Abstract
Web service composition allows developers to create and deploy applications that take advantage of the capabilities of service-oriented computing. Such applications provide the developers with reusability opportunities as well as seamless access to a wide range of services that provide simple and complex tasks to meet the clients’ requests in accordance with the service-level agreement (SLA) requirements. Web service composition issues have been addressed as a significant area of research to select the right web services that provide the expected quality of service (QoS) and attain the clients’ SLA. The proposed model enhances the processes of web service selection and composition by minimizing the number of integrated Web Services, using the Multistage Forward Search (MSF). In addition, the proposed model uses the Spider Monkey Optimization (SMO) algorithm, which improves the services provided with regards to fundamentals of service composition methods symmetry and variations. It achieves that by minimizing the response time of the service compositions by employing the Load Balancer to distribute the workload. It finds the right balance between the Virtual Machines (VM) resources, processing capacity, and the services composition capabilities. Furthermore, it enhances the resource utilization of Web Services and optimizes the resources’ reusability effectively and efficiently. The experimental results will be compared with the composition results of the Smart Multistage Forward Search (SMFS) technique to prove the superiority, robustness, and effectiveness of the proposed model. The experimental results show that the proposed SMO model decreases the service composition construction time by 40.4%, compared to the composition time required by the SMFS technique. The experimental results also show that SMO increases the number of integrated ted web services in the service composition by 11.7%, in comparison with the results of the SMFS technique. In addition, the dynamic behavior of the SMO improves the proposed model’s throughput where the average number of the requests that the service compositions processed successfully increased by 1.25% compared to the throughput of the SMFS technique. Furthermore, the proposed model decreases the service compositions’ response time by 0.25 s, 0.69 s, and 5.35 s for the Excellent, Good, and Poor classes respectively compared to the results of the SMFS Service composition response times related to the same classes.
Highlights
Service-Oriented Architecture (SOA) is a style of software design that utilizes WebServices
According to the Quality of Service (QoS) attributes’ values related to the integrated web services and the service compositions; the system learns about the workload on each class, response time, throughput, and availability, which are used to distribute the load between the different load balancing objects in the same class
It shows the performance of the web services and the QoS changes and variations
Summary
Service-Oriented Architecture (SOA) is a style of software design that utilizes Web. Services. Dynamic methods and techniques are complex processes that satisfy the SLA functional and non-functional requirements These requirements’ constraints are still a challenge, and they are not efficient enough when applied in real-time and large-scale environments, in addition to the mentioned factors that affect the QoS of the web services [8,14,15]. Other problems that may affect the service composition QoS include the utilization of web services’ resources This issue relates to the value of the maximum capacity of integrated web services. During the process of providing the services to the clients, the Load Balancer will control and monitor the web service, compositions workload, QoS, and capacity. This research presents the proposed model (using the SMO algorithm) to solve the service composition construction process and the web service resources utilization problems efficiently.
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