PheCon: Fine-Grained VM Consolidation with Nimble Resource Defragmentation in Public Cloud Platforms
Jiazhen Zhu, Wenda Tang, Xianglong Meng, Nan Gong, Tianxiang Ai, Guanghui Li, Bin Yu, Yang Xin · 2024
Resource fragmentation is inevitable due to the unknown and fluctuating sequence of requests to create or delete virtual machines (VMs) in cloud platforms. VM consolidations can be effective in addressing resource fragmentation issues to ensure better utilization of multi-dimension resources. However, current VM consolidation solutions primarily focus on optimizing the utilization of resources at the level of physical machines (PMs) and often require migrating all VMs of the target PM to other PMs, which can result in unnecessary and inappropriate VM migrations. In this paper, we propose a novel nimble fine-grained VM consolidation algorithm, PheCon, which focuses on fine-grained consolidation by considering VM flavors as the unit of consolidation. It attempts to aggregate the resource fragments from PMs and gather resources for additional VM allocations with specific flavors. In contrast to the state-of-the-art method, PheCon does not attempt to release PMs, but instead focuses on utilizing resource fragmentation to increase the number of additional VM allocations. Besides, to further reduce the resource fragmentation on PMs, PheCon leverages a hierarchical swapping method that enables placing VMs into PMs with insufficient free resources by swapping a part of smaller VMs to other PMs. In addition, to improve generalizability, PheCon takes NUMA systems into consideration, determining both the target PM and NUMA nodes for VM consolidation. Comprehensive evaluation using simulation and our production cloud datasets shows that PheCon could reduce the number of VM migrations by 35% on average compared to the state-of-the-art method.