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Title: Efficient adaptive multi-granularity service composition
Author: Barakat, Lina
Awarding Body: King's College London (University of London)
Current Institution: King's College London (University of London)
Date of Award: 2013
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Despite the tremendous benefits of the dynamic, service-oriented approach to build composite applications, it also brings great challenges. In particular, the run-time selection of the most suitable services for an application in a timely manner is not trivial, since many providers could be competing for the same type of service, but at different quality of service levels. Due to possible dependencies among services, such quality offerings could also vary at a single service level. This is further complicated by the fact that the available services may offer to achieve the tasks required at varying functional abstractions. Moreover, services are highly dynamic and unreliable in nature, which can cause serious problems to the execution of workflows relying on such services. In this thesis, we contribute towards addressing these challenges, and achieve a more efficient, robust, and optimal dynamic composition process. Specifically, through a rich collection of alternative planning options, we allow services at various granularity levels to be incorporated into the selection process. We also enrich the quality model of services with inter-service dependency awareness, to produce correct quality estimations. Furthermore, we develop efficiency-boosting techniques facilitating a scalable service selection process without affecting optimality, even in the case where the search space experiences complex dependencies among services. In the face of environment dynamism and uncertainty, we achieve an early and efficient adaptive behaviour, which ensures a valid, optimal, and satisfactory solution, in spite of high environment volatility, and without causing disruption to application execution. The effectiveness of all the algorithms and techniques developed in this thesis is demonstrated analytically and empirically. The latter is achieved both on randomly generated datasets, and through a case study evaluation applied in the context of learning object composition.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available