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Auto-Scaling in the Cloud : Evaluating a Control Based Technique

One of the major benefits of cloud computing is elasticity, that is the ability to provision resources based on workload. Scaling up resources when load is high to meet service level objectives. Scaling down when resources are underutilised thereby saving on cloud provider costs. The auto-scaling capabilities offered by cloud providers are in the majority quantitative rule based algorithms [1], dependant on Quality of Service (QoS) and the monitoring thereof. For example, an additional virtual machine (VM) may be introduced to handle increased load when an existing VM CPU metric reaches an 80% threshold, measured over a set period. In this paper I will provide background on existing scaling techniques and evaluate a fuzzy logic based autoscaling system which has the advantage over current commercial offerings in that it utilises a qualitative approach to auto-scaling.