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Prediction of Quality of Service of Software Applications

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Advances in Service-Oriented and Cloud Computing (ESOCC 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 707))

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Abstract

The ability to a priori predict the Quality of Service (QoS) of a software application is crucial both in the design of applications and in the definition of their Service Level Agreements (SLA). QoS prediction is challenging because of the different possible results of service invocations, and of the nondeterminism, correlations and complex dependencies among activities.

In this research we present a technique to probabilistically predict the QoS of service based and parallel design patterns based applications by applying Monte Carlo simulations to a simple representation of the control-flow of the applications. A proof-of-concept implementation of the analyses is discussed along with future work.

Supervisor: Antonio Brogi, Department of Computer Science, University of Pisa, Italy.

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Notes

  1. 1.

    The source code of PASO is available at https://github.com/upi-bpel/paso.

  2. 2.

    PASO is able to analyse a subset of WS-BPEL structural (sequence, flow, if, while, scope, and faultHandlers) and basic (invoke, assign, receive, reply) activities.

  3. 3.

    The source code of PASA is available at https://github.com/ahmad1245/PASA.

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Acknowledgments

This work was partly supported by the project Through the Fog (PRA_2016_64) funded by the University of Pisa.

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Authors and Affiliations

  1. Department of Computer Science, University of Pisa, Pisa, Italy

    Ahmad Ibrahim

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  1. Ahmad Ibrahim
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Correspondence to Ahmad Ibrahim .

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Editors and Affiliations

  1. University of Groningen, Groningen, The Netherlands

    Alexander Lazovik

  2. Vienna University of Technology, Vienna, Austria

    Stefan Schulte

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Ibrahim, A. (2018). Prediction of Quality of Service of Software Applications. In: Lazovik, A., Schulte, S. (eds) Advances in Service-Oriented and Cloud Computing. ESOCC 2016. Communications in Computer and Information Science, vol 707. Springer, Cham. https://doi.org/10.1007/978-3-319-72125-5_21

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  • DOI: https://doi.org/10.1007/978-3-319-72125-5_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72124-8

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