Performance-Monitoring-Based Traffic-Aware Virtual Machine Deployment on NUMA Systems

Virtualization technology enables multiple virtual machines (VMs) to share a single physical server. Commercial servers increasingly use the nonuniform memory access (NUMA) architecture due to its scalable memory performance. However, multiple VMs running on a NUMA physical server will cause performance overheads such as remote memory access latency and shared microarchitectural resource contention, which makes the VM performance less efficient and stable. These performance overheads are mainly caused by memory traffic from data-intensive workloads. In this paper, we propose a traffic-aware VM optimization (TAVO) scheme on NUMA systems. Based on the performance monitoring of the data traffic and CPU/memory resource usages in the system, TAVO addresses VM memory access locality and shared resource contention problems via automatic VM initial placement and NUMA-aware VM online scheduling. Our experimental results show that TAVO improves VM performance in terms of benchmark runtime by up to 22.6% compared with the default KVM CFS scheduler. TAVO also achieves a much stable performance with benchmark's average runtime variation under 3%.