A Classification-Based Approach to Policy Refinement

Systems are typically designed based on certain high level goals, such as performance and availability. On the other hand, during operation, usually only low level metrics (e.g., CPU utilization) are measured. The system administrators use their expertise to implicitly map bounds on these metrics such that the high level goals are met. The objective of this research is to create an automated and domain independent approach to derive policy bounds on the low level metrics such that the high level goals are met. These policies may be also be used for monitoring the system for goal assessment purposes. The refinement is carried out using a combination of data classification and test-and-development approaches. An ad hoc system is deployed and a dataset containing values of selected metrics is collected by placing appropriate workloads on the system. The policy bounds are derived by applying classification techniques on this dataset. The classification rules are further refined using statistical distributions to arrive at certain low level rules that are useful for system monitoring and to check the system health when it is deployed and running. We show the validity of our approach for an e-commerce auctioning system (RubiS).