Precentile-Based Approach to Forecasting Workload Growth
Venue
IT Performance and Capacity by CMG 41st International Conference (CMG2015), Computer Measurement Group, 3501 Route 42 Suite 130 #121 Turnersville, NJ 08012-1734 USA
Publication Year
2015
Authors
Alex Gilgur, Stephen Gunn, Douglas Browning, Xiaojun Di, Wei Chen, Rajesh Krishnaswamy
BibTeX
Abstract
When forecasting resource workloads (traffic, CPU load, memory usage, etc.), we
often extrapolate from the upper percentiles of data distributions. This works very
well when the resource is far enough from its saturation point. However, when the
resource utilization gets closer to the workload-carrying capacity of the resource,
upper percentiles level off (the phenomenon is colloquially known as flat-topping
or clipping), leading to underpredictions of future workload and potentially to
undersized resources. This paper explains the phenomenon and proposes a new
approach that can be used for making useful forecasts of workload when historical
data for the forecast are collected from a resource approaching saturation.
