abstract

Performance sensitive self-adaptive service-oriented software using hidden Markov models (abstracts only)

Authors:

Diego Perez-Palacin,

José MerseguerAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 39, Issue 3

Page 15

https://doi.org/10.1145/2160803.2160825

Published: 21 December 2011 Publication History

Abstract

Service Oriented Architecture (SOA) is a paradigm where applications are built on services offered by third party providers. Behavior of providers evolves and makes a challenge the performance prediction of SOA applications. A proper decision about when a provider should be substituted can dramatically improve the performance of the application. We propose hidden Markov models (HMM) to help service integrators to foretell the current state of third-parties. The paper leverages different algorithms that change providers based on predictions about their states. We also integrate these algorithms and HMMs in an architectural solution to coordinate them with other challenges in the SOA world.

Performance sensitive self-adaptive service-oriented software using hidden Markov models (abstracts only)
1. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic representations
    2. Stochastic processes
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory

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Published In

cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 39, Issue 3

December 2011

163 pages

ISSN:0163-5999

DOI:10.1145/2160803

Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 December 2011

Published in SIGMETRICS Volume 39, Issue 3

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