Volley: Violation Likelihood Based State Monitoring for Dataceners

Distributed state monitoring plays a critical role in industrial Cloud datacenter management. One fundamental problem in distributed state monitoring is to minimize the monitoring cost while maximizing the monitoring accuracy at the same time. In this paper, we present Volley, a violation likelihood based approach for efficient distributed state monitoring in Cloud datacenters. Volley achieves both efficiency and accuracy with a flexible monitoring framework which uses dynamic monitoring intervals determined by the likelihood of detecting state violations. Our approach consists of three techniques. First, we devise efficient node-level adaptation algorithms that minimize monitoring cost with controlled accuracy for both basic and advanced state monitoring models. Second, Volley employs a distributed scheme that coordinates the adaptation on multiple monitoring nodes of the same task for optimal tasklevel efficiency. Finally, Volley enables cost reduction with minimum accuracy loss by exploring state correlation at the multi-task level. We perform extensive experiments to evaluate our approach with industrial system, network and application monitoring tasks in a virtualized datacenter environment consisting of 800 VMs. Our results show that Volley can reduce considerable monitoring cost and still deliver user specified monitoring accuracy under various monitoring scenarios.