User-Centered QoS Computation for Web Service Selection

Chen, Shi; Lin, Donghui; Ishida, Toru

doi:10.1109/icws.2012.18

Cited by 12 publications

(4 citation statements)

References 29 publications

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“…The work in [8] gives details on how to collect networking level criteria of Web service's execution time, availability and reliability. In [9,10] the authors present approaches to compute and evaluate the QoS of Web services. In [11][12][13], the QoS-based service management has also been widely discussed.…”

Section: Related Workmentioning

confidence: 99%

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QoS-Based and Network-Aware Web Service Composition across Cloud Datacenters

Wang¹,

Yang²,

Mi³

2015

KSII TIIS

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With the development of cloud computing, more and more Web services are deployed on geo-distributed datacenters and are offered to cloud users all over the world. Through service composition technologies, these independent fine-grain services can be integrated to value-added coarse-grain services. During the composition, a number of Web services may provide the same function but differ in performance. In addition, the distribution of cloud datacenters presents a geographically dispersive manner, which elevates the impact of the network on the QoS of composite services. So it is important to select an optimal composition path in terms of QoS when many functionally equivalent services are available. To achieve this objective, we first present a graph model that takes both QoS of Web services and QoS of network into consideration. Then, a novel approach aiming at selecting the optimal composition path that fulfills the user's end-to-end QoS requirements is provided. We evaluate our approach through simulation and compare our method with existing solutions. Results show that our approach significantly outperforms existing solutions in terms of optimality and scalability.

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Section: Related Workmentioning

confidence: 99%

“…The random values of response time in each service set had a Gaussian distribution and a mean value within the range [20,1500]. The ranges of atomic services' availability, price and reputation were [0.95, 1], [1,10] and [0.4, 1] in order. For simulation, the four QoS criteria mentioned above gained equal preference from users.…”

Section: Simulation Setupmentioning

confidence: 99%

QoS-Based and Network-Aware Web Service Composition across Cloud Datacenters

Wang¹,

Yang²,

Mi³

2015

KSII TIIS

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“…Amirthayogam et al (2013) provides an agent-based architecture for finding the most suitable web service according to the consumer's Non-functional requirements like QoS along with functional requirements. Chunqi and Donghui (2012) proposed a user-centered QoS computation which is an approach of approximation in user satisfaction calculation aspect. Ouchetto et al (2012) propose a method based on a mathematical representation for e-gov systems to assess the adequacy of rendered services.…”

Section: Related Workmentioning

confidence: 99%

User-Centric Design for Semantic Discovery of Mathematical Web Services

Sheeba¹

2014

American Journal of Applied Sciences

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Adopting an efficient and effective method to locate and select desired services among thousands of available web services is an important task in the service-oriented computing environment. If the services are registered properly, then it will be easy for the service requesters to discover the Web services. Here, we provide Web Service Registration Interface for the service providers along with an ontology tree to register the functional and non-functional properties of the mathematical Web services under a particular domain. The registration details are stored in an XML file and a sorted file. A user-centric client interface is also provided for the service requesters to search the Web services based on user's preference. Finally, a Quality of Service based ranking algorithm is provided to rank the Web services and top-k Web services are returned. With the experimental evaluation, our approach not only alleviates the requesters from time consuming discovery tasks but also reduce the search space for discovery processes in the user-centric Web environment.

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“…Second, even one service provider may deploy multiple duplications of a service on different servers to guarantee the service availability to the users. Therefore, upon arrival of service requests, how to choose the most appropriate services among all these (duplicated) candidate services to provide required functionality and thereby best meeting cost and Quality of Service (QoS) requirements [5], is one of the demanding responsibilities for service brokers. This issue, known as "service selection", has been widely studied by the services computing community for several years, and is usually formulated into an optimization problem, with the goal of maximizing or minimizing some properties [7].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Dynamic Service Selection for Large-Scale Web Service Systems

Chen

Huang

Xiang

et al. 2014

2014 IEEE International Conference on Web Services

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With the increasing popularity of web services on the Internet, service selection has become an important issue in large-scale web service systems. In recent years, with the growing energy consumption associated with IT systems and services, energy efficiency has drawn extensive attention in service allocation and selection. However, existing approaches for energy efficient web service selection face great challenges, because of the high dynamics and unpredictability of task arrivals, and large scale of current web service systems. In this paper, we present a distributed online approach for web service selection that jointly considers the conflicting performance metrics including response time, queue congestion and energy consumption. Targeting at optimizing the long-term average system reward, our approach does not require any priori knowledge of the statistics or prediction on task arrivals. Mathematical analysis as well as simulation experiments demonstrate its effectiveness in optimizing energy efficiency while stabilizing the system.

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User-Centered QoS Computation for Web Service Selection

Cited by 12 publications

References 29 publications

QoS-Based and Network-Aware Web Service Composition across Cloud Datacenters

QoS-Based and Network-Aware Web Service Composition across Cloud Datacenters

User-Centric Design for Semantic Discovery of Mathematical Web Services

Energy Efficient Dynamic Service Selection for Large-Scale Web Service Systems

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