liu.seSearch for publications in DiVA
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Implementing and Comparing Static and Machine-Learning scheduling Approaches using DPDK on an Integrated CPU/GPU
Linköpings universitet, Institutionen för datavetenskap, Programvara och system.
Linköpings universitet, Institutionen för datavetenskap, Programvara och system.
2019 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
Abstract [en]

As 5G is getting closer to being commercially available, base stations processing this traffic must be improved to be able to handle the increase in traffic and demand for lower latencies. By utilizing the hardware smarter, the processing of data can be accelerated in, for example, the forwarding plane where baseband and encryption are common tasks. With this in mind, systems with integrated GPUs becomes interesting for their additional processing power and lack of need for PCIe buses.This thesis aims to implement the DPDK framework on the Nvidia Jetson Xavier system and investigate if a scheduler based on the theoretical properties of each platform is better than a self-exploring machine learning scheduler based on packet latency and throughput, and how they stand against a simple round-robin scheduler. It will also examine if it is more beneficial to have a more flexible scheduler with more overhead than a more static scheduler with less overhead. The conclusion drawn from this is that there are a number of challenges for processing and scheduling on an integrated system. Effective batch aggregation during low traffic rates and how different processes affect each other became the main challenges.

sted, utgiver, år, opplag, sider
2019. , s. 72
Emneord [en]
GPU, Jetson Xavier, DPDK
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-162295ISRN: 2019 | LIU-IDA/LITH-EX-A--19/092--SEOAI: oai:DiVA.org:liu-162295DiVA, id: diva2:1373336
Eksternt samarbeid
Ericsson
Fag / kurs
Information Technology
Presentation
2019-11-11, Alan Turing, 581 83 Linköping, Linköping, 19:17 (engelsk)
Veileder
Examiner
Tilgjengelig fra: 2019-11-29 Laget: 2019-11-26 Sist oppdatert: 2019-11-29bibliografisk kontrollert

Open Access i DiVA

fulltext(2088 kB)45 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 2088 kBChecksum SHA-512
9805a609529236e236cb489c0f30caea54f0b0cc6e679f1eca55b5ff1448baeea736c67534b8237d9241d1508100f871a12e54e68847a980657566960eb2fef9
Type fulltextMimetype application/pdf

Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 45 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 806 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf