C-KHCS: Multi-Objective Workflow Scheduling using Chaotic Krill Herd Optimization and Improved Cuckoo Search in Fog Computing

Today, fog computing as a complement to cloud computing has attracted much attention in research communities, because it has great potential to provide the processing resources and services nenoeded for applications at the edge of the network close to users. Nonetheless, inefficient scheduling of wo...

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Veröffentlicht in:IEEE transactions on services computing Jg. 17; H. 5; S. 2095 - 2108
Hauptverfasser: Nazemi, Shahin, Khorsand, Reihaneh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2024
Schlagworte:
chaotic map
cuckoo search
Edge computing
Heuristic algorithms
Internet of Things
krill herd
Levy flight
Processor scheduling
Scheduling
Search problems
Task analysis
Workflow scheduling
ISSN:1939-1374, 2372-0204
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Zusammenfassung:Today, fog computing as a complement to cloud computing has attracted much attention in research communities, because it has great potential to provide the processing resources and services nenoeded for applications at the edge of the network close to users. Nonetheless, inefficient scheduling of workflows in fog computing infrastructures leads to bandwidth wastage, resource wastage, and unfavorable quality of service (QoS). The main challenges in fog computing are finding suitable nodes to execute workflows and scheduling them in such a way as to improve the speed of convergence and avoid local optima. To overcome these problems, in this paper, we propose a multi-objective workflow scheduling algorithm using Chaotic Krill Herd optimization and improved Cuckoo Search called C-KHCS, while applying the chaotic map to improve the initial population production and escape from a local optimal solution. Moreover, the improved cuckoo search algorithm increases the global search space of the krill algorithm to reach a global optimal solution. The simulation results indicate that the proposed algorithm outperforms its competitors to minimize makespan and energy consumption.
ISSN:1939-1374
2372-0204
DOI:10.1109/TSC.2024.3384094