{"title":"云计算中负载平衡的二十年分析：对教育系统和未来方向的启示","authors":"Chander Diwaker, Neha Miglani","doi":"10.1111/jcal.70042","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>The ever-increasing computational demands call for optimal resource utilisation and system performance in the cloud environment. Organisations are increasingly migrating workloads to cloud platforms, mandating the need for efficient resource distribution. Load balancing, a critical cloud component, ensures equitable distribution of compute resources, thereby mitigating resource bottlenecks while enhancing system scalability.</p>\n </section>\n \n <section>\n \n <h3> Objectives</h3>\n \n <p>This paper presents a novel and comprehensive bibliometric analysis of research in the field of LB in cloud over the past 20 years. Unlike prior analyses, it aims at employing a broader dataset, insightful observations and streamlined methodologies to identify key trends, potential impacts, evolving landscapes and associated challenges.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Data has been retrieved from the Scopus database, encompassing 5978 articles published from 2004 to 2023. The analysis includes document types, subject-based categorizations, and the growth rate of publications. Unlike prior studies, this work yields a comparative dissection of influential contributions, identifying prominent journals, prolific authors, leading funding agencies, and geographic distribution illustrating global research impact. Additionally, citation clustering and keyword evolution emphasise drifts in research cornerstones and cropping challenges, offering deeper comprehensions into the domain's progression. Advanced bibliometric techniques such as co-citation analysis and network analysis uncover research patterns.</p>\n </section>\n \n <section>\n \n <h3> Results and Conclusions</h3>\n \n <p>This analysis underscores trends and knowledge gaps in LB over cloud. The findings offer a structured roadmap for future research, affirming the need for intelligent LB schemes aimed at enhancing cloud performance. It serves as a valuable resource for domain experts and researchers by providing insights into the current state, the field's evaluation while waving path for future advancements in cloud-based LB.</p>\n </section>\n </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 3","pages":""},"PeriodicalIF":5.1000,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Two-Decade Analysis of Load Balancing in Cloud Computing: Implications for Educational System and Future Directions\",\"authors\":\"Chander Diwaker, Neha Miglani\",\"doi\":\"10.1111/jcal.70042\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>The ever-increasing computational demands call for optimal resource utilisation and system performance in the cloud environment. Organisations are increasingly migrating workloads to cloud platforms, mandating the need for efficient resource distribution. Load balancing, a critical cloud component, ensures equitable distribution of compute resources, thereby mitigating resource bottlenecks while enhancing system scalability.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Objectives</h3>\\n \\n <p>This paper presents a novel and comprehensive bibliometric analysis of research in the field of LB in cloud over the past 20 years. Unlike prior analyses, it aims at employing a broader dataset, insightful observations and streamlined methodologies to identify key trends, potential impacts, evolving landscapes and associated challenges.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>Data has been retrieved from the Scopus database, encompassing 5978 articles published from 2004 to 2023. The analysis includes document types, subject-based categorizations, and the growth rate of publications. Unlike prior studies, this work yields a comparative dissection of influential contributions, identifying prominent journals, prolific authors, leading funding agencies, and geographic distribution illustrating global research impact. Additionally, citation clustering and keyword evolution emphasise drifts in research cornerstones and cropping challenges, offering deeper comprehensions into the domain's progression. Advanced bibliometric techniques such as co-citation analysis and network analysis uncover research patterns.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results and Conclusions</h3>\\n \\n <p>This analysis underscores trends and knowledge gaps in LB over cloud. The findings offer a structured roadmap for future research, affirming the need for intelligent LB schemes aimed at enhancing cloud performance. It serves as a valuable resource for domain experts and researchers by providing insights into the current state, the field's evaluation while waving path for future advancements in cloud-based LB.</p>\\n </section>\\n </div>\",\"PeriodicalId\":48071,\"journal\":{\"name\":\"Journal of Computer Assisted Learning\",\"volume\":\"41 3\",\"pages\":\"\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2025-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computer Assisted Learning\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jcal.70042\",\"RegionNum\":2,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Assisted Learning","FirstCategoryId":"95","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jcal.70042","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
A Two-Decade Analysis of Load Balancing in Cloud Computing: Implications for Educational System and Future Directions
Background
The ever-increasing computational demands call for optimal resource utilisation and system performance in the cloud environment. Organisations are increasingly migrating workloads to cloud platforms, mandating the need for efficient resource distribution. Load balancing, a critical cloud component, ensures equitable distribution of compute resources, thereby mitigating resource bottlenecks while enhancing system scalability.
Objectives
This paper presents a novel and comprehensive bibliometric analysis of research in the field of LB in cloud over the past 20 years. Unlike prior analyses, it aims at employing a broader dataset, insightful observations and streamlined methodologies to identify key trends, potential impacts, evolving landscapes and associated challenges.
Methods
Data has been retrieved from the Scopus database, encompassing 5978 articles published from 2004 to 2023. The analysis includes document types, subject-based categorizations, and the growth rate of publications. Unlike prior studies, this work yields a comparative dissection of influential contributions, identifying prominent journals, prolific authors, leading funding agencies, and geographic distribution illustrating global research impact. Additionally, citation clustering and keyword evolution emphasise drifts in research cornerstones and cropping challenges, offering deeper comprehensions into the domain's progression. Advanced bibliometric techniques such as co-citation analysis and network analysis uncover research patterns.
Results and Conclusions
This analysis underscores trends and knowledge gaps in LB over cloud. The findings offer a structured roadmap for future research, affirming the need for intelligent LB schemes aimed at enhancing cloud performance. It serves as a valuable resource for domain experts and researchers by providing insights into the current state, the field's evaluation while waving path for future advancements in cloud-based LB.
期刊介绍：
The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope
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