{"title":"IGWOA: Improved Grey Wolf optimization algorithm for resource scheduling in cloud-fog environment for delay-sensitive applications","authors":"","doi":"10.1007/s12083-024-01642-w","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Fog computing, a technology that offers adaptable and scalable computing resources, facing a significant difficulty in task scheduling, affecting system performance and customer satisfaction. Finding solutions to the task scheduling problem is challenging due to its NP-completeness. Researchers suggest a hybrid approach that combines the Grey Wolf Optimization Algorithm (GWO) and Heterogeneous earliest finishing time (HEFT) to address this problem. The hybrid IGWOA (Improved Grey Wolf optimization algorithm) method seeks to minimize makespan and throughput while focusing on multi-objective resource scheduling in Fog computing. Proposed algorithm is suggested to improve the exploration and exploitation phases of the traditional grey wolf algorithm. Furthermore, the HEFT-based GWO algorithm has the benefit of faster convergence in larger scheduling problems. The effectiveness of the suggested algorithm in comparison to existing techniques has been evaluated using the iFogsim toolkit. Real data set and pseudo workloads both are used for working. The statistical method Analysis of Variance (ANOVA) is used to confirm the results. The effectiveness of it in reducing makespan, and throughput is demonstrated by experimental results on 200–1000 tasks. Particularly, the proposed approach outperforms peer competing techniques AEOSSA, HHO, PSO, and FA in relation to makespan and throughput; successfully, improvement is noticed on makespan up to 9.34% over the AEOSSA and up to 72.56% over other optimization techniques for pseudo workload. Additionally, it also showed improvement on makespan up to 6.89% over the AEOSSA and up to 69.73% over other optimization techniques on NASA iPSC and HPC2N real data sets, while improving throughput by 62.4%, 52.8%, and 41.6% on pseudo workload, NASA iPSC, and HPC2N data sets, respectively. These results show proposed approach solves the resource scheduling issue in Fog computing settings.</p>","PeriodicalId":49313,"journal":{"name":"Peer-To-Peer Networking and Applications","volume":"30 1","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Peer-To-Peer Networking and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12083-024-01642-w","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Fog computing, a technology that offers adaptable and scalable computing resources, facing a significant difficulty in task scheduling, affecting system performance and customer satisfaction. Finding solutions to the task scheduling problem is challenging due to its NP-completeness. Researchers suggest a hybrid approach that combines the Grey Wolf Optimization Algorithm (GWO) and Heterogeneous earliest finishing time (HEFT) to address this problem. The hybrid IGWOA (Improved Grey Wolf optimization algorithm) method seeks to minimize makespan and throughput while focusing on multi-objective resource scheduling in Fog computing. Proposed algorithm is suggested to improve the exploration and exploitation phases of the traditional grey wolf algorithm. Furthermore, the HEFT-based GWO algorithm has the benefit of faster convergence in larger scheduling problems. The effectiveness of the suggested algorithm in comparison to existing techniques has been evaluated using the iFogsim toolkit. Real data set and pseudo workloads both are used for working. The statistical method Analysis of Variance (ANOVA) is used to confirm the results. The effectiveness of it in reducing makespan, and throughput is demonstrated by experimental results on 200–1000 tasks. Particularly, the proposed approach outperforms peer competing techniques AEOSSA, HHO, PSO, and FA in relation to makespan and throughput; successfully, improvement is noticed on makespan up to 9.34% over the AEOSSA and up to 72.56% over other optimization techniques for pseudo workload. Additionally, it also showed improvement on makespan up to 6.89% over the AEOSSA and up to 69.73% over other optimization techniques on NASA iPSC and HPC2N real data sets, while improving throughput by 62.4%, 52.8%, and 41.6% on pseudo workload, NASA iPSC, and HPC2N data sets, respectively. These results show proposed approach solves the resource scheduling issue in Fog computing settings.
期刊介绍：
The aim of the Peer-to-Peer Networking and Applications journal is to disseminate state-of-the-art research and development results in this rapidly growing research area, to facilitate the deployment of P2P networking and applications, and to bring together the academic and industry communities, with the goal of fostering interaction to promote further research interests and activities, thus enabling new P2P applications and services. The journal not only addresses research topics related to networking and communications theory, but also considers the standardization, economic, and engineering aspects of P2P technologies, and their impacts on software engineering, computer engineering, networked communication, and security.
The journal serves as a forum for tackling the technical problems arising from both file sharing and media streaming applications. It also includes state-of-the-art technologies in the P2P security domain.
Peer-to-Peer Networking and Applications publishes regular papers, tutorials and review papers, case studies, and correspondence from the research, development, and standardization communities. Papers addressing system, application, and service issues are encouraged.
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