Metaheuristic Optimization of Large-Scale QoS-aware Service Compositions

Rosenberg, Florian; Müller, Max Benjamin; Leitner, Philipp; Michlmayr, Anton; Bouguettaya, Athman; Dustdar, Schahram

doi:10.1109/scc.2010.58

Cited by 54 publications

(27 citation statements)

References 24 publications

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“…This approach can also be considered a heuristic, since the combination with local selection does not guarantee a globally optimal solution [29]. Most comparably to our work, the authors of [30] use a genetic algorithm combined with local search to efficiently solve the QoS optimization problem. The main difference of our work to all these approaches is that we do not optimize the composition with regard to global QoS goals.…”

Section: Related Workmentioning

confidence: 93%

Cost-Based Optimization of Service Compositions

Leitner

Hummer

Dustdar

2013

IEEE Trans. Serv. Comput.

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For providers of composite services preventing cases of SLA violations is crucial. Previous work has established runtime adaptation of compositions as a promising tool to achieve SLA conformance. However, in order to get a realistic and complete view of the decision process of service providers, the costs of adaptation need to be taken into account. In this paper we formalize the problem of finding the optimal set of adaptations, which minimizes the total costs arising from SLA violations and the adaptations to prevent them. We present possible algorithms to solve this complex optimization problem, and detail an end-to-end system based on our earlier work on the PREvent (prediction and prevention based on event monitoring) framework, which clearly indicates the usefulness of our model. We discuss experimental results that show how the application of our approach leads to reduced costs for the service provider, and explain the circumstances in which different algorithms lead to more or less satisfactory results. Abstract-For providers of composite services preventing cases of SLA violations is crucial. Previous work has established runtime adaptation of compositions as a promising tool to achieve SLA conformance. However, in order to get a realistic and complete view of the decision process of service providers, the costs of adaptation need to be taken into account. In this paper we formalize the problem of finding the optimal set of adaptations, which minimizes the total costs arising from SLA violations and the adaptations to prevent them. We present possible algorithms to solve this complex optimization problem, and detail an end-to-end system based on our earlier work on the PREvent (prediction and prevention based on event monitoring) framework, which clearly indicates the usefulness of our model. We discuss experimental results that show how the application of our approach leads to reduced costs for the service provider, and explain the circumstances in which different algorithms lead to more or less satisfactory results.

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Section: Related Workmentioning

confidence: 93%

Cost-Based Optimization of Service Compositions

Leitner

Hummer

Dustdar

2013

IEEE Trans. Serv. Comput.

Self Cite

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“…A summary of the solution-specific aspects that are extracted from the set of papers included in the literature Performance (84) ES(32) [32], [45], [67]-COST(1) [33], GENERAL(10) [71], [79], [86], [113], [138], [139], [145], [205], [224], [243], MAPPING(3) [5], [95], [153], MEMORY(2) [5], [153], NOT PRE-SENTED(7) [21], [36], [91], [162], [202], [219], [242], PER-FORMANCE(1) [33], STRUCTURAL(1) [150] Energy(18) ES(17) [17], [28], [67], [68], [72], [84], [116], [153], [155], [165], [188], [193], [194], [211], [212], [237], [240], GENERAL(1) [206] DT(9) [67...…”

Section: Solutionmentioning

confidence: 99%

“…[5], [33], [95], [145], [224], NMF(1) [79], NOT PRESENTED(4) [36], [91], [242], [243], SAF(10) [21], [71], [86], [113], [150], [153], [162], [202], [205], [219] ALLOCATION(7) [5], [21], [33], [95], [145], [150], [153], COMPONENT SELECTION(1) [36], GENERAL(5) [79], [138], [139], [205], [224], HARDWARE REPLICATION(1) [95], HARD-WARE SELECTION(3) [33], [145], [150], NOT PRESENTED(1) [91], OTHER PROBLEM SPECIFIC(2) [71], [113], SCHEDULING(1) [33], SERVICE COMPOSITION(2) [202], [219], SERVICE SELECTION(6) [113], [162], [202], [219], [242], …”

Section: Mb(5)mentioning

confidence: 99%

Software Architecture Optimization Methods: A Systematic Literature Review

Aleti

Bühnová

Grunske

et al. 2013

IIEEE Trans. Software Eng.

