基于分布式哈希表的协作式Web服务组合

摘要

集中式的基于案例推理(CBR)应用于感知服务质量(QoS)的Web服务组合时,面临信息维护量大、节点负载高、中心失效等问题.为解决上述问题,提出了基于分布式哈希表(DHT)的协作式Web服务组合方法COCO,利用哈希函数和空间填充曲线(SFC)将组合服务的工作流和服务质量映射为一维数据键,并利用底层DHT Overlay以Peer-to-Peer的方式查询满足用户请求的已知组合服务,一次成功查询可获得同时满足功能性要求和非功能性要求的组合服务.实验结果表明,COCO在查询时延和查询命中率方面均有较好性能,适用于大规模网络计算环境.%Centralized CBR (Case-Based Reasoning) is faced with problems such as massive data maintenance cost, high node load and central failure. To tackle these problems, COCO (Distributed Hash Table (DHT) based Collaborative Web Service Composition) was proposed. COCO mappped the workflow and QoS properties of a composite service into a one-dimensional key using Hash function and Space-Filling Curve (SFC). Keys were used for queries of the existing composite services that satisfied user request in a Peer-to-Peer (P2P) fashion in the underlying DHT Overlay. One successful query returned composite services satisfying both functional and non-functional requirements. The experimental results show that COCO has scalable performance on both query delay and query hit rate, indicating COCO's applicability in large-scale network computing environment.