An Efficient Hybrid Metaheuristic Algorithm for QoS-Aware Cloud Service Composition Problem
Fadl Dahana b, Wojdan M. BinSaeedanc, Meteb M. Altafd, Mahfoudh Saeed Al-Asalye, Mohammad Mehedi Hassane
- aCollege of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
- bFaculty of Computing and Information Technology Alturbah, Taiz University, Taiz, Yemen
- cCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
- dAdvanced Manufacturing and Industry 4.0 Center, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
- eResearch Chair of Smart Technologies, King Saud University, Riyadh, Saudi Arabia
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Abstract
Cloud computing has a great ability to store and manage remote access to services in a term of software as a service (SaaS). Recently, many organizations have moved to use outsourcing over the cloud to reduce the local resource burden. The stored services over the cloud are too scalable and complex, so an optimization method is more desirable to select appropriate services that satisfy the clients' request. To do so, the quality of service (QoS) parameters that associated with each service are the best resources for choosing and optimizing the appropriate services over the cloud. Therefore, the cloud service composition aims to select and integrate services over the cloud to satisfy the clients' request. In this work, a hybrid algorithm is introduced, which combines ant colony optimization (ACO) and genetic algorithm (GA) to efficiently compose the services over the cloud. The GA is used to tune the ACO's parameters automatically and the ACO adapts its performance based on the parameters tuning. The main contribution of this work is to help the ACO algorithm to avoid stagnation problem and enhance the performance of the ACO where this performance is affected by the value of the ACO's parameters. The experimental results on 15 different real datasets have shown the effectiveness of the proposed algorithm to search comparable solutions compared to five competitors.
TL;DRA hybrid algorithm is introduced, which combines ant colony optimization (ACO) and genetic algorithm (GA) to efficiently compose the services over the cloud to avoid stagnation problem and enhance the performance of the ACO where this performance is affected by the value of theACO’s parameters. (Semantic Scholar)