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SOA Performance Enhancement Through XML Fragment Caching
DOI:10.1287/isre.1110.0368.png)
摘要
En 中文
Organizations are increasingly choosing to implement service-oriented architectures to integrate distributed, loosely coupled applications. These architectures are implemented as services, which typically use XML-based messaging to communicate between service consumers and service providers across enterprise networks. We propose a scheme for caching fragments of service response messages to improve performance and service quality in service-oriented architectures. In our fragment caching scheme, we decompose responses into smaller fragments such that reusable components can be identified and cached in the XML routers of an XML overlay network within an enterprise network. Such caching mitigates processing requirements on providers and moves content closer to users, thus reducing bandwidth requirements on the network as well as improving Service times. We describe the system architecture and caching algorithm details for our caching scheme, develop an analysis of the expected benefits of our scheme, and present the results of both simulation and case study-based experiments to show the validity and performance improvements provided by our caching scheme. Our simulation experimental results show an up to 60% reduction in bandwidth consumption and up to 50% response time improvement. Further, our case study experiments demonstrate that when there is no resource bottleneck, the cache-enabled case reduces average response times by 40%-50% and increases throughput by 150% compared to the no-cache and full message caching cases. In experiments contrasting fragment caching and full message caching, we found that full message caching provides benefits when the number of possible unique responses is low while the benefits of fragment caching increase as the number of possible unique responses increases. These experimental results clearly demonstrate the benefits of our approach.
Keyword:
caching
XML
SOA
service-oriented architecture
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期刊
IF:
5.1
论文数:
1.4K
被引数:
1.4W
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