Multi-Tiered On-Demand Resource Scheduling for VM-Based Data Center

The trend of using virtualization for server consolidation is more and more popular in enterprise data center. However, on-demand resource allocation among the concurrent hosted services in such a virtualized environment is still a challenge. In order to optimize resource allocation among services in data center, this paper proposes a multi-tiered resource scheduling scheme which automatically provides on-demand capacities to the hosted services via resources flowing among VMs. We model the resource flowing using optimization theory. Based on this model, we present a global re-source flowing algorithm in the multi-tiered resource scheduling scheme. This algorithm preferentially ensures performance of some critical services by degrading of others to some extent when resource competition arises. Using our RAINBOW prototype, we evaluate the multi-tiered resource scheduling scheme with the performance improvements for the most critical services up to 9%~16%, which are 75% of the maximum improvement margin, while performance degradation of others is up to 2%, and leads to 1%~5% improvements in resource utilization than RAINBOW without resource flowing. Compared with the existent scheme, our work leads to 9% less improvements for critical services, while introduces 39% less degradation to low priority services.