F.2.7. Optimization
Seyed Morteza Babamir; Narges Zahiri
Abstract
Web service composition represents a graph of interacting services designed to fulfill user requirements, where each node denotes a service, and each edge represents an interaction between two services. A few candidates with different quality attributes exist on the web for conducting each web service. ...
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Web service composition represents a graph of interacting services designed to fulfill user requirements, where each node denotes a service, and each edge represents an interaction between two services. A few candidates with different quality attributes exist on the web for conducting each web service. Consequently, numerous compositions with identical functionality but differing quality attributes can be formed, making the near-optimal composition selection an NP-hard problem. This paper proposes a tool-supported Evolutionary Optimization Algorithm (EOA) for near-optimal composition selection. The proposed EOA is a Discretized and Extended Gray Wolf Optimization (DEGWO) algorithm. This approach first discretizes the continuous solution space and then extends the functionality of GWO to identify global near-optimal solutions while accelerating solution convergence. DEGWO was evaluated in comparison with other related methods in terms of metrics. Experimental results showed DEGWO achieved average improvements of 8%, 39%, and 5% in terms of availability, 36%, 43%, and 30% in terms of response time, and 65%, 53%, and 51% in terms of cost compared to the three leading algorithms, RDGWO+GA, HGWO, and SFLAGA, respectively.
Mohammad AllamehAmiri; Vali Derhami; Mohammad Ghasemzadeh
Abstract
Quality of service (QoS) is an important issue in the design and management of web service composition. QoS in web services consists of various non-functional factors, such as execution cost, execution time, availability, successful execution rate, and security. In recent years, the number of available ...
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Quality of service (QoS) is an important issue in the design and management of web service composition. QoS in web services consists of various non-functional factors, such as execution cost, execution time, availability, successful execution rate, and security. In recent years, the number of available web services has proliferated, and then offered the same services increasingly. The same web services are distinguished based on their quality parameters. Also, clients usually demand more value added services rather than those offered by single, isolated web services. Therefore, selecting a composition plan of web services among numerous plans satisfies client requirements and has become a challenging and time-consuming problem. This paper has proposed a new composition plan optimizer with constraints based on genetic algorithm. The proposed method can find the composition plan that satisfies user constraints efficiently. The performance of the method is evaluated in a simulated environment.
