Characterizing the Impact of the Workload on the Value of Dynamic Resizing in Data Centers

TL;DR

This paper investigates how workload characteristics influence the effectiveness of dynamic resizing in data centers, revealing that both slow and fast workload variations significantly impact potential energy savings.

Contribution

It introduces a combined optimization and stochastic modeling framework to analyze the conditions under which dynamic resizing is beneficial in data centers.

Findings

Dynamic resizing benefits depend on workload variability.

Slow and fast workload fluctuations both affect energy savings.

Analytic and numerical models identify when resizing is advantageous.

Abstract

Energy consumption imposes a significant cost for data centers; yet much of that energy is used to maintain excess service capacity during periods of predictably low load. Resultantly, there has recently been interest in developing designs that allow the service capacity to be dynamically resized to match the current workload. However, there is still much debate about the value of such approaches in real settings. In this paper, we show that the value of dynamic resizing is highly dependent on statistics of the workload process. In particular, both slow time-scale non-stationarities of the workload (e.g., the peak-to-mean ratio) and the fast time-scale stochasticity (e.g., the burstiness of arrivals) play key roles. To illustrate the impact of these factors, we combine optimization-based modeling of the slow time-scale with stochastic modeling of the fast time scale. Within this framework, we provide both analytic and numerical results characterizing when dynamic resizing does (and does not) provide benefits.