E2EProf: Automated End-to-End Performance Management for Enterprise Systems

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/14352

Title: E2EProf: Automated End-to-End Performance Management for Enterprise Systems
Author: Agarwala, Sandip ; Alegre, Fernando ; Schwan, Karsten ; Mehalingham, Jegannathan
Abstract: Distributed systems are becoming increasingly complex, caused by the prevalent use of web services, multi-tier architectures, and grid computing, where dynamic sets of components interact with each other across distributed and heterogeneous computing infrastructures. For these applications to be able to predictably and efficiently deliver services to end users, it is therefore, critical to understand and control their runtime behavior. In a datacenter environment, for instance, understanding the end-to-end dynamic behavior of certain IT subsystems, from the time requests are made to when responses are generated and finally, received, is a key prerequisite for improving application response, to provide required levels of performance, or to meet service level agreements (SLAs). The 'E2EProf' toolkit enables the efficient and non-intrusive capture and analysis of end-to-end program behavior for complex enterprise applications. E2EProf permits an enterprise to recognize and analyze performance problems when they occur -- online, to take corrective actions as soon as possible and wherever necessary along the paths currently taken by user requests -- end-to-end, and to do so without the need to instrument applications -- non-intrusively. Online analysis exploits a novel signal analysis algorithm, termed 'pathmap', which dynamically detects the causal paths taken by client requests through application and backend servers and annotates these paths with end-to-end latencies and with the contributions to these latencies from different path components. Thus, with pathmap, it is possible to dynamically identify the bottlenecks present in selected servers or services and to detect the abnormal or unusual performance behaviors indicative of potential problems or overloads. Pathmap and the E2EProf toolkit successfully detect causal request paths and associated performance bottlenecks in the RUBiS ebay-like multi-tier web application and in one of the datacenter of our industry partner, Delta Air Lines.
Type: Technical Report
URI: http://hdl.handle.net/1853/14352
Date: 2007
Relation: CERCS;GIT-CERCS-07-02
Publisher: Georgia Institute of Technology
Subject: End-to-end performance diagnosis
Online time
Series analysis

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