OPTIMIZED LOAD BALANCING TECHNIQUES IN CLOUD COMPUTING ENVIRONMENT: A SYSTEMATIC LITERATURE REVIEW AND FUTURE TRENDS

In today's rapidly evolving computing landscape, cloud computing has emerged as a pivotal paradigm that offers scalable and flexible resource provisioning to meet the demands of diverse applications. A critical aspect in this environment is load balancing, a technique that optimally distributes workloads across available resources to ensure efficient resource utilization, enhanced system performance, and seamless user experiences. This systematic literature review (SLR) delves into the realm of optimized load balancing techniques within cloud computing environments, encompassing various approaches, optimization methods, challenges, and future trends. The SLR reviews 50 research articles to track the evolution of load balancing techniques from traditional to modern dynamic strategies in cloud infrastructures. The review categorizes load balancing techniques into centralized, decentralized, and hybrid methods, noting their features and limitations. It highlights the importance of optimization methods, like meta-heuristics, machine learning, and swarm intelligence, in enhancing resource allocation and response times, and addresses challenges such as heterogeneity, scalability, and real-time adaptability.This review also identifies imminent challenges faced by load balancing in cloud computing environments. These challenges encompass the intricate interplay between resource allocation, fluctuating workloads, and dynamic system conditions. Additionally, the review offers insights into future trends that include harnessing the power of artificial intelligence and machine learning techniques, exploring fog and edge computing for load distribution, and exploring hybrid load balancing strategies to further enhance system efficiency. This review offers a comprehensive understanding of optimized load balancing in cloud computing, covering the evolution of techniques, optimization methods, and challenges. It also identifies emerging trends to guide future research and practice.In the subsequent sections, we delve deeper into the current state of research, outlining the proposed approach in alignment with the research objectives. Subsequently, we expound upon the experimental outcomes attained through the proposed framework, encapsulating both its methodology and results.