A Fuzzy Scheduling Strategy for Deadline-Based Workflow Applications in Uncertain Edge-Cloud Environments

Workflow scheduling is critical to performing many practical workflow applications. Scheduling based on edgecloud computing can help addressing the high complexity of workflow applications, while decreasing the data transmission delay. However, due to the nature of heterogeneous resources in edge-cloud environments and the complicated data dependencies between the tasks in such a workflow, significant challenges for workflow scheduling remain, including the selection of an optimal tasks-servers solution from the possible numerous combinations. Existing studies are mainly done subject to rigorous conditions without fluctuations, ignoring the fact that workflow scheduling is typically present in uncertain environments. In this study, we focus on reducing the execution cost of workflow applications mainly caused by task computation and data transmission, while satisfying the workflow deadline in uncertain edge-cloud environments. The Triangular Fuzzy Numbers (TFNs) are adopted to represent task processing time and data transferring time. A costdriven fuzzy scheduling strategy based on an Adaptive Discrete Particle Swarm Optimization (ADPSO) algorithm is proposed, which is employed the operators of Genetic Algorithm (GA). This strategy introduces the randomly two-point crossover operator, neighborhood mutation operator, and adaptive multipoint mutation operator of GA to effectively avoid converging on local optima. The experimental results show that our strategy can effectively reduce the workflow execution cost in uncertain edgecloud environments, compared with other benchmark solutions. This work is partly supported by the Natural Science Foundation of China under Grant No. 62072108, the Natural Science Foundation of Fujian Province for Distinguished Young Scholar No. 2020J06014, the Natural Science Foundation of Fujian Province under Grant No. 2019J01286, and the Young and Middle-aged Teacher Education Foundation of Fujian Province under Grant No. JT180098.(Corresponding authors: Xing Chen and Neal N. Xiong.) Bing Lin is with the College of Physics and Energy, Fujian Normal University, Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials, Fuzhou, 350117, China. Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fuzhou, 350117, China. Fujian Provincial Collaborative Innovation Center for Optoelectronic Semiconductors and Efficient Devices, Xiamen, 361005, China. E-mail: WheelLX@163.com. Chaowei Lin and Xing Chen are with the College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350118, China, and with Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou, 350118, China. E-mail: cwlin1998@foxmail.com, chenxing@fzu.edu.cn. Neal N. Xiong is with the Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK, USA. E-mail: xiongnaixue@gmail.com. Peisong Hua is with the Faculty of Electrical and Computer Engineering in University of Alberta, Edmonton, AB T6G 2R3, Canada. E-mail: peisong@ualberta.ca. Qiang Shen is with the Department of Computer Science, Faculty of Business and Physical Sciences, Aberystwyth University, Aberystwyth SY23 3DB, UK. Email: qqs@aber.ac.uk.