A Comprehensive Analysis of Load Balancing Techniques: Identifying Gaps in Energy Efficiency, Adaptability, and Scalability

Load balancing is a critical component in distributed systems, ensuring efficient resource utilization, minimized response time, and improved system performance. Despite numerous algorithms proposed in the literature—ranging from static to dynamic and heuristic to AI-based approaches—many still face challenges concerning energy optimization, adaptability to dynamic workloads, and scalability in large-scale environments. This paper presents a comprehensive review and comparative analysis of existing load balancing techniques across cloud, edge, and distributed computing environments. The study systematically evaluates each algorithm’s operational principles, strengths, and limitations. Key performance parameters such as energy consumption, load adaptability, fault tolerance, and scalability are examined. Through this analysis, the paper identifies significant research gaps and proposes essential criteria that a next-generation load balancing algorithm must fulfill. The findings lay the groundwork for developing a more robust, adaptive, and energy-efficient load balancing solution tailored for future computing paradigms.