SURVEY ON DYNAMIC POWER OPTIMIZATION IN MULTI-TENANT CLOUD COMPUTING

Abstract

As cloud computing continues to evolve, the challenge of optimizing power consumption in multi-tenant environments has become increasingly critical. This survey paper explores dynamic power optimization strategies tailored for multi-tenant cloud infrastructures, where multiple users share resources while maintaining performance and service-level agreements (SLAs). We review existing techniques, including workload management, resource allocation, and virtualization strategies, focusing on their effectiveness in minimizing energy consumption without compromising operational efficiency. The paper categorizes these strategies into proactive and reactive approaches, assessing their strengths and weaknesses in dynamic environments. Additionally, we examine the role of emerging technologies such as machine learning and edge computing in enhancing power optimization efforts. By providing a comprehensive overview of current methodologies and identifying gaps in research, this survey aims to offer insights and guidelines for future developments in sustainable cloud computing practices. Ultimately, our findings highlight the importance of adopting holistic power management solutions that address the unique challenges posed by multi-tenancy, paving the way for greener cloud infrastructures.