Dynamic Balanced Configuration of Multi-resources in Virtualized Clusters

Dynamic resource configuration is crucial to the provisioning of service level agreements (SLAs) in cloud computing. Most of today's autonomic resource configuration approaches are designed to scale a single type of resource. A few works are able to partition multiple resources, but mainly to meet the requirement of throughput. Unlike throughput, however, response time behaves nonlinearly with respect to resources. Therefore, these approaches are hardly applicable to dynamic sharing of multi-resources for the provisioning of response time guarantee. Moreover, the optimization of resource efficiency and utilization has great significance to IaaS providers. We show theoretically and experimentally that resource optimization lies in balanced configuration of resources. In this paper, we propose a framework, BConf, for dynamic balanced configuration of multi-resources for the provisioning of response time guarantee in virtualized clusters. BConf employs an integrated MPC (model predictive control) and adaptive PI (proportional integral) control approach (IMAP). MPC is applied to actively balance multiple resources using a novel resource metric. For the performance prediction, a gray-box model is built on generic OS and hardware metrics in addition to resource actuators and performance. We find out that resource penalty is an effective metric to measure the imbalanced degree of a configuration. Using this metric and the model, BConf tunes resources in a balanced way by minimizing the resource penalty while satisfying the response time target. Adaptive PI is used to coordinate with MPC by narrowing the optimization space to a promising region. Within BConf framework, resources are coordinated during contention. Experimental results with mixed TPC-W and TPC-C benchmarks show that BConf reduces resource usages by about 50% and 30% for TPC-W and TPC-C respectively, improves stability by more than 35.6%, and has a much shorter settling time, in comparison with a representative partition approach. The advantages of BConf in resource coordination are also demonstrated.