In several application contexts, Web Services adoption is limited due to performance issues. Design methods often propose the adoption of coarse-grained interfaces to reduce the number of interactions between clients and servers. This is an important design concern since marshaling and transferring small parts of complex business objects might entail sensible delays, especially in high latency networks. Nevertheless, transferring large data in coarse-grained interactions might bring useless data on the client side, whereas a small part of the transferred object is used. To reduce data transfers, in addition to well-known techniques based on XML compression, a possible approach is considering a finer granularity at data level: results produced by Web services invocations could be transferred to the client by using incremental loading. However, existing Web service technology does not provide run-time infrastructures with an adequate support for lazy serialization. In addition, pure lazy serialization could incur in high overheads due to many interactions, especially in wide area networks. This paper presents a novel approach to extend existing Web services run-time supports with dynamic offloading capabilities based on an adaptive strategy that allows servers to learn clients behaviors at runtime. By exploiting this approach, service based applications can improve their performances, as experimental results show, without any invasive change to existing Web services and clients.

International audienceImproving performance of Web services interactions is an important factor to burst the adoption of SOAin mission-critical applications, especially when they deal with large business objects whose transfer time is not negligible. Designing messages dynamic granularity (offloading) is a key challenge for achieving good performances. This requires the server being able to predict the pieces of data actually used by clients in order to send only such data. However, exact prediction is not easy, and consequently lazy interactions are needed to transfer additional data whenever the prediction fails. To preserve semantics, lazy accesses to the results of a Web service interaction need to work on a dedicated copy of the business object stored as application state. Thus, dynamic offloading can experience an overhead due to a prediction failure, which is the sum of round-trip and storage access delays, which could compromise the benefits of the technique. This paper improves our previous work enabling dynamic offloading for both IN and OUT parameters, and analyses how attributes copies impact on the technique, by comparing the overheads introduced by different storage technologies in a real implementation of a Web services framework that extends CXF. More specifically, we quantitatively characterize the execution contexts that make dynamic offloading effective, and the expected accuracy of the predictive strategy to have a gain in term of response time compared to plain services invocations. Finally, the paper introduces the Attribute Loading Delegation technique that enables optimized data-transfers for those applications where data-intensive multiple-interactions take place

In several application contexts, Web Services adoption is limited due to performance issues. Design methods and migration strategies from legacy systems often propose the adoption of coarse-grained interfaces to reduce the number of interactions between clients and servers. This is an important design concern since marshaling and transferring small parts of complex business objects might entail sensible delays, especially in high latency networks. Nevertheless, transferring large data in coarse-grained interactions might bring useless data on the client side, whereas a small part of the transferred object is actually used. This paper presents a novel approach to extend existing Web services run-time supports with dynamic offloading capabilities based on an adaptive strategy that allows servers to learn clients behaviors at runtime. By exploiting this approach, service based applications can improve their performances, as experimental results show, without any invasive change to existing Web services and clients.

Service engineering is an emerging interdisciplinary subject which crosscuts business modeling, knowledge management and economic analysis. To better satisfy service providers' profiting goals, the service system modeling needs to take care of both the short and long run customer satisfaction. We believe that the ideology of value driven design fits well for this need. We propose to work towards value driven design by introducing a form of service design patterns, we call service value broker (SVB) patterns, with the aim to shorten the distance between economical analysis and IT implementation. SVB patterns allow us to not only study the value added in terms of functional and business aspects, but also reason about the need for brokerage across various domains. In this paper, we focus on modeling the basis of SVB. The analysis is provided with a formal development of SVB to be integrated across a variety of functional and business aspects of services. Placing particular emphasis on scenarios where these can be applied, an example of improvements provided by SVB is also included.