Energy Proportionality in Heterogeneous Data Center Supporting Applications with Variable Load
Abstract
The increasing number of data centers raises serious concerns regarding their energy consumption. Although servers have become more energy-efficient over time, their idle consumption remains high, which is an issue as resources in data centers are often over-provisioned. This work proposes a novel approach for building data centers so that their energy consumption is proportional to load. A data center hence comprises heterogeneous machines carefully chosen for their performance and energy efficiency ratios. We focus on web applications whose load varies over time and design a scheduler that dynamically reconfigures the infrastructure to minimize its energy consumption according to current load and application requirements. Based on load forecasts, it takes reconfiguration decisions and performs actions such as migrating applications and switching machines on or off. The approach is evaluated considering a data center with heterogeneous resources, and the experiments show how to adjust the parameters of scheduling policies to save the most energy while satisfying Quality of Service (QoS) constraints.
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