Title
From Local Impact Functions to Global Adaptation of Service Compositions
Abstract
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.
Year
DOI
Venue
2009
10.1007/978-3-642-05118-0_41
SSS
Keywords
Field
DocType
available adaptation,adaptation target,local impact functions,complex software system,unpredictable execution environment,general framework,prototype framework,global adaptation,service adaptation,service compositions,customizable system,performance indicator,execution context,web based applications,computer science,software systems
Performance indicator,Computer science,Software system,Service composition,Decision process,Dynamic web page,Distributed computing
Conference
Volume
ISSN
Citations 
5873
0302-9743
3
PageRank 
References 
Authors
0.45
16
5
Name
Order
Citations
PageRank
Liliana Rosa130.45
Luís Rodrigues2528.02
Antónia Lopes369752.57
Matti Hiltunen429917.56
Schlichting, R.52234372.48