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Service recommendation and selection in centralized and decentralized environments.With the increasing use of web services in everyday tasks we are entering an era of Internet of Services (IoS). Service discovery and selection in both centralized and decentralized environments have become a critical issue in the area of web services, in particular when services having similar functionality but different Quality of Service (QoS). As a result, selecting a high quality service that best suits consumer requirements from a large list of functionally equivalent services is a challenging task. In response to increasing numbers of services in the discovery and selection process, there is a corresponding increase of service consumers and a consequent diversity in Quality of Service (QoS) available. Increases in both sides leads to a diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. Furthermore, it is challenging for customers to select suitable services from a large number of services that satisfy consumer functional requirements. Therefore, web service recommendation becomes an attractive solution to provide recommended services to consumers which can satisfy their requirements.In this thesis, first a service ranking and selection algorithm is proposed by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. With the initial list of available services the approach considers those services with a partial match of consumer requirements and ranks them based on the QoS parameters, this allows the consumer to select suitable service. In addition, providing weight value for QoS parameters might not be an easy and understandable task for consumers, as a result an automatic weight calculation method has been included for consumer requirements by utilizing distance correlation between QoS parameters. The second aspect of the work in the thesis is the process of QoS based web service recommendation. With an increasing number of web services having similar functionality, it is challenging for service consumers to find out suitable web services that meet their requirements. We propose a personalised service recommendation method using the LDA topic model, which extracts latent interests of consumers and latent topics of services in the form of probability distribution. In addition, the proposed method is able to improve the accuracy of prediction of QoS properties by considering the correlation between neighbouring services and return a list of recommended services that best satisfy consumer requirements. The third part of the thesis concerns providing service discovery and selection in a decentralized environment. Service discovery approaches are often supported by centralized repositories that could suffer from single point failure, performance bottleneck, and scalability issues in large scale systems. To address these issues, we propose a context-aware service discovery and selection approach in a decentralized peer-to-peer environment. In the approach homophily similarity was used for bootstrapping and distribution of nodes. The discovery process is based on the similarity of nodes and previous interaction and behaviour of the nodes, which will help the discovery process in a dynamic environment. Our approach is not only considering service discovery, but also the selection of suitable web service by taking into account the QoS properties of the web services. The major contribution of the thesis is providing a comprehensive QoS based service recommendation and selection in centralized and decentralized environments. With the proposed approach consumers will be able to select suitable service based on their requirements. Experimental results on real world service datasets showed that proposed approaches achieved better performance and efficiency in recommendation and selection process.
Towards an efficient indexing and searching model for service discovery in a decentralised environment.Given the growth and outreach of new information, communication, computing and electronic technologies in various dimensions, the amount of data has explosively increased in the recent years. Centralised systems suffer some limitations to dealing with this issue due to all data is stored in central data centres. Thus, decentralised systems are getting more attention and increasing in popularity. Moreover, efficient service discovery mechanisms have naturally become an essential component in both large-scale and small-scale decentralised systems and. This research study is aimed at modelling a novel efficient indexing and searching model for service discovery in decentralised environments comprising numerous repositories with massive stored services. The main contributions of this research study can be summarised in three components: a novel distributed multilevel indexing model, an optimised searching algorithm and a new simulation environment. Indexing model has been widely used for efficient service discovery. For instance; the inverted index is one of the popular indexing models used for service retrieval in consistent repositories. However, redundancies are inevitable in the inverted index which is significantly time-consuming in the service discovery and retrieval process. This theeis proposes a novel distributed multilevel indexing model (DM-index), which offers an efficient solution for service discovery and retrieval in distributed service repositories comprising massive stored services. The architecture of the proposed indexing model encompasses four hierarchical levels to eliminate redundancy information in service repositories, to narrow the searching space and to reduce the number of traversed services whilst discovering services. Distributed Hash Tables have been widely used to provide data lookup services with logarithmic message costs which only require maintenance of limited amounts of routing states. This thesis develops an optimised searching algorithm, named Double-layer No-redundancy Enhanced Bi-direction Chord (DNEB-Chord), to handle retrieval requests in distributed destination repositories efficiently. This DNEB-Chord algorithm achieves faster routing performances with the double-layer routing mechanism and optimal routing index. The efficiency of the developed indexing and searching model is evaluated through theoretical analysis and experimental evaluation in a newly developed simulation environment, named Distributed Multilevel Bi-direction Simulator (DMBSim), which can be used as cost efficient tool for exploring various service configurations, user retrieval requirements and other parameter settings. Both the theoretical validation and experimental evaluations demonstrate that the service discovery efficiency of the DM-index outperforms the sequential index and inverted index configurations. Furthermore, the experimental evaluation results demostrate that the DNEB-Chord algorithm performs better than the Chord in terms of reducing the incurred hop counts. Finally, simulation results demonstrate that the proposed indexing and searching model can achieve better service discovery performances in large-scale decentralised environments comprising numerous repositories with massive stored services.