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.
Highlights
The elastic nature of cloud computing enables cloud clients to benefit from the cloud’s pay-as-you-go pricing model, which reduces cloud clients’ capital expenses and their overall operational costs
Maintaining Service Level Agreements (SLAs) with the end users obliges the cloud service provider to provide a certain level of Quality-of-Service (QoS) and the cloud service provider gets penalized if the cloud service fails to meet the desired SLAs
Background and related work we present an overview of the existing auto-scaling systems, and describe the rule-based auto-scaling technique and introduce its configuration parameters
Summary
The elastic nature of cloud computing enables cloud clients to benefit from the cloud’s pay-as-you-go pricing model, which reduces cloud clients’ capital expenses and their overall operational costs. The proposed auto-scaling system uses genetic algorithm principle to automatically identify an optimum configuration of the rule-based systems. According to the results (see Table 6), decreasing the upper threshold increases the resource cost, while it reduces the number of the SLA violations.
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