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

Mathematics

Issam Alhadid¹  Evon Abu-Taieh¹  Sufian Khwaldeh¹  Mohammad Al Rawajbeh²  Ibrahim Aljarah³  Ra’ed Masa’deh⁴ 

[1] Faculty of Information Technology and Systems, University of Jordan, Aqaba 77111, Jordan;Faculty of Science and Information Technology, AL Zaytoonah University of Jordan, Amman 11942, Jordan;King Abdulla II School for Information Technology, University of Jordan, Amman 11942, Jordan;School of Business, University of Jordan, Amman 11942, Jordan;

关键词: service-oriented architecture;    web services;    composition;    k-means clustering;    knapsack;    optimization;   

DOI  :  10.3390/math9172023

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown