Handling hierarchy in cloud data centers: A Hyper-Heuristic approach for resource contention and energy-aware Virtual Machine management

For cloud data centers, a performant yet energy -efficient operation is critical for service quality and experience. The growing demand for cloud -based services has led to the development of large-scale hierarchical data center structures, characterized by horizontal expansion and vertical hierarchy, leading to challenges in managing Virtual Machines (VM) at a granular level. The hierarchical arrangement can increase the risk of deployment failures, often stemming from inadequate computational resources on physical hosts, even when the clusterlevel resources seem sufficient. While substantial work has gone into managing VMs at the physical host level, there remains a dearth of research under hierarchical data center configurations. To fill the research gap, we address the hierarchy in cloud data centers with a novel two -stage approach named VMM-HHGT, aiming at suppressing VM deployment failures, while balancing the energy consumption and computation resource contention. VMM-HHGT comprises a Hyper -Heuristic -assisted broker (VMM-HH), which can learn the workload patterns and hardware configurations to generate cluster -selection heuristics. An offline training process is incorporated for continuous heuristic evolution with zero overhead on decision -making. Besides, a Game -Theory -assisted hypervisor (GT) is designed for inter -host live VM migration for fine-grained balancing of energy consumption and resource contention. Extensive experiments with traces from real -world VMware data centers show that VMM-HHGT achieves a higher deployment success rate compared to the state-of-the-art approaches, with a well -situated performance in energy consumption and resource contention.