Regression based multi-tier resource provisioning for session slowdown guarantees

Autonomous management of a multi-tier Internet service involves two critical and challenging tasks, one understanding its dynamic behavior when subjected to dynamic workload and second adaptive management of its resources to achieve performance guarantees. In this paper, we propose a statistical machine learning based approach to achieve session slowdown guarantees of a multi-tier Internet service. Session slowdown is the ratio of a session's total queueing delay to its total processing time. It is a compelling performance metric of session-based Internet services because it directly measures user-perceived relative performance. However, there is no analytical model for session slowdown on multi-tier servers. We first conduct training to learn the statistical regression models that quantitatively capture an Internet service's dynamic behavior as relationships between various service parameters. Then, we propose a dynamic resource provisioning approach that utilizes the learned regression models to efficiently achieve session slowdown guarantees under varying workloads. The approach is based on the combination of extensive offline training and online monitoring of the Internet service behavior. Experiments using the industry standard TPC-W benchmark demonstrate the effectiveness and efficiency of the regression based dynamic resource provisioning approach in meeting the session slowdown guarantees of a multi-tier e-commerce application.