Using genetic algorithms to find optimal solution in a search space for a cloud predictive cost-driven decision maker
Abstract In a cloud computing environment there are two types of cost associated with the auto-scaling systems: resource cost and Service Level Agreement (SLA) violation cost. The goal of an auto-scaling system is to find a balance between these costs and minimize the total auto-scaling cost. Howeve...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2018-11-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13677-018-0122-7 |
| _version_ | 1818038779734130688 |
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| author | Ali Yadav Nikravesh Samuel A. Ajila Chung-Horng Lung |
| author_facet | Ali Yadav Nikravesh Samuel A. Ajila Chung-Horng Lung |
| author_sort | Ali Yadav Nikravesh |
| collection | DOAJ |
| description | Abstract In a cloud computing environment there are two types of cost associated with the auto-scaling systems: resource cost and Service Level Agreement (SLA) violation cost. The goal of an auto-scaling system is to find a balance between these costs and minimize the total auto-scaling cost. However, the existing auto-scaling systems neglect the cloud client’s cost preferences in minimizing the total auto-scaling cost. This paper presents a cost-driven decision maker which considers the cloud client’s cost preferences and uses the genetic algorithm to configure a rule-based system to minimize the total auto-scaling cost. The proposed cost-driven decision maker together with a prediction suite makes a predictive auto-scaling system which is up to 25% more accurate than the Amazon auto-scaling system. The proposed auto-scaling system is scoped to the business tier of the cloud services. Furthermore, a simulation package is built to simulate the effect of VM boot-up time, Smart Kill, and configuration parameters on the cost factors of a rule-based decision maker. |
| first_indexed | 2024-12-10T07:48:10Z |
| format | Article |
| id | doaj.art-0f6582f7858e456ba29774d629eac803 |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-12-10T07:48:10Z |
| publishDate | 2018-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-0f6582f7858e456ba29774d629eac8032022-12-22T01:57:07ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2018-11-017112110.1186/s13677-018-0122-7Using genetic algorithms to find optimal solution in a search space for a cloud predictive cost-driven decision makerAli Yadav Nikravesh0Samuel A. Ajila1Chung-Horng Lung2Department of Systems and Computer Engineering, Carleton UniversityDepartment of Systems and Computer Engineering, Carleton UniversityDepartment of Systems and Computer Engineering, Carleton UniversityAbstract In a cloud computing environment there are two types of cost associated with the auto-scaling systems: resource cost and Service Level Agreement (SLA) violation cost. The goal of an auto-scaling system is to find a balance between these costs and minimize the total auto-scaling cost. However, the existing auto-scaling systems neglect the cloud client’s cost preferences in minimizing the total auto-scaling cost. This paper presents a cost-driven decision maker which considers the cloud client’s cost preferences and uses the genetic algorithm to configure a rule-based system to minimize the total auto-scaling cost. The proposed cost-driven decision maker together with a prediction suite makes a predictive auto-scaling system which is up to 25% more accurate than the Amazon auto-scaling system. The proposed auto-scaling system is scoped to the business tier of the cloud services. Furthermore, a simulation package is built to simulate the effect of VM boot-up time, Smart Kill, and configuration parameters on the cost factors of a rule-based decision maker.http://link.springer.com/article/10.1186/s13677-018-0122-7Self-adaptive auto-scaling systemsCloud resource provisioningGenetic algorithmCloud cost-driven decision makerVirtual machine (VM)Service level agreement (SLA) |
| spellingShingle | Ali Yadav Nikravesh Samuel A. Ajila Chung-Horng Lung Using genetic algorithms to find optimal solution in a search space for a cloud predictive cost-driven decision maker Journal of Cloud Computing: Advances, Systems and Applications Self-adaptive auto-scaling systems Cloud resource provisioning Genetic algorithm Cloud cost-driven decision maker Virtual machine (VM) Service level agreement (SLA) |
| title | Using genetic algorithms to find optimal solution in a search space for a cloud predictive cost-driven decision maker |
| title_full | Using genetic algorithms to find optimal solution in a search space for a cloud predictive cost-driven decision maker |
| title_fullStr | Using genetic algorithms to find optimal solution in a search space for a cloud predictive cost-driven decision maker |
| title_full_unstemmed | Using genetic algorithms to find optimal solution in a search space for a cloud predictive cost-driven decision maker |
| title_short | Using genetic algorithms to find optimal solution in a search space for a cloud predictive cost-driven decision maker |
| title_sort | using genetic algorithms to find optimal solution in a search space for a cloud predictive cost driven decision maker |
| topic | Self-adaptive auto-scaling systems Cloud resource provisioning Genetic algorithm Cloud cost-driven decision maker Virtual machine (VM) Service level agreement (SLA) |
| url | http://link.springer.com/article/10.1186/s13677-018-0122-7 |
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