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Using Docker in Process Level Isolation for Heterogeneous Computing on GPU Accelerated On-Board Data Processing Systems
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-8096-3891
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-7586-0409
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-8785-5380
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The technological advancements make the intelligent on-board data processing possible on a small scale of satellites and deep-space exploration spacecraft such as CubeSats. However, the operation of satellites may fall into critical conditions when the on-board data processing interferes strongly to the basic operation functionalities of satellites. In order to avoid these issues, there exist techniques such as isolation, partitioning, and virtualization. In this paper, we present an experimental study of isolation of on-board payload data processing from the basic operations of satellites using Docker. Docker is a leading technology in process level isolation as well as continuous integration and continuous deployment (CI/CD) method. This study continues with the prior study on heterogeneous computing method, which improves the schedulability of the entire system up to 90%. Based on this heterogeneous computing method, the comparison study has been conducted between the non-isolated and isolated environments.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Process level isolation, Docker, On-board data processing, Heterogeneous computing, cgroups, Linux
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-45939OAI: oai:DiVA.org:mdh-45939DiVA, id: diva2:1369130
Conference
12th IAA Symposium on Small Satellites for Earth Observation, Berlin, Germany
Projects
DPAC - Dependable Platforms for Autonomous systems and ControlAvailable from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-12-13Bibliographically approved
In thesis
1. Improving On-Board Data Processing using CPU-GPU Heterogeneous Architectures for Real-Time Systems
Open this publication in new window or tab >>Improving On-Board Data Processing using CPU-GPU Heterogeneous Architectures for Real-Time Systems
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis investigates the efficacy of heterogeneous computing architectures in real-time systems.The goals of the thesis are twofold. First, to investigate various characteristics of the Heterogeneous System Architectures (HSA) compliant reference platforms focusing on computing performance and power consumption. The investigation is focused on the new technologies that could boost on-board data processing systems in satellites and spacecraft. Second, to enhance the usage of the heterogeneous processing units by introducing a technique for static allocation of parallel segments of tasks.

The investigation and experimental evaluation show that our method of GPU allocation for the parallel segments of tasks is more energy efficient compared to any other studied allocation. The investigation is conducted under different types of environments, such as process-level isolated environment, different software stacks, including kernels, and various task set scenarios. The evaluation results indicate that a balanced use of heterogeneous processing units (CPU and GPU) could improve schedulability of task sets up to 90% with the proposed allocation technique.

Abstract [sv]

Denna avhandling undersöker effektiviteten hos heterogena datorarkitekturer i realtidssystem. Målet med avhandlingen är tvåfaldigt. Till att börja med, att undersöka olika egenskaper hos plattformar baserade på Heterogeneous System Architecture, med fokus på datorprestanda och strömförbrukning. Undersökningen är inriktad på tekniker som kan öka datorbehandlingssystemen ombord i satelliter och rymdskepp. För det andra förbättra användningen av heterogena arkitekturer genom att införa en teknik för statisk allokering av parallella programsegment.

Undersökningen och den experimentella utvärderingen visar att vår metod för effektiv användning av GPU-allokering för parallella programsegment är den mest energieffektiva jämfört med någon annan studerad allokering. Undersökningarna har genomförts i olika typer av miljöer, såsom processisolerad miljö, olika mjukvarustackar, inklusive kernel, och olika uppsättningsscenarier. Utvärderingsresultaten indikerar dessutom att en balanserad användning av heterogena beräkningsenheter (CPU och GPU) kan förbättra schemaläggningen för vissa program upp till 90% jämfört med de tidigare föreslagna allokeringsteknikerna.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2019
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 286
Keywords
on-board data processing, CPU-GPU, heterogeneous architectures, real-time systems
National Category
Engineering and Technology Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-45940 (URN)978-91-7485-450-3 (ISBN)
Presentation
2019-12-18, Kappa, Mälardalens högskola, Västerås, 09:15 (English)
Opponent
Supervisors
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-19Bibliographically approved

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Tsog, NandinbaatarNolin, MikaelBruhn, Fredrik

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