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. 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.