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This study undertakes a systematic exploration of contemporary virtual machine optimization strategies, aiming to unravel the intricate dynamics that shape computational efficiency in virtualized environments. The research synthesizes findings from a diverse array of recent studies published from 2020 onwards, encompassing themes such as dynamic resource allocation, machine learning integration, security fortification, and technology synergies. Through rigorous thematic analysis, a comprehensive framework is developed, providing a holistic view of the interrelated strategies that drive the evolution of virtual machine efficiency. Key insights underscore the significance of dynamic resource allocation in responding to fluctuating workloads, the pivotal role of machine learning in predictive resource provisioning, and the robustness of encryption-based security measures. Technology synergies, particularly the integration of containers and virtual machines, emerge as a crucial avenue for enhancing overall system efficiency. Quantum-inspired algorithms further add an avant-garde dimension to the discourse, showcasing potential breakthroughs in computational optimization. The study concludes by offering practical recommendations for practitioners, emphasizing the implementation of dynamic resource allocation, exploration of machine learning-driven solutions, enhancement of security measures, and adoption of technology synergies. Acknowledging context-specific limitations, this research lays a foundation for future investigations into emerging trends, providing valuable insights for organizations seeking to optimize virtualized systems.
Aliguay et al. (Fri,) studied this question.
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