Diagnosing data center behavior flow by flow

Abstract

Multi-tenant data centers are complex environments, running thousands of applications that compete for the same infrastructure resources and whose behavior is guided by (sometimes) divergent configurations. Small workload changes or simple operator tasks may yield unpredictable results and lead to expensive failures and performance degradation. In this paper, we propose a holistic approach for detecting operational problems in data centers. Our framework, FlowDiff, collects information from all entities involved in the operation of a data center - applications, operators, and infrastructure - and continually builds behavioral models for the operation. By comparing current models with pre-computed, known-to-be-stable models, FlowDiff is able to detect many operational problems, ranging from host and network failures to unauthorized access. FlowDiff also identifies common system operations (e.g., VM migration, software upgrades) to validate the behavior changes against planned operator tasks. We show that using passive measurements on control traffic from programmable switches to a centralized controller is sufficient to build strong behavior models; FlowDiff does not require active measurements or expensive server instrumentation. Our experimental results using NEC data center testbed, Amazon EC2, and simulations demonstrate that FlowDiff is effective and robust in detecting anomalous behavior. FlowDiff scales well with the number of applications running in the data center and their traffic volume. © 2013 IEEE.