Details
| Original language | English |
|---|---|
| Title of host publication | Service-Oriented Computing |
| Subtitle of host publication | ICSOC 2008 Workshops - ICSOC 2008 International Workshops, Revised Selected Papers |
| Pages | 190-199 |
| Number of pages | 10 |
| ISBN (electronic) | 978-3-642-01247-1 |
| Publication status | Published - 2009 |
| Event | International Conference on Service-Oriented Computing, ICSOC 2008 - Sydney, NSW, Australia Duration: 1 Dec 2008 → 1 Dec 2008 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 5472 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
QoS-based service selection aims at finding the best component services that satisfy the end-to-end quality requirements. The problem can be modeled as a multi-dimension multi-choice 0-1 knapsack problem, which is known as NP-hard. Recently published solutions propose using linear programming techniques to solve the problem. However, the poor scalability of linear program solving methods restricts their applicability to small-size problems and renders them inappropriate for dynamic applications with run-time requirements. In this paper, we address this problem and propose a scalable QoS computation approach based on a heuristic algorithm, which decomposes the optimization problem into small sub-problems that can be solved more efficiently than the original problem. Experimental evaluations show that near-to-optimal solutions can be found using our algorithm much faster than using linear programming methods.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
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Service-Oriented Computing: ICSOC 2008 Workshops - ICSOC 2008 International Workshops, Revised Selected Papers. 2009. p. 190-199 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5472 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A scalable approach for QoS-based web service selection
AU - Alrifai, Mohammad
AU - Risse, Thomas
AU - Dolog, Peter
AU - Nejdl, Wolfgang
PY - 2009
Y1 - 2009
N2 - QoS-based service selection aims at finding the best component services that satisfy the end-to-end quality requirements. The problem can be modeled as a multi-dimension multi-choice 0-1 knapsack problem, which is known as NP-hard. Recently published solutions propose using linear programming techniques to solve the problem. However, the poor scalability of linear program solving methods restricts their applicability to small-size problems and renders them inappropriate for dynamic applications with run-time requirements. In this paper, we address this problem and propose a scalable QoS computation approach based on a heuristic algorithm, which decomposes the optimization problem into small sub-problems that can be solved more efficiently than the original problem. Experimental evaluations show that near-to-optimal solutions can be found using our algorithm much faster than using linear programming methods.
AB - QoS-based service selection aims at finding the best component services that satisfy the end-to-end quality requirements. The problem can be modeled as a multi-dimension multi-choice 0-1 knapsack problem, which is known as NP-hard. Recently published solutions propose using linear programming techniques to solve the problem. However, the poor scalability of linear program solving methods restricts their applicability to small-size problems and renders them inappropriate for dynamic applications with run-time requirements. In this paper, we address this problem and propose a scalable QoS computation approach based on a heuristic algorithm, which decomposes the optimization problem into small sub-problems that can be solved more efficiently than the original problem. Experimental evaluations show that near-to-optimal solutions can be found using our algorithm much faster than using linear programming methods.
UR - http://www.scopus.com/inward/record.url?scp=70350623226&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-01247-1_20
DO - 10.1007/978-3-642-01247-1_20
M3 - Conference contribution
AN - SCOPUS:70350623226
SN - 978-3-642-01246-4
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 190
EP - 199
BT - Service-Oriented Computing
T2 - International Conference on Service-Oriented Computing, ICSOC 2008
Y2 - 1 December 2008 through 1 December 2008
ER -