A Mechanism for Web Service Selection and Recommendation Based on Multi-QoS Constraints

Li, Shao-chong; Chen, Haopeng; Chen, Xi

doi:10.1109/services.2010.31

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…Shao-chang Li et al (2010) proposed a heuristic algorithm for selecting services based on QoS parameters, concerning the degree of user satisfaction. Almulla et al (2011) and Bhushan and Pradeep (2016) presented a ranking mechanism by using a fuzzy constraint satisfaction problem, in which QoS criteria are considered.…”

Section: Related Workmentioning

confidence: 99%

A Four-level Linear Discriminant Analysis Based Service Selection in the Cloud Environment

Bhushan¹,

Reddy²

2016

IJTech

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The cloud is an outstanding platform to deal with functionally equivalent services which are exponentially increasing day-by-day. The selection of services to meet the client requirements is a subtle task. The services can be selected by ranking all the candidate services using their network and non-network Quality-of-Service (QoS) parameters, which is formulated as a NP hard optimization problem. In this paper, we proposed a linear discriminant analysis (LDA) based a four level matching model for service selection based on QoS parameters, which includes description matching of a service, matchmaking phase, LDA-based QoS matching and ranking. The LDA-service selection agent is deployed on each cloud to classify services into classes and rank the services based on the aggregate QoS value of each service. Finally, the test results show the efficiency in service selection with minimal discovery overhead, significant reduction in the computation time and the number of candidate services to be considered.

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Section: Related Workmentioning

confidence: 99%

A Four-level Linear Discriminant Analysis Based Service Selection in the Cloud Environment

Bhushan¹,

Reddy²

2016

IJTech

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“…The most important task in this framework is the service classification process, which is then used in a service ability matching algorithm, computing the satisfaction level of available service implementations on the basis of the service requirements. Also [26] proposes a strategy to select and recommend services based on multi-QoS constraints. The system focuses on measurable QoS attributes, i.e., performance, reliability, and availability.…”

Section: Service Reuse and Selectionmentioning

confidence: 99%

A Survey of SOA Technologies in NGN Network Architectures

Branca

Atzori

2011

IEEE Commun. Surv. Tutorials

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The SOA (Service Oriented Architecture) paradigm has been driving the definition of important technologies in the telecommunication field for the last decade, as it fosters the deployment of reusable components to support efficient seamless services across different networks. Most of these technologies have focused on the definition of high-level network service interfaces and functionalities, such as Parlay-X and IMS, guaranteeing the success of the SOA model in the telco market after that of web applications. Some works proposing the adoption of this model in the lower network layers have also been proposed, but they have not proved to have the same impact yet.This paper intends to provide the reader with a survey of the works that have been proposed in this field by following the NGN (Next Generation Network) functional architecture as defined by the ITU-T. Specifically, we focus on the service and transport strata of the NGN model and, within each of these, we highlight the main logic functions that have been the subject of investigation according to a service-oriented approach. From the literature review, it can be seen that most of the works fall in service stratum, mostly because of the need for an easy integration of distributed service components at this layer. Only a few papers focus on the transport functionalities, where the benefits of applying the SOA paradigm still need to be investigated.

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“…Likewise, the QoS information is regarded as a key factor in service matching, selection and discovery, service composition and self-adaption [11] in Service-oriented Computing (SOC) paradigms.…”

Section: Related Workmentioning

confidence: 99%

A QoS-based Self-adaptive Framework for OpenAPI

Sun

Chen

Liang

et al. 2011

2011 Seventh International Conference on Computational Intelligence and Security

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In Web2.0, OpenAPIs (such as Google Map, Flickr and Amazon S3) are considered to be among the most important and vital building blocks for the mashups that combine data and services provided by third parties through OpenAPIs, as well as internal data sources owned by users to aggregate user values and promote user innovation. But as a growing number of OpenAPIs are available on the Internet, the developer and user faces the challenge of dynamic complexity in finding and integrating the right set of OpenAPIs. In this paper, we propose a framework to address the problem of self-adaption in the Internet-scale mashups. First, we design a repository for storing OpenAPIs with Multi-granularity and then introduce QoS metrics measuring the potential impact of OpenAPI properties. We further encapsulate different OpenAPIs whose functionality is synonymous, but with different providers, communication protocols, data formats and parameters. Ultimately, we provide a ranking-style algorithm exploiting the encapsulated interface and QoS characteristics to dynamically select the right OpenAPI in runtime. In short, we propose a QoS-based mashup platform that can facilitate the construction of mashup and improve users' experience. The process is transparent and automatic to the users'. Experimental evaluation demonstrates the effectiveness of our approach.

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A Mechanism for Web Service Selection and Recommendation Based on Multi-QoS Constraints

Cited by 10 publications

References 13 publications

A Four-level Linear Discriminant Analysis Based Service Selection in the Cloud Environment

A Four-level Linear Discriminant Analysis Based Service Selection in the Cloud Environment

A Survey of SOA Technologies in NGN Network Architectures

A QoS-based Self-adaptive Framework for OpenAPI

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