Kargupta, S; (2014) A Proactive Approach to Application Performance Analysis, Forecast and Fine-Tuning. Doctoral thesis , UCL (University College London).

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Abstract

A major challenge currently faced by the IT industry is the cost, time and resource associated with repetitive performance testing when existing applications undergo evolution. IT organizations are under pressure to reduce the cost of testing, especially given its high percentage of the overall costs of application portfolio management. Previously, to analyse application performance, researchers have proposed techniques requiring complex performance models, non-standard modelling formalisms, use of process algebras or complex mathematical analysis. In Continuous Performance Management (CPM), automated load testing is invoked during the Continuous Integration (CI) process after a build. CPM is reactive and raises alarms when performance metrics are violated. The CI process is repeated until performance is acceptable. Previous and current work is yet to address the need of an approach to allow software developers proactively target a specified performance level while modifying existing applications instead of reacting to the performance test results after code modification and build. There is thus a strong need for an approach which does not require repetitive performance testing, resource intensive application profilers, complex software performance models or additional quality assurance experts. We propose to fill this gap with an innovative relational model associating the operation‟s Performance with two novel concepts – the operation‟s Admittance and Load Potential. To address changes to a single type or multiple types of processing activities of an application operation, we present two bi-directional methods, both of which in turn use the relational model. From annotations of Delay Points within the code, the methods allow software developers to either fine-tune the operation‟s algorithm “targeting” a specified performance level in a bottom-up way or to predict the operation‟s performance due to code changes in a top-down way under a given workload. The methods do not need complex performance models or expensive performance testing of the whole application. We validate our model on a realistic experimentation framework. Our results indicate that it is possible to characterize an application Performance as a function of its Admittance and Load Potential and that the application Admittance can be characterized as a function of the latency of its Delay Points. Applying this method to complex large-scale systems has the potential to significantly reduce the cost of performance testing during system maintenance and evolution.

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