Improved chaotic binary grey wolf optimization algorithm for workflow scheduling in green cloud computing
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RESEARCH PAPER
Improved chaotic binary grey wolf optimization algorithm for workflow scheduling in green cloud computing Ali Mohammadzadeh1 · Mohammad Masdari1 · Farhad Soleimanian Gharehchopogh1 · Ahmad Jafarian2 Received: 3 April 2020 / Revised: 9 July 2020 / Accepted: 25 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The workflow scheduling in the cloud computing environment is a well-known NP-complete problem, and metaheuristic algorithms are successfully adapted to solve this problem more efficiently. Grey wolf optimization (GWO) is a recently proposed interesting metaheuristic algorithm to deal with continuous optimization problems. In this paper, we proposed IGWO, an improved version of the GWO algorithm which uses the hill-climbing method and chaos theory to achieve better results. The proposed algorithm can increase the convergence speed of the GWO and prevents falling into the local optimum. Afterward, a binary version of the proposed IGWO algorithm, using various S functions and V functions, is introduced to deal with the workflow scheduling problem in cloud computing data centers, aiming to minimize their executions’ cost, makespan, and the power consumption. The proposed workflow scheduling scheme is simulated using the CloudSim simulator and the results show that our scheme can outperform other scheduling approaches in terms of metrics such as power consumption, cost, and makespan. Keywords Meta-heuristic · Grey wolf optimization · Green cloud computing · Workflow scheduling
1 Introduction Cloud computing provides an interesting technology that makes scientific and industrial projects easier to implement. Infrastructure as a service (IaaS) is a type of cloud computing that provides online resources for virtualized computing. In addition to software as a service (SaaS) and platform as a service (PaaS), IaaS is one of the three main categories of cloud computing services [1]. Cloud computing can be used to deploy highly complex applications for scientific workflow. Workflows break down complex, data-intensive * Mohammad Masdari [email protected] Ali Mohammadzadeh [email protected] Farhad Soleimanian Gharehchopogh [email protected] Ahmad Jafarian [email protected] 1
Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran
2
applications into smaller tasks and perform them in serial or parallel depending on the application’s nature. Workflow models are commonly used in fields such as science, business, and engineering. In the planning of the scientific workflow, we need to take the following questions: (1) how to allocate tasks to VMs; (2) in what order the VMs will perform tasks taking into account the data dependency between tasks. Usually, these scientific workflows require a great deal of data of different sizes and simulations of long-term computers. We need high computing power and the availability of large infrastr
