Dynamic object offloading in Web services

Abstract

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.

Enabling Advanced Loading Strategies for Data Intensive Web Services

“…However, we believe that NoSQL DBMSs better fit this kind of application, since they are designed for cloud-based environments and are intrinsically scalable [10]. Finally, our tests outline a lower bound in terms of N a due to the pure-lazy approach, but better performances can be easily achieved by employing a light-weight learning strategy, as shown in [3].…”

“…In [3], we have shown that dynamic object offloading can lead to a considerable performance gain, in terms of the time needed by the client to retrieve a business object, if such client exhibits a predictable behaviour. On the other hand, we have shown that a simple learning technique, as well as a random strategy, can still outperform pure-eager or pure-lazy loading, depending on object granularity, if the client randomly decides to access the generic attribute i.…”

“…To preserve the correct semantics, lazy accesses to the results of a Web service interaction need to work on dedicated copies of business object attributes that are stored on the server [3].…”

“…The LS also provides update operations, so that the remote endpoint may notify it about which attributes have been actually used and which have not. This information can be exploited to design a strategy that adapts itself to endpoints behaviour, as reported on in [3].…”

“…Once the list of the attributes to be offloaded has been obtained, the ORI serializes them to the OffloadingRepository (OR), which will make them available for lazy access (3,4). The request is then forwarded to the server (5) and handled by the IncomingRequestInterceptor (IRI), that is responsible of unwrapping it and hiding the applied loading strategy (6,7), by means of the ProxyManager (PM).…”