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Prediction-Based Joint Energy Optimization for Virtualized Data Centers

Published: 25 May 2020 Publication History

Abstract

Today's data centers tend to have tens to hundreds of thousands of servers to provide massive and sophisticated services. Statistically, data center and data center networks (DCNs) remain highly underutilized which can be exploited for energy-saving. In this paper, we have studied energy-saving problem for network and server sides of the data center. The problem was formulated as a Mixed Integer Linear Program (MILP) that is solvable by an optimization software to jointly minimize the energy consumed by the servers and DCN. To overcome the optimization software high computational time, a heuristic algorithm to provide practical and efficient solution is introduced. The heuristic algorithm has two stages: first, it uses the virtual machines (VM) and the predicted servers resource utilization to provide VM consolidation algorithm and turn-off unused servers. The second stage uses an abstract performance-aware network flow consolidation that focused the traffic on subset of the network and turn-off the unused network devices. Simulation experiments using CloudsimSDN were conducted to validate the heuristic using real traces from Wikipedia in terms of energy consumption and average response time. The results show that the heuristic can save servers and network energy while maintaining performance.

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cover image ACM Conferences
ACMSE '20: Proceedings of the 2020 ACM Southeast Conference
April 2020
337 pages
ISBN:9781450371056
DOI:10.1145/3374135
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 May 2020

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Author Tags

  1. Data centers
  2. Energy saving
  3. Optimization
  4. Prediction

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ACM SE '20
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ACM SE '20: 2020 ACM Southeast Conference
April 2 - 4, 2020
FL, Tampa, USA

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