A Two‐Decade Analysis of Load Balancing in Cloud Computing: Implications for Educational System and Future Directions

• Published in: Journal of computer assisted learning Vol. 41; no. 3
• Main Authors: Diwaker, Chander, Miglani, Neha
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.06.2025; Wiley Subscription Services, Inc
• Subjects: bibliometric analysis; Bibliometrics; Citation analysis; Cloud computing; Clustering; Documents; Geographic Distribution; Geographical distribution; Load balancing; Network analysis; research trends; text mining; Trend Analysis; Trends
• ISSN: 0266-4909; 1365-2729
• DOI: 10.1111/jcal.70042

ABSTRACT 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.