Nebula: Distributed edge cloud for data intensive computing

Mathew Ryden, Kwangsung Oh, Abhishek Chandra, Jon Weissman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

93 Scopus citations

Abstract

Centralized cloud infrastructures have become the de-facto platform for data-intensive computing today. However, they suffer from inefficient data mobility due to the centralization of cloud resources, and hence, are highly unsuited for dispersed-data-intensive applications, where the data may be spread at multiple geographical locations. In this paper, we present Nebula: a dispersed cloud infrastructure that uses voluntary edge resources for both computation and data storage. We describe the lightweight Nebula architecture that enables distributed data-intensive computing through a number of optimizations including location-aware data and computation placement, replication, and recovery. We evaluate Nebula's performance on an emulated volunteer platform that spans over 50 PlanetLab nodes distributed across Europe, and show how a common data-intensive computing framework, MapReduce, can be easily deployed and run on Nebula. We show Nebula MapReduce is robust to a wide array of failures and substantially outperforms other wide-area versions based on a BOINC like model.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE International Conference on Cloud Engineering, IC2E 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages57-66
Number of pages10
ISBN (Electronic)9781479937660
DOIs
StatePublished - Sep 18 2014
Event2nd IEEE International Conference on Cloud Engineering, IC2E 2014 - Boston, United States
Duration: Mar 10 2014Mar 14 2014

Publication series

NameProceedings - 2014 IEEE International Conference on Cloud Engineering, IC2E 2014

Other

Other2nd IEEE International Conference on Cloud Engineering, IC2E 2014
Country/TerritoryUnited States
CityBoston
Period3/10/143/14/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

  • Cloud programming models and tools
  • Data Intensive
  • Edge
  • Geo-distributed
  • MapReduce
  • Voluntary

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