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On the Scalability of Compositions of Service-Oriented Applications

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 13121)


One of the current challenges in the context of service-oriented applications is the generation of composition plans for applications that optimize their QoS attributes by taking advantage of the resources offered by different providers, and generate them as efficiently as possible. Scalability is indeed a major issue, and the problem becomes a real challenge for applications with big numbers of services. In this work, we propose a divide-and-conquer algorithm that exploits the architecture of applications, to reduce the size of the search space. With it, we are able to recompose the solution for the global problem, with a significant gain in execution time. A variant of the algorithm—in which, when a sub-problem cannot be further divided without loosing information, a solver is used to find the optimal solution for it—allows us to trade execution time and precision. We report on the extensive experimentation carried out, where applications with up to \(2\,000\) services are considered, and which includes a comparison with the results delivered by GA solvers.

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  1. 1.

    The GA solver used in this paper is implemented using the Jenetics library ( The hyper-parameters used in our experiments can be found in Appendix A. The Jenetics library offers two stop conditions: a hard timeout can be given, but also a convergence criteria can be provided so that the evolution stops when the fitness is deemed as converged.

  2. 2.

    Each of the experiments in this paper has been executed 10 times, and averages are shown to make the graphs smoother.


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This work has been partially supported by projects UMA-CEIATECH-09 (Andalucía TECH/J. Andalucía/FEDER), UMA18-FEDERJA-180 (J. Andalucía/FEDER), PGC2018-094905-B-I00 (Spanish MINECO/FEDER).

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Correspondence to Francisco Durán .

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A GA settings

A GA settings

Hyper-parameters. The hyper-parameters with which experiments in this paper have been performed are as follows:

  • Population: 100.

  • Probability to mutate any chromosome: \(13\%\).

  • Crossover probability: \(70\%\), which can affect a total of 5 different chromosomes at once (Multi-point crossover of 5 used).

  • Survivor selector: Elitist of 2 individuals. This feature assumes some risk, as it is possible to drag individuals which are local minimums during the GA execution.

  • OffSpring selector: The individuals chosen to create offspring will use the RouletteWheelSelector method, meaning that the probability that an individual will be chosen to generate offspring will be \(P(i) = \frac{f_{i}}{\sum _{j = 0}^{N-1} f_{j}}\), where N is the total population.

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Pozas, N., Durán, F. (2021). On the Scalability of Compositions of Service-Oriented Applications. In: Hacid, H., Kao, O., Mecella, M., Moha, N., Paik, Hy. (eds) Service-Oriented Computing. ICSOC 2021. Lecture Notes in Computer Science(), vol 13121. Springer, Cham.

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