, which caused by server execution performance fluctuations during task computation and bandwidth fluctuations during data transmission. Meanwhile, two-dimensional discrete particle is adopted to encode the fuzzy scheduling strategy of workflow, and the two-point crossover operator, neighborhood mutation and adaptive multipoint mutation operator of genetic algorithm (GA) were introduced to improve the diversity of population and avoid the local optimum. Experimental results show that compared with other algorithms, ADFGA-PSO can obtain better fuzzy execution cost for deadline-constrained workflow scheduling under uncertain edge environment.
This work was supported by the National Key R&D Program of China (2018YFB1004800), the National Natural Science Foundation of China (62072108), the Guiding Project of Fujian Province under Grant (2018H0017), and the Natural Science Foundation of Fujian Province under Grant (2019J01286).
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Lin, C., Lin, B., Chen, X. (2021). Research on Scientific Workflow Scheduling Based on Fuzzy Theory Under Edge Environment. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_25
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