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Efficient Job Scheduling Paradigm Based on Hybrid Sparrow Search Algorithm and Differential Evolution Optimization for Heterogeneous Cloud Computing Platforms by Khaleel, Mustafa Ibrahim (author) is a Computer Science article available to read on EtoBox.
What is Efficient Job Scheduling Paradigm Based on Hybrid Sparrow Search Algorithm and Differential Evolution Optimization for Heterogeneous Cloud Computing Platforms about?
The job scheduling paradigms include dispatching Internet of Things (IoT) critical services onto processing nodes. Here most energy is consumed in finding suitable virtual machines (VMs) that can execute IoT tasks without resource fragments. Therefore, a significant problem is minimizing energy consumption through efficient task placement that leads to load balance and minimizes resource leakage. To resolve this problem, we proposed a dual-phase metaheuristic algorithm called CSSA-DE. First, we conduct a clustering approach to group computing nodes into effective clusters. Each node is trained at different utilization levels, and the one that can yield the highest Performance-to-Power Ratio (PPR) is selected as the mega cluster head (MCH). Then, we integrated the sparrow search algorithm (SSA) with the differential evolution (DE) algorithm to expand the high search efficiency of finding an appropriate pair task-VM combination. Further, the integration phase can exploit the count of overloaded and underloaded VMs, reducing resource fragments. The performance of CSSA-DE is highly competitive and relatively better in multiple cases compared to state-of-the-art algorithms.
Who reads Efficient Job Scheduling Paradigm Based on Hybrid Sparrow Search Algorithm and Differential Evolution Optimization for Heterogeneous Cloud Computing Platforms?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Khaleel, Mustafa Ibrahim (author)
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Computer Science (Physical Sciences)