In recent years the service-oriented design paradigm has enabled applications to be built by incorporating third party services. With the increasing popularity of this new paradigm, many companies and organizations have started to adopt this technology, which has resulted in an increase of the number and variety of third party providers. With the vast improvement of global networking infrastructure, a large number of providers offer their services for worldwide clients. As a result, clients are often presented with a number of providers that offer services with the same or similar functionalities, but differ in terms of non-functional attributes (or Quality of Service – QoS), such as performance. In this environment, the role of provider recommendation has become more important - in assisting clients in choosing the provider that meets their QoS requirement. / In this thesis we focus on provider recommendation based on one of the most important QoS attributes – performance. Specifically, we investigate client-perceived performance, which is the application-level performance measured at the client-side every time the client invokes the service. This performance metric has the advantage of accurately representing client experience, compared to the widely used server-side metrics in the current frameworks (e.g. Service Level Agreement or SLA in Web Services context). As a result, provider recommendation based on this metric will be favourable from the client’s point of view. / In this thesis we address two key research challenges related to provider recommendation based on client-perceived performance - performance assessment and performance prediction. We begin by identifying heterogeneity factors that affect client-perceived performance among clients in a global Internet environment. We then perform extensive real-world experiments to evaluate the significance of each factor to the client-perceived performance. / From our finding on heterogeneity factors, we then develop a performance estimation technique to address performance assessment for cases where direct measurements are unavailable. This technique is based on the generalization concept, i.e. estimating performance based on the measurement gathered by similar clients. A two-stage grouping scheme based on the heterogeneity factors we identified earlier is proposed to address the problem of determining client similarity. We then develop an estimation algorithm and validate it using synthetic data, as well as real world datasets. / With regard to performance prediction, we focus on the medium-term prediction aspect to address the needs of the emerging technology requirements: distinguishing providers based on medium-term (e.g. one to seven days) performance. Such applications are found when the providers require subscription from their clients to access the service. Another situation where the medium-term prediction is important is in temporal-aware selection: the providers need to be differentiated, based on the expected performance of a particular time interval (e.g. during business hours). We investigate the applicability of classical time series prediction methods: ARIMA and exponential smoothing, as well as their seasonal counterparts – seasonal ARIMA and Holt-Winters. Our results show that these existing models lack the ability to capture the important characteristics of client-perceived performance, thus producing poor medium-term prediction. We then develop a medium-term prediction method that is specifically designed to account for the key characteristics of a client-perceived performance series, and to show that our prediction methods produce higher accuracy for medium-term prediction compared to the existing methods. / In order to demonstrate the applicability of our solution in practice, we developed a provider recommendation framework based on client-perceived performance (named PROPPER), which utilizes our findings on performance assessment and prediction. We formulated the recommendation algorithm and evaluated it through a mirror selection case study. It is shown that our framework produces better outcomes in most cases, compared to country-based or geographic distance-based selection schemes, which are the current approach of mirror selection nowadays.
| Identifer | oai:union.ndltd.org:ADTP/269986 |
| Date | January 2009 |
| Creators | Thio, Niko |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
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