Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Networking and Internet Architecture

arXiv:2210.17227 (cs)
[Submitted on 31 Oct 2022 (v1), last revised 22 Dec 2022 (this version, v2)]

Title:Modelling M/M/R-JSQ-PS sojourn time distribution for Ultra-Reliable Low Latency Communication services

Authors:Geraint I. Palmer, Jorge Martín-Pérez
View PDF
Abstract:The future Internet promises to support time-sensitive services that require ultra low latencies and reliabilities of 99.99%. Recent advances in cellular and WiFi connections enhance the network to meet high reliability and ultra low latencies. However, the aforementioned services require that the server processing time ensures low latencies with high reliability, otherwise the end-to-end performance is not met. To that end, in this paper we use queuing theory to model the sojourn time distribution for Ultra-Reliable Low Latency Communication services of M/M/R-JSQ-PS systems: Markovian queues with R CPU servers following a join shortest queue processor-sharing discipline (for example Linux systems). We develop open-source simulation software, and develop and compare six analytical approximations for the sojourn time distribution. The proposed approximations yield Wasserstein distances below 2 time units, and upon medium loads incur into errors of less than 1.78 time units (e.g., milliseconds) for the 99.99th percentile sojourn time. Moreover, the proposed sojourn time approximations are stable regardless the number of CPUs and stay close to the simulations.
Comments: 14 Pages, 10 figures, submitted to Elsevier European Journal of Operational Research
Subjects: Networking and Internet Architecture (cs.NI); Optimization and Control (math.OC)
Cite as: arXiv:2210.17227 [cs.NI]
  (or arXiv:2210.17227v2 [cs.NI] for this version)
  https://doi.org/10.48550/arXiv.2210.17227

Submission history

From: Jorge Martín-Pérez [view email]
[v1] Mon, 31 Oct 2022 11:11:41 UTC (3,180 KB)
[v2] Thu, 22 Dec 2022 20:06:59 UTC (3,250 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
license icon view license
Current browse context:
cs.NI
< prev   |   next >
new | recent | 2022-10
Change to browse by:
cs
math
math.OC

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)