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Optimizing the datacenter for data-centric workloads

Stijn Polfliet, Frederick Ryckbosch and Lieven Eeckhout UGent (2011) ICS '11 : proceedings of the international conference on supercomputing. p.182-191
abstract
The amount of data produced on the internet is growing rapidly. Along with data explosion comes the trend towards more and more diverse data, including rich media such as audio and video. Data explosion and diversity leads to the emergence of data-centric workloads to manipulate, manage and analyze the vast amounts of data. These data-centric workloads are likely to run in the background and include application domains such as data mining, indexing, compression, encryption, audio/video manipulation, data warehousing, etc. Given that datacenters are very much cost sensitive, reducing the cost of a single component by a small fraction immediately translates into huge cost savings because of the large scale. Hence, when designing a datacenter, it is important to understand data-centric workloads and optimize the ensemble for these workloads so that the best possible performance per dollar is achieved. This paper studies how the emerging class of data-centric workloads affects design decisions in the datacenter. Through the architectural simulation of minutes of run time on a validated full-system x86 simulator, we derive the insight that for some data-centric workloads, a high-end server optimizes performance per total cost of ownership (TCO), whereas for other workloads, a low-end server is the winner. This observation suggests heterogeneity in the datacenter, in which a job is run on the most cost-efficient server. Our experimental results report that a heterogeneous datacenter achieves an up to 88%, 24% and 17% improvement in cost-efficiency over a homogeneous high-end, commodity and low-end server datacenter, respectively.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
computer architecture, data center, workload characterization
in
ICS '11 : proceedings of the international conference on supercomputing
pages
182 - 191
publisher
Association for Computing Machinery (ACM)
place of publication
New York, NY, USA
conference name
25th International conference on Supercomputing (ICS 2011)
conference location
Tucson, AZ, USA
conference start
2011-05-31
conference end
2011-06-04
ISBN
9781450301022
DOI
10.1145/1995896.1995926
project
HPC-UGent: the central High Performance Computing infrastructure of Ghent University
language
English
UGent publication?
yes
classification
C1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1977531
handle
http://hdl.handle.net/1854/LU-1977531
date created
2012-01-03 17:32:56
date last changed
2017-01-02 09:53:22
@inproceedings{1977531,
  abstract     = {The amount of data produced on the internet is growing rapidly. Along with data explosion comes the trend towards more and more diverse data, including rich media such as audio and video. Data explosion and diversity leads to the emergence of data-centric workloads to manipulate, manage and analyze the vast amounts of data. These data-centric workloads are likely to run in the background and include application domains such as data mining, indexing, compression, encryption, audio/video manipulation, data warehousing, etc.
Given that datacenters are very much cost sensitive, reducing the cost of a single component by a small fraction immediately translates into huge cost savings because of the large scale. Hence, when designing a datacenter, it is important to understand data-centric workloads and optimize the ensemble for these workloads so that the best possible performance per dollar is achieved.
This paper studies how the emerging class of data-centric workloads affects design decisions in the datacenter.
Through the architectural simulation of minutes of run time on a validated full-system x86 simulator, we derive the insight that for some data-centric workloads, a high-end server optimizes performance per total cost of ownership (TCO), whereas for other workloads, a low-end server is the winner. This observation suggests heterogeneity in the datacenter, in which a job is run on the most cost-efficient server. Our experimental results report that a heterogeneous datacenter achieves an up to 88\%, 24\% and 17\% improvement in cost-efficiency over a homogeneous high-end, commodity and low-end server datacenter, respectively.},
  author       = {Polfliet, Stijn and Ryckbosch, Frederick and Eeckhout, Lieven},
  booktitle    = {ICS '11 : proceedings of the international conference on supercomputing},
  isbn         = {9781450301022},
  keyword      = {computer architecture,data center,workload characterization},
  language     = {eng},
  location     = {Tucson, AZ, USA},
  pages        = {182--191},
  publisher    = {Association for Computing Machinery (ACM)},
  title        = {Optimizing the datacenter for data-centric workloads},
  url          = {http://dx.doi.org/10.1145/1995896.1995926},
  year         = {2011},
}

Chicago
Polfliet, Stijn, Frederick Ryckbosch, and Lieven Eeckhout. 2011. “Optimizing the Datacenter for Data-centric Workloads.” In ICS  ’11 : Proceedings of the International Conference on Supercomputing, 182–191. New York, NY, USA: Association for Computing Machinery (ACM).
APA
Polfliet, S., Ryckbosch, F., & Eeckhout, L. (2011). Optimizing the datacenter for data-centric workloads. ICS  ’11 : proceedings of the international conference on supercomputing (pp. 182–191). Presented at the 25th International conference on Supercomputing (ICS 2011), New York, NY, USA: Association for Computing Machinery (ACM).
Vancouver
1.
Polfliet S, Ryckbosch F, Eeckhout L. Optimizing the datacenter for data-centric workloads. ICS  ’11 : proceedings of the international conference on supercomputing. New York, NY, USA: Association for Computing Machinery (ACM); 2011. p. 182–91.
MLA
Polfliet, Stijn, Frederick Ryckbosch, and Lieven Eeckhout. “Optimizing the Datacenter for Data-centric Workloads.” ICS  ’11 : Proceedings of the International Conference on Supercomputing. New York, NY, USA: Association for Computing Machinery (ACM), 2011. 182–191. Print.