Automated debugging of SLO violations in enterprise systems

Abstract

A critical business requirement of today's enterprise applications is automated debugging of violation of Service Level Objectives (SLOs). However, the increasing scale and complexity of these systems present various challenges in building such solutions. This problem becomes challenging mainly because of two reasons: (1) availability of large number of metrics that can potentially be the causes, (2) availability of large number of data-points. The existing techniques are either highly compute-intensive and thus are not viable for use on large volumes of data or compromise on accuracy. To successfully balance these two objectives simultaneously, we propose to intelligently prune the search space. We apply feature selection to remove irrelevant and redundant metrics. We then identify temporal regions of interest to narrow down the analysis to a smaller set of data-points. We present a comparative study of the proposed approach with other existing approaches through experimental evaluation.