International Journal of Science, Engineering and TechnologyAuthors: Jessica Howard, Samuel Price, Victoria Stewart, Abigail Foster, Chaitanya Srinivas, Rishi Kumar
Abstract: Cloud computing environments require efficient resource allocation and load balancing mechanisms to ensure high performance, scalability, reliability, and optimal utilization of computational resources. As cloud applications continue to experience dynamic and unpredictable workloads, traditional static allocation techniques often struggle to maintain service quality and operational efficiency. This study presents an evidence mapping analysis of smart resource allocation and load balancing strategies employed in modern cloud applications, focusing on their effectiveness in traffic optimization, workload distribution, response time reduction, and infrastructure utilization. The research systematically reviews and categorizes existing approaches, including heuristic algorithms, predictive analytics, machine learning-based techniques, adaptive scheduling models, and intelligent traffic management frameworks. Through evidence mapping, the study identifies prevailing research trends, evaluation metrics, implementation challenges, and emerging opportunities in cloud resource optimization. The findings indicate that intelligent and adaptive load balancing mechanisms significantly improve application performance, fault tolerance, scalability, and energy efficiency compared to conventional methods. Furthermore, the analysis highlights the growing integration of artificial intelligence, real-time monitoring, and autonomous decision-making systems in achieving efficient cloud resource management. The study concludes that smart load balancing and resource allocation strategies play a critical role in enhancing cloud application performance and operational resilience, while the evidence mapping framework provides valuable insights for researchers and practitioners seeking to develop next-generation cloud infrastructures capable of supporting increasingly complex and dynamic workloads.
DOI: http://doi.org/10.5281/zenodo.20637125
