Research and Teaching Statement

My research focuses on both policy and mechanism for managing datacenter-scale installations (thousands of computers) of interactive-response, Internet-resident services. “Management ” includes recovery from failures, on-demand scalability, dynamic resource allocation, and improved power efficiency. The mechanism for carrying out these tasks is to construct software building blocks in which common operations, such as failure recovery, scaling up/down, or reprovisioning, can be achieved by rebooting a machine (or its dual, adding a new machine and killing the faulty one). The policy is based on the use of statistical machine learning (SML) techniques to automatically identify and react to problems that would take too long for a human operator to diagnose manually. The ideal of 99.999 % service availability corresponds to just 5 minutes of service downtime per year, which cannot be achieved if humans must participate in every operational decision [2]. Two main themes of my previous work on Recovery-Oriented Computing (ROC) have influenced the design of recent commercial and research systems. The first is the design stance of crash-only software [3]: since robust software must survive unexpected crashes anyway, the crash recovery code should be the only recovery code, and any non-crash problem (slowdown, anomalous behavior, etc.) observed during operation should be immediately coerced to a crash failure. This is a radical design simplification that allows focusing on optimizing the performance of the one and only recovery path. The second theme is exploiting this fast recovery by applying SML problem detectio