QoS-Aware Automatic Service Composition: A Graph View
QoS-Aware Automatic Service Composition: A Graph View
复制标题
QoS 感知自动服务组合：图形视图
DOI：
10.1007/s11390-011-0183-2
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发表时间：
2011-09
影响因子：
0.7
通讯作者：
Liu, Zhi-Yong
中科院分区：
文献类型：
--
作者：
Jiang, Wei;Wu, Tian;Hu, Song-Lin;Liu, Zhi-Yong
In the research of service composition, it demands efficient algorithms that not only retrieve correct service compositions automatically from thousands of services but also satisfy the quality requirements of different service users. However, most approaches treat these two aspects as two separate problems, automatic service composition and service selection. Although the latest researches realize the restriction of this separate view and some specific methods are proposed, they still suffer from serious limitations in scalability and accuracy when addressing both requirements simultaneously. In order to cope with these limitations and efficiently solve the combined problem which is known as QoS-aware or QoS-driven automatic service composition problem, we propose a new graph search problem, single-source optimal directed acyclic graphs (DAGs), for the first time. This novel single-source optimal DAGs (SSOD) problem is similar to, but more general than the classical single-source shortest paths (SSSP) problem. In this paper, a new graph model of SSOD problem is proposed and a Sim-Dijkstra algorithm is presented to address the SSOD problem with the time complexity of O(n log n + m) (n and m are the number of nodes and edges in the graph respectively), and the proofs of its soundness. It is also directly applied to solve the QoS-aware automatic service composition problem, and a service composition tool named QSynth is implemented. Evaluations show that Sim-Dijkstra algorithm achieves superior scalability and efficiency with respect to a large variety of composition scenarios, even more efficient than our worklist algorithm that won the performance championship of Web Services Challenge 2009.
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DOI：
10.1109/cec.2009.27
发表时间：
2009-07
期刊：
2009 IEEE Conference on Commerce and Enterprise Computing
影响因子：
--
作者：
Peter Bartalos;M. Bieliková
通讯作者：
Peter Bartalos;M. Bieliková
DOI：
10.1109/aiworc.2000.843314
发表时间：
2000-04
期刊：
Proceedings Academia/Industry Working Conference on Research Challenges '00. Next Generation Enterprises: Virtual Organizations and Mobile/Pervasive Technologies. AIWORC'00. (Cat. No.PR00628)
影响因子：
--
作者：
通讯作者：
--
DOI：
--
发表时间：
2001
期刊：
--
影响因子：
--
作者：
T. Cormen;C. Leiserson;R. Rivest;C. Stein
通讯作者：
T. Cormen;C. Leiserson;R. Rivest;C. Stein
影响因子：
4.9
作者：
M. Paolucci;Takahiro Kawamura;T. Payne;K. Sycara
通讯作者：
M. Paolucci;Takahiro Kawamura;T. Payne;K. Sycara
DOI：
10.1145/988672.988747
发表时间：
2004-05
期刊：
--
影响因子：
--
作者：
Abhijit A. Patil;Swapna A. Oundhakar;A. Sheth;Kunal Verma
通讯作者：
Abhijit A. Patil;Swapna A. Oundhakar;A. Sheth;Kunal Verma