School of Information Technologies How a Consumer Can Measure Elasticity for Cloud Platforms

One major benefit claimed for cloud computing is elasticity: the cost to a consumer of computation can grow or shrink with the workload. This paper offers improved ways to quantify the elasticity concept, using data available to the consumer. We define a measure that reflects the financial penalty to the consumer, from under-provisioning (leading to unacceptable latency or other QoS for requests) or over-provisioning (paying more than necessary for the resources). We propose to compute this penalty measure for a suite of different time-varying workloads, and thus define a single figure that is an overall score for the relative elasticity of two platforms. We have applied these workloads to a public cloud; from our experiments we extract insights into the characteristics of a platform that influence its elasticity. We explore the impact of the rules used to increase or decrease capacity. In particular, we show that the default rules of a widely-used platform give poor elasticity, and that other rules do better on our measure.