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<p><b>Abstract</b>—Many modern networked applications require specific levels of service quality from the underlying network. Moreover, next-generation networked applications are expected to adapt to changes in the underlying network, services, and user interactions. While some applications have built-in adaptivity, the adaptation itself requires specification of a system model. This paper presents <it>Sapphire</it>, an experimental approach for systematic model generation for application adaptation within a target network. It employs a nearly-automated, statistical design of experiments to characterize the relationships of both application and network-level parameters. First, it applies the Analysis of Variance (ANOVA) method to identify the most significant parameters and their interactions that affect performance. Next, it generates a model of application performance with respect to these parameters within the ranges of measurements. The key benefit of the framework is the integration of several well-established concepts of statistical modeling and distributed systems in the form of simple APIs so that existing applications can take advantage of it. We demonstrate the usefulness and flexibility of <it>Sapphire</it> by generating a performance model of an audio streaming application. We show that many existing multimedia and QoS-sensitive applications can exploit a statistical modeling approach such as <it>Sapphire</it> to incorporate application adaptivity. The approach can also be used for feedback control of distributed applications, tuning network and application parameters to achieve service levels in a target network.</p>
Application-aware adaptation, measurements, statistical analysis, performance analysis.

A. Bose, K. G. Shin and M. El Gendy, "Sapphire: Statistical Characterization and Model-Based Adaptation of Networked Applications," in IEEE Transactions on Parallel & Distributed Systems, vol. 17, no. , pp. 1512-1525, 2006.
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