Nebula: Distributed Edge Cloud for Data Intensive Computing - 2017


Centralized cloud infrastructures have become the popular platforms for information-intensive computing today. But, they suffer from inefficient data mobility because of the centralization of cloud resources, and hence, are highly unsuited for geo-distributed knowledge-intensive applications where the data might be spread at multiple geographical locations. During this paper, we have a tendency to present Nebula: a dispersed edge cloud infrastructure that explores the employment of voluntary resources for both computation and information storage. We describe the light-weight Nebula design that allows distributed information-intensive computing through a number of optimization techniques including location-aware information and computation placement, replication, and recovery. We have a tendency to evaluate Nebula performance on an emulated volunteer platform that spans over fifty PlanetLab nodes distributed across Europe, and show how a typical knowledge-intensive computing framework, MapReduce, can be simply deployed and run on Nebula. We show Nebula MapReduce is robust to a big range of failures and substantially outperforms alternative wide-area versions primarily based on emulated existing systems.

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