244

146

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Due to significant industrial demands toward software systems with increasing complexity and challenging quality requirements, software architecture design has become an important development activity and the research domain is rapidly evolving. In the last decades, software architecture optimization methods, which aim to automate the search for an optimal architecture design with respect to a (set of) quality attribute(s), have proliferated. However, the reported results are fragmented over different research communities, multiple system domains, and multiple quality attributes. To integrate the existing research results, we have performed a systematic literature review and analyzed the results of 188 research papers from the different research communities. Based on this survey, a taxonomy has been created which is used to classify the existing research. Abstract-Due to significant industrial demands toward software systems with increasing complexity and challenging quality requirements, software architecture design has become an important development activity and the research domain is rapidly evolving. In the last decades, software architecture optimization methods, which aim to automate the search for an optimal architecture design with respect to a (set of) quality attribute(s), have proliferated. However, the reported results are fragmented over different research communities, multiple system domains, and multiple quality attributes. To integrate the existing research results, we have performed a systematic literature review and analyzed the results of 188 research papers from the different research communities. Based on this survey, a taxonomy has been created which is used to classify the existing research. Furthermore, the systematic analysis of the research literature provided in this review aims to help the research community in consolidating the existing research efforts and deriving a research agenda for future developments.

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“…[21] was the first effort to address the lower cost data-intensive service composition problem. Also, there is ample evidence regarding the applicability of genetic algorithms for large-scale optimization problems [13], [23] and service composition in cloud computing [25]. In order to deal with the dynamic changes of services and network conditions in cloud computing, as well as the constraints of different users and the flexibility of the selection criteria, a modified genetic algorithm-based dataintensive service composition approach is proposed in this paper.…”

Section: Related Workmentioning

confidence: 99%

An Improved Genetic Algorithm for Cost-Effective Data-Intensive Service Composition

Wang

Shen

Luo³

et al. 2013

2013 Ninth International Conference on Semantics, Knowledge and Grids

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The explosion of digital data and the dependence on data-intensive services have been recognized as the most significant characteristics of IT trends in the current decade. Designing workflow of data-intensive services requires data analysis from multiple sources to get required composite services. Composing such services requires effective transfer of large data. Thus many new challenges are posed to control the cost and revenue of the whole composition. This paper addresses the data-intensive service composition and presents an innovative data-intensive service selection algorithm based on a modified genetic algorithm. The performance of this new algorithm is also tested by simulations and compared against other traditional approaches, such as mix integer programming. The contributions of this paper are three folds: 1) An economical model for dataintensive service provision is proposed, 2) An extensible QoS model is also proposed to calculate the QoS values of data-intensive services, 3) Finally, a modified genetic algorithm-based approach is introduced to compose data-intensive services. A local selection method with modifications of crossover and mutation operators is adopted for this algorithm. The results of experiments will demonstrate the scalability and effectiveness of our proposed algorithm. Abstract-The explosion of digital data and the dependence on data-intensive services have been recognized as the most significant characteristics of IT trends in the current decade. Designing workflow of data-intensive services requires data analysis from multiple sources to get required composite services. Composing such services requires effective transfer of large data. Thus many new challenges are posed to control the cost and revenue of the whole composition. This paper addresses the data-intensive service composition and presents an innovative data-intensive service selection algorithm based on a modified genetic algorithm. The performance of this new algorithm is also tested by simulations and compared against other traditional approaches, such as mix integer programming. The contributions of this paper are three folds: 1) An economical model for data-intensive service provision is proposed; 2) An extensible QoS model is also proposed to calculate the QoS values of data-intensive services; 3) Finally, a modified genetic algorithmbased approach is introduced to compose data-intensive services. A local selection method with modifications of crossover and mutation operators is adopted for this algorithm. The results of experiments will demonstrate the scalability and effectiveness of our proposed algorithm.

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Metaheuristic Optimization of Large-Scale QoS-aware Service Compositions

Cited by 54 publications

References 24 publications

Cost-Based Optimization of Service Compositions

Cost-Based Optimization of Service Compositions

Software Architecture Optimization Methods: A Systematic Literature Review

An Improved Genetic Algorithm for Cost-Effective Data-Intensive Service Composition

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