Relationship between Cloud Computing Resource Allocation Algorithms and Energy Efficiency in Data Centers in China

Purpose: The purpose of this article was to analyze the relationship between cloud computing resource allocation algorithms and energy efficiency in data centers in China. 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: ​ China show that advanced cloud resource allocation algorithms like Ant Colony Optimization and Genetic Algorithms improve data center energy efficiency by 20–35%. Integrating predictive models further reduces power use and cooling needs. However, adoption is limited by high costs and security concerns. Unique Contribution to Theory, Practice and Policy: The resource-based view (RBV), the green it adoption theory & the complex adaptive systems theory may be used to anchor future studies on relationship between cloud computing resource allocation algorithms and energy efficiency in data centers in China. Researchers and practitioners should implement pilot projects in operational cloud data centers to collect longitudinal performance data on energy savings, workload handling, and potential trade-offs (e.g., latency, migration overhead). Policymakers should consider financial incentives, green certifications, and regulatory frameworks that encourage investment in energy-aware scheduling technologies.