A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm
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| Udgivet i: | Sensors vol. 22, no. 13 (2022), p. 4873 |
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MDPI AG
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| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 2686103039 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1424-8220 | ||
| 024 | 7 | |a 10.3390/s22134873 |2 doi | |
| 035 | |a 2686103039 | ||
| 045 | 2 | |b d20220101 |b d20221231 | |
| 084 | |a 231630 |2 nlm | ||
| 100 | 1 | |a Seyed Salar Sefati | |
| 245 | 1 | |a A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm | |
| 260 | |b MDPI AG |c 2022 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The rapid development of Cloud Computing (CC) has led to the release of many services in the cloud environment. Service composition awareness of Quality of Service (QoS) is a significant challenge in CC. A single service in the cloud environment cannot respond to the complex requests and diverse requirements of the real world. In some cases, one service cannot fulfill the user’s needs, so it is necessary to combine different services to meet these requirements. Many available services provide an enormous QoS and selecting or composing those combined services is called an Np-hard optimization problem. One of the significant challenges in CC is integrating existing services to meet the intricate necessities of different types of users. Due to NP-hard complexity of service composition, many metaheuristic algorithms have been used so far. This article presents the Artificial Bee Colony and Genetic Algorithm (ABCGA) as a metaheuristic algorithm to achieve the desired goals. If the fitness function of the services selected by the Genetic Algorithm (GA) is suitable, a set of services is further introduced for the Artificial Bee Colony (ABC) algorithm to choose the appropriate service from, according to each user’s needs. The proposed solution is evaluated through experiments using Cloud SIM simulation, and the numerical results prove the efficiency of the proposed method with respect to reliability, availability, and cost. | |
| 653 | |a Genetic algorithms | ||
| 653 | |a Optimization | ||
| 653 | |a Software services | ||
| 653 | |a Quality of service | ||
| 653 | |a Methods | ||
| 653 | |a Cloud computing | ||
| 653 | |a Energy consumption | ||
| 700 | 1 | |a Halunga, Simona | |
| 773 | 0 | |t Sensors |g vol. 22, no. 13 (2022), p. 4873 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2686103039/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/2686103039/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2686103039/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |