How a consumer can measure elasticity for cloud platforms

  • Authors:

  • Sadeka Islam;Kevin Lee;Alan Fekete;Anna Liu

  • Affiliations:

  • National ICT Australia, University of New South Wales, Sydney, Australia;National ICT Australia, University of New South Wales, Sydney, Australia;University of New South Wales, National ICT Australia, Sydney, Australia;National ICT Australia, University of New South Wales, Sydney, Australia

  • Venue:
  • ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
  • Year:
  • 2012

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Abstract

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 a particular consumer, from under-provisioning (leading to unacceptable latency or unmet demand) or over-provisioning (paying more than necessary for the resources needed to support a workload). We have applied several 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.