Nebula: Distributed Edge Cloud for Data Intensive Computing

Albert Jonathan, Mathew Ryden, Kwangsung Oh, Abhishek Chandra, Jon Weissman

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


Centralized cloud infrastructures have become the popular platforms 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 geo-distributed data-intensive applications where the data may be spread at multiple geographical locations. In this paper, we present Nebula: a dispersed edge cloud infrastructure that explores the use of voluntary resources for both computation and data storage. We describe the lightweight Nebula architecture that enables distributed data-intensive computing through a number of optimization techniques including location-aware data and computation placement, replication, and recovery. We evaluate Nebula 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 emulated existing systems.

Original languageEnglish (US)
Article number7954728
Pages (from-to)3229-3242
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number11
StatePublished - Nov 1 2017

Bibliographical note

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  • Distributed Systems
  • cloud computing
  • data intensive computing
  • edge cloud


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