Using genetic algorithms to find optimal solution in a search space for a cloud predictive cost-driven decision maker

• Main Authors: Ali Yadav Nikravesh, Samuel A. Ajila, Chung-Horng Lung
• Format: Article
• Language: English
• Published: SpringerOpen 2018-11-01
• Series: Journal of Cloud Computing: Advances, Systems and Applications
• Subjects: Self-adaptive auto-scaling systems; Cloud resource provisioning; Genetic algorithm; Cloud cost-driven decision maker; Virtual machine (VM); Service level agreement (SLA)
• Online Access: http://link.springer.com/article/10.1186/s13677-018-0122-7

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.