Multi-Objective Quality-Driven Service Selection-A Fully Polynomial Time Approximation Scheme

The goal of multi-objective quality-driven service selection (QDSS) is to find service selections for a workflow whose quality-of-service (QoS) values are Pareto-optimal. We consider multiple QoS attributes such as response time, cost, and reliability. A selection is Pareto-optimal if no other selec...

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Vydáno v:IEEE transactions on software engineering Ročník 40; číslo 2; s. 167 - 191
Hlavní autoři: Trummer, Immanuel, Faltings, Boi, Binder, Walter
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2014
IEEE Computer Society
Témata:
Algorithms
Approximation
Approximation algorithms
Approximation methods
Complexity
Complexity theory
Decision making models
Equivalence
Mathematical analysis
Motion pictures
multi-objective optimization
Optimization
Optimization algorithms
Pareto optimum
Permissible error
Polynomials
Quality of service
Quality-driven service selection
Randomized algorithms
Studies
Workflow software
ISSN:0098-5589, 1939-3520
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Shrnutí:The goal of multi-objective quality-driven service selection (QDSS) is to find service selections for a workflow whose quality-of-service (QoS) values are Pareto-optimal. We consider multiple QoS attributes such as response time, cost, and reliability. A selection is Pareto-optimal if no other selection has better QoS values for some attributes and at least equivalent values for all others. Exact algorithms have been proposed that find all Pareto-optimal selections. They suffer however from exponential complexity. Randomized algorithms scale well but do not offer any formal guarantees on result precision. We present the first approximation scheme for QDSS. It aims at the sweet spot between exact and randomized algorithms: It combines polynomial complexity with formal result precision guarantees. A parameter allows to seamlessly trade result precision against efficiency. We formally analyze complexity and precision guarantees and experimentally compare our algorithm against exact and randomized approaches. Comparing with exact algorithms, our approximation scheme allows to reduce optimization time from hours to seconds. Its approximation error remains below 1.4 percent while randomized algorithms come close to the theoretical maximum.
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ISSN:0098-5589
1939-3520
DOI:10.1109/TSE.2013.61