---
_id: '11850'
abstract:
- lang: eng
  text: 'Modern networked systems are increasingly reconfigurable, enabling demand-aware
    infrastructures whose resources can be adjusted according to the workload they
    currently serve. Such dynamic adjustments can be exploited to improve network
    utilization and hence performance, by moving frequently interacting communication
    partners closer, e.g., collocating them in the same server or datacenter. However,
    dynamically changing the embedding of workloads is algorithmically challenging:
    communication patterns are often not known ahead of time, but must be learned.
    During the learning process, overheads related to unnecessary moves (i.e., re-embeddings)
    should be minimized. This paper studies a fundamental model which captures the
    tradeoff between the benefits and costs of dynamically collocating communication
    partners on l servers, in an online manner. Our main contribution is a distributed
    online algorithm which is asymptotically almost optimal, i.e., almost matches
    the lower bound (also derived in this paper) on the competitive ratio of any (distributed
    or centralized) online algorithm.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Monika H
  full_name: Henzinger, Monika H
  id: 540c9bbd-f2de-11ec-812d-d04a5be85630
  last_name: Henzinger
  orcid: 0000-0002-5008-6530
- first_name: Stefan
  full_name: Neumann, Stefan
  last_name: Neumann
- first_name: Stefan
  full_name: Schmid, Stefan
  last_name: Schmid
citation:
  ama: 'Henzinger MH, Neumann S, Schmid S. Efficient distributed workload (re-)embedding.
    In: <i>SIGMETRICS’19: International Conference on Measurement and Modeling of
    Computer Systems</i>. Association for Computing Machinery; 2019:43–44. doi:<a
    href="https://doi.org/10.1145/3309697.3331503">10.1145/3309697.3331503</a>'
  apa: 'Henzinger, M. H., Neumann, S., &#38; Schmid, S. (2019). Efficient distributed
    workload (re-)embedding. In <i>SIGMETRICS’19: International Conference on Measurement
    and Modeling of Computer Systems</i> (pp. 43–44). Phoenix, AZ, United States:
    Association for Computing Machinery. <a href="https://doi.org/10.1145/3309697.3331503">https://doi.org/10.1145/3309697.3331503</a>'
  chicago: 'Henzinger, Monika H, Stefan Neumann, and Stefan Schmid. “Efficient Distributed
    Workload (Re-)Embedding.” In <i>SIGMETRICS’19: International Conference on Measurement
    and Modeling of Computer Systems</i>, 43–44. Association for Computing Machinery,
    2019. <a href="https://doi.org/10.1145/3309697.3331503">https://doi.org/10.1145/3309697.3331503</a>.'
  ieee: 'M. H. Henzinger, S. Neumann, and S. Schmid, “Efficient distributed workload
    (re-)embedding,” in <i>SIGMETRICS’19: International Conference on Measurement
    and Modeling of Computer Systems</i>, Phoenix, AZ, United States, 2019, pp. 43–44.'
  ista: 'Henzinger MH, Neumann S, Schmid S. 2019. Efficient distributed workload (re-)embedding.
    SIGMETRICS’19: International Conference on Measurement and Modeling of Computer
    Systems. SIGMETRICS: International Conference on Measurement and Modeling of Computer
    Systems, 43–44.'
  mla: 'Henzinger, Monika H., et al. “Efficient Distributed Workload (Re-)Embedding.”
    <i>SIGMETRICS’19: International Conference on Measurement and Modeling of Computer
    Systems</i>, Association for Computing Machinery, 2019, pp. 43–44, doi:<a href="https://doi.org/10.1145/3309697.3331503">10.1145/3309697.3331503</a>.'
  short: 'M.H. Henzinger, S. Neumann, S. Schmid, in:, SIGMETRICS’19: International
    Conference on Measurement and Modeling of Computer Systems, Association for Computing
    Machinery, 2019, pp. 43–44.'
conference:
  end_date: 2019-06-28
  location: Phoenix, AZ, United States
  name: 'SIGMETRICS: International Conference on Measurement and Modeling of Computer
    Systems'
  start_date: 2019-06-24
date_created: 2022-08-16T07:14:57Z
date_published: 2019-06-20T00:00:00Z
date_updated: 2023-02-17T09:41:45Z
day: '20'
doi: 10.1145/3309697.3331503
extern: '1'
external_id:
  arxiv:
  - '1904.05474'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1904.05474
month: '06'
oa: 1
oa_version: Preprint
page: 43–44
publication: 'SIGMETRICS''19: International Conference on Measurement and Modeling
  of Computer Systems'
publication_identifier:
  isbn:
  - 978-1-4503-6678-6
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: Efficient distributed workload (re-)embedding
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2019'
...