diverse cloud applications deployed on-demand make for workload burstiness. Burstiness is quantified statistically through different variance measures. This paper focuses on the statistical measures used to quantify cloud workload burstiness. Using diverse workloads, it identifies different statistical models that uniquely capture workload specific burstiness. Subsequently, it employs recent econometric models described as Auto-regressive Conditional Score (ACS) motivated by their ability to model time-varying parameters that capture burstiness more accurately than existing methods. Furthermore, it has inspired a novel measure of burstiness, the Normalized Score Index (NSI). Compared to existing measures, the NSI captures burstiness specific to statistical features per workload. When standard variance features are observed, the NSI reverts to traditional measures and when nonstandard features are present, it models them accordingly. The NSI has been applied to a diverse workload set and yields both a static metric and a means by which to track burstiness over a workload's lifecycle.
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