Volley: Violation Likelihood Based State Monitoring for Datacenters

Distributed state monitoring plays a critical role in 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. Volley consists of three unique techniques. It utilizes efficient node-level adaptation algorithms that minimize monitoring cost with controlled accuracy. Volley also employs a distributed scheme that coordinates the adaptation on multiple monitor nodes of the same task for optimal task- level efficiency. Furthermore, it enables multi-task level cost reduction by exploring state correlation among monitoring tasks. We perform extensive experiments to evaluate Volley with system, network and application monitoring tasks in a virtualized datacenter environment. Our results show that Volley can reduce considerable monitoring cost and still deliver user specified monitoring accuracy under various scenarios.