vDARM: Dynamic Adaptive Resource Management for Virtualized Multiprocessor Systems
Jianmin Qiana, Jian Lib, Ruhui Mac and Haibing Guand
Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, China
ahacker_qian@sjtu.edu.cn
bli-jian@sjtu.edu.cn
cruhuima@sjtu.edu.cn
dhbguan@sjtu.edu.cn
ABSTRACT
Modern data center servers have been enhancing their computing capacity by increasing processor counts. Meanwhile, these servers are highly virtualized to achieve efficient resource utilization and energy savings. However, due to the shifting of server architecture to non-uniform memory access (NUMA), current hypervisor-level or OS-level resource management methods continue to be challenged in their ability to meet the performance requirement of various user applications. In this work, we first build a performance slowdown model to accurate identify the current system overheads. Based on the model, we finally design a dynamic adaptive virtual resource management method (vDARM) to eliminate the runtime NUMA overheads by re-configuring virtual-to-physical resource mappings. Experiment results show that, compared with state-of-art approaches, vDARM can bring up an average performance improvement of 42.3% on an 8-node NUMA machines. Meanwhile, vDARM only incurs extra CPU utilization no more than 4%.
