Work

Dynamic Right-Sizing for Power-Proportional Data Centers

Year: 2012

Type: article

Abstract: Power consumption imposes a significant cost for data centers implementing cloud services, yet much of that power is used to maintain excess service capacity during periods of low load. This paper investigates how much can be saved by dynamically “right-sizing” the data center by turning off servers during such periods and how to achieve that saving via an online algorithm. We propose a very general model and prove that the optimal offline algorithm for dynamic right-sizing has a simple structure when viewed in reverse time, and this structure is exploited to develop a new “lazy” online algorithm, which is proven to be 3-competitive. We validate the algorithm using traces from two real data-center workloads and show that significant cost savings are possible. Additionally, we contrast this new algorithm with the more traditional approach of receding horizon control.

Source: IEEE/ACM Transactions on Networking

DOI: 10.1109/tnet.2012.2226216

Authors: Minghong Lin, Adam Wierman, Andrew, Eno Thereska

Institutions: California Institute of Technology, Swinburne University of Technology, Microsoft Research (United Kingdom)

Language: English

Topic: Cloud Computing and Resource Management

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funders: National Science Foundation, Microsoft Research, Australian Research Council