Vipul Mathur

Responding rapidly to service level violations using virtual appliances

One of the key goals in the data center today is providing storage services with service-level objectives (SLOs) for performance metrics such as latency and throughput. Meeting such SLOs is challenging due to the dynamism observed in these environments. In this position paper, we propose dynamic instantiation of virtual appliances, that is, virtual machines with storage functionality, as a mechanism to meet storage SLOs efficiently. In order for dynamic instantiation to be realistic for rapidlychanging environments, it should be automated. Therefore, an important goal of this paper is to show that such automation is feasible. We do so through a caching case study. Specifically, we build the automation framework for dynamically instantiating virtual caching appliances. This framework identifies sets of interfering workloads that can benefit from caching, determines the cache-size requirements of workloads, non-disruptively migrates the application to use the cache, and warms the cache to quickly return to acceptable service levels. We show through an experiment that this approach addresses SLO violations while using resources efficiently.

Model building for dynamic multi-tenant provider environments

Increasingly, storage vendors are finding it difficult to leverage existing white-box and black-box modeling techniques to build robust system models that can predict system behavior in the emerging dynamic and multi-tenant data centers. White-box models are becoming brittle because the model builders are not able to keep up with the innovations in the storage system stack, and black-box models are becoming brittle because it is increasingly difficult to a priori train the model for the dynamic and multi-tenant data center environment. Thus, there is a need for innovation in system model building area. In this paper we present a machine learning based blackbox modeling algorithm called M-LISP that can predict system behavior in untrained region for these emerging multitenant and dynamic data center environments. We have implemented and analyzed M-LISP in real environments and the initial results look very promising. We also provide a survey of some common machine learning algorithms and how they fare with respect to satisfying the modeling needs of the new data center environments.

Anode: Empirical detection of performance problems in storage systems using time-series analysis of periodic measurements

Performance problems are particularly hard to detect and diagnose in most computer systems, since there is no clear failure apart from the system being slow. In this paper, we present an empirical, data-driven methodology for detecting performance problems in data storage systems, and aiding in quick diagnosis once a problem is detected. The key feature of our solution is that it uses a combination of time-series analysis, domain knowledge and expert inputs to improve the overall efficacy. Our solution learns from a systems own history to establish the baseline of normal behavior. Hence it is not necessary to determine any static trigger-levels for metrics to raise alerts. Static triggers are ineffective since each system and its workloads are different from others. The method presented here (a) gives accurate indications of the time period when something goes wrong in a system, and (b) helps pin-point the most affected parts of the system to aid in diagnosis. Validation on more than 400 actual field support cases shows about 85% true positive rate with less than 10% false positive rate in identifying time periods of performance impact before or during the time a case was open. Results in a controlled lab environment are even better.