Abstract
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.
Highlights
This article is an open access articleWeb services are distributed, loosely coupled software components designed to perform simple tasks and to support interoperable machine-to-machine interaction and exchange data over a network
The proposed model consists of four processes: the first process classifies the web services into classes based on each web service action; the second process creates clusters inside each class based on the web services’ quality of service (QoS) attributes and the reward function; the third process constructs the services’ compositions using the knapsack algorithm; and the final process tracks and evaluates the QoS attributes related to the integrated web services that construct the service composition using the reward function, where web services’ and compositions’ QoS attributes are used to find the reward function of the service composition to evaluate and track the provided services and to upgrade the composition knapsacks according to the service-level agreement requirements
The percentage of the web services’ resource utilization when the composiFigure 8 presents the number of used and unused web service resources in the sertion threshold equals 100 is better than the results when the composition threshold equals vices composition process using the proposed model, and the multistage forward search (MFS) and SARSA algo200. This is related to the minimum number of requests allowed in the service compositions, rithms, where the web services’ resource utilization using the proposed model increased which means that the unused web services and the web services with available resources will be utilized and reused to construct a greater number of service compositions and to Figure 7.services
Summary
Loosely coupled software components designed to perform simple tasks and to support interoperable machine-to-machine interaction and exchange data over a network. The proposed model has been tested on a real benchmarks dataset (QWS) to demonstrate its efficiency for optimizing the web service resource utilization, tracking the service composition QoS using the composition reward function, and in comparing the results with the requirements of clients’ SLA QoS attributes; 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. The proposed web service composition model using k-means clustering and knapsack algorithms is used to enhance the service composition selection and construction processes and to optimize web services’ resource utilization by maximizing the service compositions’ QoS and minimizing the number of web services integrated in the service composition knapsack. This research paper is organized as follows: Section 2 presents published web services selection and composition research; in Section 3, the proposed model is introduced, which includes the web service selection and composition QoS attributes, as well as the reward functions, the processes of classification and clustering, and the knapsack algorithm; Section 4 discusses the experiments’ dataset, results, and evaluation; Section 5 presents the research conclusions and suggestions for future work
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