Improved harris hawks optimization algorithm for workflow scheduling challenge in cloud–edge environment

被引：35

作者：

Zivkovic M. ^{[1
]}

Bezdan T. ^{[1
]}

Strumberger I. ^{[1
]}

Bacanin N. ^{[1
]}

Venkatachalam K. ^{[2
]}

机构：

[1] Singidunum University, Danijelova 32, Belgrade

[2] School of Computer Science and Engineering, VIT Bhopal University, Bhopal

来源：

Lecture Notes on Data Engineering and Communications Technologies | 2021年 / 66卷

关键词：

Cloud–edge computing; Harris Hawks optimization; Swarm intelligence; Workflow scheduling;

D O I：

10.1007/978-981-16-0965-7_9

中图分类号：

学科分类号：

摘要：

Edge computing is a relatively novel technology, which is closely related to the concepts of the Internet of things and cloud computing. The main purpose of edge computing is to bring the resources as close as possible to the clients, to the very edge of the cloud. By doing so, it is possible to achieve smaller response times and lower network bandwidth utilization. Workflow scheduling in such an edge–cloud environment is considered to be an NP-hard problem, which has to be solved by a stochastic approach, especially in the scenario of multiple optimization goals. In the research presented in this paper, a modified Harris hawks optimization algorithm is proposed and adjusted to target cloud–edge workflow scheduling problem. Simulations are carried out with two main objectives—cost and makespan. The proposed experiments have used real workflow models and evaluated the proposed algorithm by comparing it to the other approaches available in the recent literature which were tested in the same simulation environment and experimental conditions. Based on the results from conducted experiments, the proposed improved Harris hawks optimization algorithm outperformed other state-of-the-art approaches by reducing cost and makespan performance metrics. © 2021, Springer Science and Business Media Deutschland GmbH. All rights reserved.

引用

页码：87 / 102

页数：15

共 20 条

[1]

Shiliang L., Lianglun C., Bin R., Practical swarm optimization based fault-tolerance algorithm for the internet of things, KSII Trans Internet Inf Syst, 8, 4, pp. 1178-1191, (2014)

[2]

Xie Y., Zhu Y., Wang Y., Cheng Y., Xu R., Sani A.S., Yuan D., Yang Y., A novel directional and non-local-convergent particle swarm optimization based workflow scheduling in cloud-edge environment, Future Gener Comput Syst, 97, pp. 361-378, (2019)

[3]

Thanh Dat D., Doan H., Fbrc: Optimization of task scheduling in fog-based region and cloud, IEEE Trustcom/Bigdatase/Icess, 2017, pp. 1109-1114, (2017)

[4]

Wang H., Wang Y., Maximizing reliability and performance with reliability-driven task scheduling in heterogeneous distributed computing systems, J Ambient Intell Humanized Comput, (2018)

[5]

Wang T., Liu Z., Chen Y., Xu Y., Dai X., Load balancing task scheduling based on genetic algorithm in cloud computing, In: 2014 IEEE 12Th International Conference on Dependable, Autonomic and Secure Computing, pp. 146-152, (2014)

[6]

Zhan Z.H., Zhang G.Y., Gong Y.J., Zhang J., Load balance aware genetic algorithm for task scheduling in cloud computing, Simulated Evolution and Learning, pp. 644-655, (2014)

[7]

Yang X.-S., Swarm intelligence based algorithms: A critical analysis, Evol Intell, 7, pp. 17-28, (2014)

[8]

Strumberger I., Bacanin N., Tuba M., Enhanced firefly algorithm for constrained numer-ical optimization, ieee congress on evolutionary computation, Proceedings of the IEEE International Congress on Evolutionary Computation (CEC 2017), pp. 2120-2127, (2017)

[9]

Tuba M., Bacanin N., Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems, Neurocomputing, 2, pp. 197-207, (2014)

[10]

Bacanin N., Tuba M., Artificial bee colony (ABC) algorithm for constrained optimization improved with genetic operators, Stud Inf Control, 21, pp. 137-146, (2012)

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