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Abstract MapReduce is a popular distributed data-pr MapReduce is a popular distributed data-processing system for analyzing big data in cloud environments. This platform is often used for critical data processing, e.g., in the context of scientific or financial simulation. Unfortunately, there is accumulating evidence of severe problems – including arbitrary faults and cloud outages – affecting the services that run atop cloud services. Faced with this challenge, we have recently explored multicloud solutions to increase the resilience and availability of MapReduce. Based on this experience, we present system design guidelines that allow to scale out MapReduce computation to multiple clouds in order to tolerate arbitrary and malicious faults, as well as cloud outages. Crucially, the techniques we introduce have reasonable cost and do not require changes to MapReduce or to the users’ code, enabling immediate deployment. sers’ code, enabling immediate deployment.
Author Pedro Costa + , Miguel Correia + , Fernando Ramos +
Document Document for Publication-Costa2017.pdf +
Journal IEEE Cloud Computing  +
Key Costa2017  +
NumPubDate 2,017  +
Project Project:SUPERCLOUD +
ResearchLine Fault and Intrusion Tolerance in Open Distributed Systems (FIT) +
Title On the Design of Resilient Multicloud MapReduce  +
Type article  +
Year 2017  +
Has improper value forThis property is a special property in this wiki. Month  + , Url  +
Categories Publication  +
Modification dateThis property is a special property in this wiki. 22 June 2017 09:07:20  +
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