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DBLP
Doctoral thesis
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Reasoning-based Capability Configuration Management in Adaptable Service Systems.

Authors: Supadulchai, Paramai;

Reasoning-based Capability Configuration Management in Adaptable Service Systems.

Abstract

Networked Service Systems are considered. Services are realized by service components, which by their inter-working, provide a service in the role of a service provider to service users. During more than two decades, networked service systems have been an important research topic. Focus was on efficiency in the definition, deployment and execution of services. This focus now has been changed into adaptability. Adaptable service systems are service systems that are able to adapt dynamically to changes in time and position related to users, nodes, capabilities, system performance, changed service requirements and policies. This thesis has focus on adaptability aspects related to capabilities. A capability is defined here as an inherent property of a node, which is used as a basis for implementing a service. Goal adaptability properties of an adaptable service system can be classified as general and core properties. The general properties are requirement to the architectural framework while the core properties are requirement to the functionality. The core properties are classified as i) re-arrangement flexibility, ii) failure robustness and iii) resource load awareness and control properties. The realization of the goal adaptability properties both needs an appropriate architectural framework as well as management functionality. This thesis presents a solution framework for reasoning-based capability configuration management for adaptable service systems. This framework is defined to consist of five contributions. Each contribution consists of sub-contributions; each of which represents contributed concept, model or mechanism. The contributions are: • C1: Capability-based computing architecture • C2: Policy-based reasoning • C3: Capability configuration management • C4: Concept model and data representation • C5: Scenarios - experimentation and simulation Capability-based computing architecture is a capability and QoS-based architectural framework intended to be used for the specification and execution of any service functionality. Policy-based reasoning is a support functionality that makes adaptable service systems being able to take decisions based on flexible and expressive behavioral specification. Capability configuration management is a functionality related to capability specifications, configuration, allocation, re-allocation and optimization. Concept model and data representation is the data model applied for the formalization and representation of the concepts applied for the capability configuration management based on policy-based reasoning. Scenarios – experimentation and simulation shows the experiments and simulations that have been conducted for validating the other contributions. My PhD work and thesis is related to TAPAS (Telematics Architecture for Play-based Adaptable Service Systems). This thesis is structured into two main parts: Part I – Introduction and Part II – Selected publications. Part I is intended for the reader to get an overview of the publications included in Part II.

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green