scispace - formally typeset
Journal ArticleDOI

Dynamic right-sizing for power-proportional data centers

Reads0
Chats0
TLDR
A very general model is proposed and it is proved 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.
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.

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

A Survey on Mobile Edge Computing: The Communication Perspective

TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management is provided in this paper, where a set of issues, challenges, and future research directions for MEC are discussed.
Posted Content

A Survey on Mobile Edge Computing: The Communication Perspective

TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management and recent standardization efforts on MEC are introduced.
Journal ArticleDOI

Data Center Energy Consumption Modeling: A Survey

TL;DR: An in-depth study of the existing literature on data center power modeling, covering more than 200 models, organized in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models.
Journal ArticleDOI

On Global Electricity Usage of Communication Technology: Trends to 2030

TL;DR: An estimation of the global electricity usage that can be ascribed to Communication Technology between 2010 and 2030 suggests that CT electricity usage could contribute up to 23% of the globally released greenhouse gas emissions in 2030.
Journal ArticleDOI

Resource Management in Clouds: Survey and Research Challenges

TL;DR: This paper outlines a conceptual framework for cloud resource management and uses it to structure the state-of-the-art review, and identifies five challenges for future investigation that relate to providing predictable performance for cloud-hosted applications.
References
More filters
Book

Model Predictive Control

TL;DR: In this article, the authors present a model predictive controller for a water heating system, which is based on the T Polynomial Process (TOP) model of the MPC.
Proceedings ArticleDOI

Live migration of virtual machines

TL;DR: The design options for migrating OSes running services with liveness constraints are considered, the concept of writable working set is introduced, and the design, implementation and evaluation of high-performance OS migration built on top of the Xen VMM are presented.
Book

Online Computation and Competitive Analysis

TL;DR: This book discusses competitive analysis and decision making under uncertainty in the context of the k-server problem, which involves randomized algorithms in order to solve the problem of paging.
Journal ArticleDOI

The Case for Energy-Proportional Computing

TL;DR: Energy-proportional designs would enable large energy savings in servers, potentially doubling their efficiency in real-life use, particularly the memory and disk subsystems.
Proceedings ArticleDOI

A scheduling model for reduced CPU energy

TL;DR: This paper proposes a simple model of job scheduling aimed at capturing some key aspects of energy minimization, and gives an off-line algorithm that computes, for any set of jobs, a minimum-energy schedule.
Related Papers (5)