OSM PoC 5 - Placement of Workloads in Distributed Cloud Networks: Difference between revisions

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Arctos Labs, Telenor, Wind River, Netrounds
Arctos Labs, Telenor, Wind River, Netrounds


''All PoC Team Members are OSM members or participants''
''All PoC Team Members are OSM members or participants''  


Main Contact: Mats Eriksson (Arctos Labs) [mailto:mats.eriksson@ARCTOSLABS.COM mats.eriksson@ARCTOSLABS.COM]
* Arctos Labs: Mats Eriksson [mailto:mats.eriksson@ARCTOSLABS.COM mats.eriksson@ARCTOSLABS.COM] - Main Contact
* Telenor: Min Xie [mailto:min.xie@telenor.com min.xie@telenor.com]
* Wind River: Masoud Fatollahy [mailto:Masoud.Fatollahy@windriver.com Masoud.Fatollahy@windriver.com]
* Netrounds: Marcus Friman [mailto:marcus.friman@netrounds.com marcus.friman@netrounds.com]


= Abstract =
= Abstract =
This PoC aims to achieve the automated optimization of VNF placement using constraint and cost models at the required latency and lowest possible cost.
This PoC demonstrates a solution that can optimize workload placement over a distributed and interconnected multi-cloud (e.g. edge). The optimization takes technical service constraints into account whilst at the same time finding the most cost optimal placement for each VNF in the service chain, including costs related to their interconnection over a backbone


= PoC Key Takeaways =
= PoC Key Takeaways =
Constraint models complementing NSDs in order to capture service performance requirements:
Constraint models complementing NSDs in order to capture service performance requirements:
Placement of VNF workloads based on latency requirements
* Placement of VNF workloads based on latency requirements
Placement decisions using real-time latency measurements
* Placement decisions using real-time latency measurements
Placement optimization using cost models to predict link and compute costs
* Placement optimization using cost models to predict link and compute costs
Placement optimization assurance to continuously re-evaluate in case of DC or link failures
* Placement optimization assurance to continuously re-evaluate in case of DC or link failures


= PoC Proposal =
= PoC Proposal =
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= OSM Components =
= OSM Components =
* OSM Rel FIVE
* OSM Rel FOUR


= Demos =
= Demos =
TBD
* EdgeCloudSummit, Paris, 4-6 June 2019


'''Would you like to see your OSM PoC listed in this wiki? Please follow the instructions in the [[OSM PoC Framework]]'''
 
 
 
'''''Would you like to see your OSM PoC listed in this wiki? Please follow the instructions in the [[OSM PoC Framework]]'''''

Latest revision as of 09:54, 13 March 2019

PoC Team Members

Arctos Labs, Telenor, Wind River, Netrounds

All PoC Team Members are OSM members or participants

Abstract

This PoC demonstrates a solution that can optimize workload placement over a distributed and interconnected multi-cloud (e.g. edge). The optimization takes technical service constraints into account whilst at the same time finding the most cost optimal placement for each VNF in the service chain, including costs related to their interconnection over a backbone

PoC Key Takeaways

Constraint models complementing NSDs in order to capture service performance requirements:

  • Placement of VNF workloads based on latency requirements
  • Placement decisions using real-time latency measurements
  • Placement optimization using cost models to predict link and compute costs
  • Placement optimization assurance to continuously re-evaluate in case of DC or link failures

PoC Proposal

PoC Report

OSM Components

  • OSM Rel FOUR

Demos

  • EdgeCloudSummit, Paris, 4-6 June 2019



Would you like to see your OSM PoC listed in this wiki? Please follow the instructions in the OSM PoC Framework