Title
An Efficient Service Discovery Algorithm for Counting Bloom Filter-Based Service Registry
Abstract
The Service registry, the yellow pages of Service-Oriented Architecture (SOA), plays a central role in SOA-based service systems. The service registry has to be scalable to manage large number of services along with their requirements on storage and discovery. Based on our previous work on feature-based services quantification, we characterize services according to their diverse functional and non-functional requirements, and represent them as string formats which can be stored, probed, and indexed by efficient data structures, such as hash table and Bloom filter. Then, we propose a comprehensive service-storage solution using the counting Bloom filter (CBF). The application of CBF enables us to structure candidate services into separate groups, resulting in an accelerated services discovery process. The contributions of this research work include a new approach to manage large number of services based on quantified service features, and a storage architecture design to support service discovery. Experimental results strongly support these claims.
Year
DOI
Venue
2009
10.1109/ICWS.2009.121
ICWS
Keywords
Field
DocType
previous work,web services,service discovery,data structures,soa-based service system,service discovery algorithm,service storage solution,service feature,large number,bloom filter-based service registry,counting bloom filter,service-oriented architecture,hash table,bloom filter,accelerated services discovery process,structure candidate service,feature-based services quantification,service registry,quantified service feature,efficient service discovery algorithm,service system,service oriented architecture,acceleration,radiation detectors,silicon,data structure,data mining,non functional requirement,indexation,business
Data structure,Bloom filter,Data mining,Computer science,Web service,Service discovery,Business process discovery,Database,Service-oriented architecture,Scalability,Hash table
Conference
ISBN
Citations 
PageRank 
978-0-7695-3709-2
10
0.58
References 
Authors
10
3
Name
Order
Citations
PageRank
Shuxing Cheng1434.11
Carl K. Chang21229137.07
Liang-Jie Zhang3982138.17