Descartes Modeling Language (DML)
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Originating from the field of physics and economics, the term elasticity is nowadays heavily used in the context of cloud computing. In this context, elasticity is commonly understood as the ability of a system to automatically provision and deprovision computing resources on demand as workloads change. However, elasticity still lacks a precise definition as well as representative metrics coupled with a benchmarking methodology to enable comparability of systems. Existing definitions of elasticity are largely inconsistent and unspecific, which leads to confusion in the use of the term and its differentiation from related terms such as scalability and efficiency; the proposed measurement methodologies do not provide means to quantify elasticity without mixing it with efficiency or scalability aspects. In this short paper, we propose a precise definition of elasticity and analyze its core properties and requirements explicitly distinguishing from related terms such as scalability and efficiency. Furthermore, we present a set of appropriate elasticity metrics and sketch a new elasticity tailored benchmarking methodology addressing the special requirements on workload design and calibration.
Nikolas Roman Herbst, Samuel Kounev, and Ralf Reussner. Elasticity in Cloud Computing: What it is, and What it is Not. In Proceedings of the 10th International Conference on Autonomic Computing (ICAC 2013), San Jose, CA, June 24-28. USENIX. 2013. [ bib | .pdf | slides | poster ]