Exploiting nonstationarity for performance prediction

Real production applications ranging from enterprise applications to large e-commerce sites share a crucial but seldom-noted characteristic: The relative frequencies of transaction types in their workloads are nonstationary, i.e., the transaction mix changes over time. Accurately predicting application-level performance in businesscritical production applications is an increasingly important problem. However, transaction mix nonstationarity casts doubt on the practical usefulness of prediction methods that ignore this phenomenon. This paper demonstrates that transaction mix nonstationarity enables a new approach to predicting application-level performance as a function of transaction mix. We exploit nonstationarity to circumvent the need for invasive instrumentation and controlled benchmarking during model calibration; our approach relies solely on lightweight passive measurements that are routinely collected in today’s production environments. We evaluate predictive accuracy on two...

• Type: Conference
• Year: 2007
• Where: EUROSYS
• Authors: Christopher Stewart, Terence Kelly, Alex Zhang
• Keywords: EUROSYS 2007, System Software, Transaction Mix, Transaction Mix Changes, Transaction Mix Nonstationarity