Service selection in a marketplace: a multi-perspective solution

10.32920/ryerson.14662698 ◽

2021 ◽

Author(s):

Dipak Pudasaini

Keyword(s):

Web Service ◽

Research Work ◽

Service Selection ◽

User Requirements ◽

Objective Functions ◽

Web Service Selection ◽

Optimal Score ◽

Proposed Model ◽

Degree Of Satisfaction ◽

High Degree

Most of the current research work on web service selection only considered the selection problem from the perspective of one party – service consumers. A service marketplace serves many parties including service consumers and providers. Thus, it is important to consider multiple parties. In this thesis, we propose a service selection model considering the benefits of multiple parties: consumers, providers and the marketplace. The model ranks services based on not only how much these services satisfy the user requirements but also how much the requests can be distributed to different providers and the revenue gain in the marketplace. We design different objective functions, then combine into a QoS-Plus-PF objective function. The results show that proposed model could achieve a high degree of satisfaction of user requests (i.e., 0.61% to 5.26% worse than the optimal score), and meanwhile have the capability of promoting more diversified set of services (i.e., 48.95% promotion percentage).

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An Intelligent Web Service Composition and Resource-Optimization Method Using K-Means Clustering and Knapsack Algorithms

Mathematics ◽

10.3390/math9172023 ◽

2021 ◽

Vol 9 (17) ◽

pp. 2023

Author(s):

Issam Alhadid ◽

Sufian Khwaldeh ◽

Mohammad Al Rawajbeh ◽

Evon Abu-Taieh ◽

Ra’ed Masa’deh ◽

...

Keyword(s):

Web Services ◽

Web Service ◽

Resource Utilization ◽

Service Composition ◽

Service Selection ◽

Experimental Results ◽

State Action ◽

Web Service Selection ◽

Proposed Model ◽

The Web

Service-oriented architecture (SOA) has emerged as a flexible software design style. SOA focuses on the development, use, and reuse of small, self-contained, independent blocks of code called web services that communicate over the network to perform a certain set of simple tasks. Web services are integrated as composite services to offer complex tasks and to provide the expected services and behavior in addition to fulfilling the clients’ requests according to the service-level agreement (SLA). Web service selection and composition problems have been a significant area of research to provide the expected quality of service (QoS) and to meet the clients’ expectations. This research paper presents a hybrid web service composition model to solve web service selection and composition problems and to optimize web services’ resource utilization using k-means clustering and knapsack algorithms. The proposed model aims to maximize the service compositions’ QoS and minimize the number of web services integrated within the service composition using the knapsack algorithm. Additionally, this paper aims to track the service compositions’ QoS attributes by evaluating and tracking the web services’ QoS using the reward function and, accordingly, use the k-means algorithm to decide to which cluster the web service belongs. The experimental results on a real dataset show the superiority and effectiveness of the proposed algorithm in comparison with the results of the state–action–reward–state–action (SARSA) and multistage forward search (MFS) algorithms. The experimental results show that the proposed model reduces the average time of the web service selection and composition processes to 37.02 s in comparison to 47.03 s for the SARSA algorithm and 42.72 s for the MFS algorithm. Furthermore, the average of web services’ resource utilization results increased by 4.68% using the proposed model in comparison to the resource utilization by the SARSA and MFS algorithms. In addition, the experimental results showed that the average number of service compositions using the proposed model improved by 26.04% compared with the SARSA and MFS algorithms.

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A Hybrid User-Centric Approach for Efficient Web Service Selection

International Journal of Information Retrieval Research ◽

10.4018/ijirr.2020040101 ◽

2020 ◽

Vol 10 (2) ◽

pp. 1-20

Author(s):

Neerja Negi ◽

Satish Chandra

Keyword(s):

Web Services ◽

Web Service ◽

Service Selection ◽

User Requirements ◽

End User ◽

Main Challenge ◽

Web Service Selection ◽

User Expectations ◽

Evaluation Mechanism ◽

The Web

Over the last few years, e-commerce has exhibited explosive growth due to the ease of availability of the internet. E-commerce is rapidly changing the way in which businesses are interacting with each other as well as with their consumers. In each innovative e-commerce application, web services are being included as an important component. This leads to the availability of a huge number of web services that provide similar functionalities. The main challenge is to select the appropriate web service which fulfills the end user requirements. So, there is a need for a web service selection method that selects the web services not only based on their functionality, but also considers the nonfunctional requirements. This article proposes a method to preprocess web services using the J48 classification technique. After that, a hybrid weight evaluation mechanism is employed to obtain the weight values of each nonfunctional parameter. In the end, the web services that are near to user expectations are selected out using the ranking method.

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