Managing Data Center Tickets: Prediction and Active Sizing

Abstract

Performance ticket handling is an expensive operation in highly virtualized cloud data centers where physical boxes host multiple virtual machines (VMs). A large body of tickets arise from the resource usage warnings, e.g., CPU and RAM usages that exceed predefined thresholds. The transient nature of CPU and RAM usage as well as their strong correlation across time among co-located VMs drastically increase the complexity in ticket management. Based on a large resource usage data collected from production data centers, amount to 6K physical machines and more than 80K VMs, we first discover patterns of spatial dependency among co-located virtual resources. Leveraging our key findings, we develop an Active Ticket Managing(ATM) system that consists of (i) a novel time series prediction methodology and (ii) a proactive VM resizing policy for CPU and RAM resources for co-located VMs on a physical box that aims to drastically reduce usage tickets. ATM exploits the spatial dependency across multiple resources of co-located VMs for usage prediction and proactive VM resizing. Evaluation results on traces of 6K physical boxes and a prototype of a MediaWiki system show that ATM is able to achieve excellent prediction accuracy of a large number of VM time series and significant usage ticket reduction, i.e., up to 60%, at low computational overhead.