Efficient Load Balancing in Hybrid Clouds through Optimized VM Migration Strategies

Abstract

Dynamic VM migration optimization plays a pivotal role in enhancing the efficiency, performance, and cost-effectiveness of hybrid cloud environments. By intelligently transferring virtual machines (VMs) between private and public cloud resources, organizations can optimize resource utilization, improve application performance, and ensure business continuity. This research explores the challenges and opportunities associated with dynamic VM migration optimization in hybrid clouds. It delves into existing optimization algorithms, performance metrics, and security considerations. Furthermore, the research investigates the potential of emerging technologies like artificial intelligence and machine learning to enhance optimization strategies. Key research contributions include a comprehensive evaluation of existing optimization algorithms, considering their strengths, weaknesses, and suitability for different hybrid cloud scenario followed by the development of novel optimization techniques that incorporate advanced machine learning algorithms for more accurate workload prediction and resource allocation. An in-depth analysis of security implications associated with VM migration and the development of security-centric optimization strategies to mitigate risks will also be conducted. The exploration of the integration of dynamic VM migration optimization with emerging technologies like container orchestration and serverless computing to address the evolving needs of modern applications would be the cornerstone for this research project. This research also aims to provide valuable insights for organizations seeking to optimize their hybrid cloud environments. The findings can guide the development of effective VM migration strategies that improve resource utilization, enhance application performance, and ensure a secure and scalable hybrid cloud infrastructure