Cross-Layer Optimization for Virtual Machine Resource Management

Virtualized systems (e.g., public and private clouds) are playing an increasingly vital role to support the computing of applications from different domains. Existing resource management solutions in such systems typically treat virtual machines (VMs) as black boxes, which presents a hurdle to achieving application-desired Quality of Service (QoS). This paper advocates the cooperation between VM host- and guest-layer schedulers for optimizing the resource management and application performance. It presents an approach to such cross-layer optimization by enabling the host-layer scheduler to feedback resource allocation decisions and adapt guest-layer application configurations. As case studies, the proposed approach is applied to virtualized databases and map services which have challenging dynamic and complex resource demands as well as sophisticated configurations. Specifically, for databases, the proposed approach adapts query executions by tuning the cost model parameters according to the available storage bandwidth and memory capacity. For map services, it adapts the quality of returned map imagery in order to meet the response time target as the workload intensity and available network bandwidth change over time. A prototype of the proposed approach is implemented on Xen and Hyper-V VMs, and evaluated using a TPC-H based database workload and a TerraFly-based map service workload. The results show that with the proposed host-to-guest application adaptation, the TPC-H workload improves its performance by 33.5%, and the TerraFly workload improves the map imagery quality by 40% and always meets its response time target, compared to the schemes without adaptation.