From Local Impact Functions to Global Adaptation of Service Compositions

  • Liliana Rosa
  • Luís Rodrigues
  • Antónia Lopes
  • Matti Hiltunen
  • Richard Schlichting
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5873)


The problem of self-optimization and adaptation in the context of customizable systems is becoming increasingly important with the emergence of complex software systems and unpredictable execution environments. Here, a general framework for automatically deciding on when and how to adapt a system whenever it deviates from the desired behavior is presented. In this framework, the adaptation targets of the system are described in terms of a high-level policy that establishes goals for a set of performance indicators. The decision process is based on information provided independently for each service that describes the available adaptations, their impact on performance indicators, and any limitations or requirements. The technique consists of both offline and online phases. Offline, rules are generated specifying service adaptations that may help to achieve the specified goals when a given change in the execution context occurs. Online, the corresponding rule is evaluated when a change occurs to choose which adaptations to perform. Experimental results using a prototype framework in the context of a web-based application demonstrate the effectiveness of this approach.


Service Composition Adaptation Policy Global Adaptation Dynamic Content Impact Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Liliana Rosa
    • 1
  • Luís Rodrigues
    • 1
  • Antónia Lopes
    • 2
  • Matti Hiltunen
    • 3
  • Richard Schlichting
    • 3
  1. 1.INESC-ID/IST 
  2. 2.Faculty of SciencesUniversity of Lisbon 
  3. 3.AT&T Labs Research 

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