Julie Symons

Ensembles of models for automated diagnosis of system performance problems

Violations of service level objectives (SLO) in Internet services are urgent conditions requiring immediate attention. Previously we explored (I. Cohen et al., 2004) an approach for identifying which low-level system properties were correlated to high-level SLO violations (the metric attribution problem). The approach is based on automatically inducing models from data using pattern recognition and probability modeling techniques. In this paper we extend our approach to adapt to changing workloads and external disturbances by maintaining an ensemble of probabilistic models, adding new models when existing ones do not accurately capture current system behavior. Using realistic workloads on an implemented prototype system, we show that the ensemble of models captures the performance behavior of the system accurately under changing workloads and conditions. We fuse information from the models in the ensemble to identify likely causes of the performance problem, with results comparable to those produced by an oracle that continuously changes the model based on advance knowledge of the workload. The cost of inducing new models and managing the ensembles is negligible, making our approach both immediately practical and theoretically appealing.

Anomaly? application change? or workload change? towards automated detection of application performance anomaly and change

Automated tools for understanding application behavior and its changes during the application life-cycle are essential for many performance analysis and debugging tasks. Application performance issues have an immediate impact on customer experience and satisfaction. A sudden slowdown of enterprise-wide application can effect a large population of customers, lead to delayed projects and ultimately can result in company financial loss. We believe that online performance modeling should be a part of routine application monitoring. Early, informative warnings on significant changes in application performance should help service providers to timely identify and prevent performance problems and their negative impact on the service. We propose a novel framework for automated anomaly detection and application change analysis. It is based on integration of two complementary techniques: i) a regression-based transaction model that reflects a resource consumption model of the application, and ii) an application performance signature that provides a compact model of run-time behavior of the application. The proposed integrated framework provides a simple and powerful solution for anomaly detection and analysis of essential performance changes in application behavior. An additional benefit of the proposed approach is its simplicity: it is not intrusive and is based on monitoring data that is typically available in enterprise production environments.

Automated anomaly detection and performance modeling of enterprise applications

Automated tools for understanding application behavior and its changes during the application lifecycle are essential for many performance analysis and debugging tasks. Application performance issues have an immediate impact on customer experience and satisfaction. A sudden slowdown of enterprise-wide application can effect a large population of customers, lead to delayed projects, and ultimately can result in company financial loss. Significantly shortened time between new software releases further exacerbates the problem of thoroughly evaluating the performance of an updated application. Our thesis is that online performance modeling should be a part of routine application monitoring. Early, informative warnings on significant changes in application performance should help service providers to timely identify and prevent performance problems and their negative impact on the service. We propose a novel framework for automated anomaly detection and application change analysis. It is based on integration of two complementary techniques: (i) a regression-based transaction model that reflects a resource consumption model of the application, and (ii) an application performance signature that provides a compact model of runtime behavior of the application. The proposed integrated framework provides a simple and powerful solution for anomaly detection and analysis of essential performance changes in application behavior. An additional benefit of the proposed approach is its simplicity: It is not intrusive and is based on monitoring data that is typically available in enterprise production environments. The introduced solution further enables the automation of capacity planning and resource provisioning tasks of multitier applications in rapidly evolving IT environments.

Analysis of application performance and its change via representative application signatures

Application servers are a core component of a multi-tier architecture that has become the industry standard for building scalable client-server applications. A client communicates with a service deployed as a multi-tier application via request-reply transactions. A typical server reply consists of the Web page dynamically generated by the application server. The application server may issue multiple database calls while preparing the reply. Understanding the cascading effects of the various tasks that are sprung by a single request-reply transaction is a challenging task. Furthermore, significantly shortened time between new software releases further exacerbates the problem of thoroughly evaluating the performance of an updated application. We address the problem of efficiently diagnosing essential performance changes in application behavior in order to provide timely feedback to application designers and service providers. In this work, we propose a new approach based on an application signature that enables a quick performance comparison of the new application signature against the old one, while the application continues its execution in the production environment. The application signature is built based on new concepts that are introduced here, namely the transaction latency profiles and transaction signatures. These become instrumental for creating an application signature that accurately reflects important performance characteristics. We show that such an application signature is representative and stable under different workload characteristics. We also show that application signatures are robust as they effectively capture changes in transaction times that result from software updates. Application signatures provide a simple and powerful solution that can further be used for efficient capacity planning, anomaly detection, and provisioning of multi-tier applications in rapidly evolving IT environments.