University of Idaho
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, the authors propose a holistic approach for detecting operational problems in data centers. Their 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.