Optimization of Cloud Computing Resources in Japan

Purpose: The aim of the study was to assess the optimization of cloud computing resources in Japan. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: This study found that advancements focus on various techniques such as dynamic resource allocation, load balancing, and auto-scaling. Dynamic resource allocation involves real-time adjustments based on current workloads to ensure optimal usage without over-provisioning, which minimizes costs. Load balancing techniques distribute workloads across multiple servers to prevent any single server from becoming a bottleneck, enhancing system reliability and performance. Auto-scaling allows cloud services to automatically scale up or down based on demand, ensuring that resources are available when needed and conserving them during low usage periods. These optimization strategies are supported by machine learning algorithms and predictive analytics, which further improve the precision of resource management and anticipate future demands. Overall, the optimization of cloud computing resources is essential for maintaining service quality, reducing operational costs, and meeting the dynamic needs of users and applications in an increasingly digital world. Implications to Theory, Practice and Policy: Economic resource allocation theory, queuing theory and game theory may be used to anchor future studies on assessing the optimization of cloud computing resources in Japan. Organizations should implement dynamic resource allocation mechanisms, such as the First Fit (FF) algorithm and dynamic auto-scaling strategies, to optimize resource utilization and improve system responsiveness in real-time. Policymakers should promote energy-efficient workload consolidation techniques and distributed cloud storage solutions to incentivize organizations to adopt sustainable resource management practices.