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 E. Smirni has been partially supported by NSF grants CSR-0720699 and CCF-0811417. Author's addresses: L. Cherkasova, HP Labs, Palo Alto, CA; email: lucy.cherkasova@hp.com; K. Ozonat, HP Labs, Palo Alto, CA; email: kivanc.ozonat@hp.com; N. Mi, Northeastern University, Boston, MA; email: n.mi@neu.edu; J. Symons, HP, Cupertino, CA; email: julie.symons@hp.com; E. Smirni, College of William and Mary, Williamsburg, VA; email: esmirni@cs.wm.edu. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or permissions@acm.org. solution further enables the automation of capacity planning and resource provisioning tasks of multitier applications in rapidly evolving IT environments.
REF proposes an automated anomaly detection and performance modeling approach in large scale enterprise applications.
Automated anomaly detection and performance modeling of enterprise applications
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Computer Science