AEGLE's Cloud Infrastructure for Resource Monitoring and Containerized Accelerated Analytics

Konstantina Koliogeorgi, Dimosthenis Masouros, Georgios Zervakis, Sotirios Xydis, Tobias Becker, Georgi Gaydadjiev, Dimitrios Soudris

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

This paper presents the cloud infrastructure of the AEGLE project, that targets to integrate cloud technologies together with heterogeneous reconfigurable computing in large scale healthcare systems for Big Bio-Data analytics. AEGLEs engineering concept brings together the hot big-data engines with emerging acceleration technologies, putting the basis for personalized and integrated health-care services, while also promoting related research activities. We introduce the design of AEGLE's accelerated infrastructure along with the corresponding software and hardware acceleration stacks to support various big data analytics workloads showing that through effective resource containerization AEGLE's cloud infrastructure is able to support high heterogeneity regarding to storage types, execution engines, utilized tools and execution platforms. Special care is given to the integration of high performance accelerators within the overall software stack of AEGLE's infrastructure, which enable efficient execution of analytics, up to 140× according to our preliminary evaluations, over pure software executions.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017
PublisherIEEE
Pages362-367
Number of pages6
Volume2017-July
ISBN (Electronic)9781509067626
DOIs
Publication statusPublished - 20 Jul 2017
Externally publishedYes
Event2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017 - Bochum, North Rhine-Westfalia, Germany
Duration: 3 Jul 20175 Jul 2017

Conference

Conference2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017
Country/TerritoryGermany
CityBochum, North Rhine-Westfalia
Period3/07/175/07/17

Keywords

  • Accelerated Analytics
  • Cloud Infrastructure
  • Resource Monitoring

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