Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Implementing and Comparing Static and Machine-Learning scheduling Approaches using DPDK on an Integrated CPU/GPU
Linköping University, Department of Computer and Information Science, Software and Systems.
Linköping University, Department of Computer and Information Science, Software and Systems.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
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.

Place, publisher, year, edition, pages
2019. , p. 72
Keywords [en]
GPU, Jetson Xavier, DPDK
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-162295ISRN: 2019 | LIU-IDA/LITH-EX-A--19/092--SEOAI: oai:DiVA.org:liu-162295DiVA, id: diva2:1373336
External cooperation
Ericsson
Subject / course
Information Technology
Presentation
2019-11-11, Alan Turing, 581 83 Linköping, Linköping, 19:17 (English)
Supervisors
Examiners
Available from: 2019-11-29 Created: 2019-11-26 Last updated: 2019-11-29Bibliographically approved

Open Access in DiVA

fulltext(2088 kB)29 downloads
File information
File name FULLTEXT01.pdfFile size 2088 kBChecksum SHA-512
9805a609529236e236cb489c0f30caea54f0b0cc6e679f1eca55b5ff1448baeea736c67534b8237d9241d1508100f871a12e54e68847a980657566960eb2fef9
Type fulltextMimetype application/pdf

By organisation
Software and Systems
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 29 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 233 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf