On the feasibility of byzantine fault-tolerant mapreduce in clouds-of-clouds

Miguel Correia; Pedro Costa; Marcelo Pasin; Alysson Bessani; Fernando Ramos; Paulo Verissimo

doi:10.1109/SRDS.2012.46

On the feasibility of byzantine fault-tolerant mapreduce in clouds-of-clouds

Miguel Correia, Pedro Costa, Marcelo Pasin, Alysson Bessani, Fernando Ramos, Paulo Verissimo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Scopus citations

Abstract

MapReduce is a framework for processing large data sets largely used in cloud computing. MapReduce implementations like Hadoop can tolerate crashes and file corruptions, but there is evidence that general arbitrary faults do occur and can affect the correctness of job executions. Furthermore, many individual cloud outages have been reported, raising concerns about depending on a single cloud. We present a MapReduce runtime that tolerates arbitrary faults and runs in a set of clouds at a reasonable cost in terms of computation and execution time. The main challenge is to avoid sending through the internet the huge amount of data that would normally be exchanged between map and reduce tasks. © 2012 IEEE.

Original language	English (US)
Title of host publication	Proceedings of the IEEE Symposium on Reliable Distributed Systems
Pages	448-453
Number of pages	6
DOIs	https://doi.org/10.1109/SRDS.2012.46
State	Published - Dec 1 2012
Externally published	Yes

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Generated from Scopus record by KAUST IRTS on 2021-03-16

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10.1109/SRDS.2012.46

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title = "On the feasibility of byzantine fault-tolerant mapreduce in clouds-of-clouds",

abstract = "MapReduce is a framework for processing large data sets largely used in cloud computing. MapReduce implementations like Hadoop can tolerate crashes and file corruptions, but there is evidence that general arbitrary faults do occur and can affect the correctness of job executions. Furthermore, many individual cloud outages have been reported, raising concerns about depending on a single cloud. We present a MapReduce runtime that tolerates arbitrary faults and runs in a set of clouds at a reasonable cost in terms of computation and execution time. The main challenge is to avoid sending through the internet the huge amount of data that would normally be exchanged between map and reduce tasks. {\textcopyright} 2012 IEEE.",

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AU - Correia, Miguel

AU - Costa, Pedro

AU - Pasin, Marcelo

AU - Bessani, Alysson

AU - Ramos, Fernando

AU - Verissimo, Paulo

N1 - Generated from Scopus record by KAUST IRTS on 2021-03-16

PY - 2012/12/1

Y1 - 2012/12/1

N2 - MapReduce is a framework for processing large data sets largely used in cloud computing. MapReduce implementations like Hadoop can tolerate crashes and file corruptions, but there is evidence that general arbitrary faults do occur and can affect the correctness of job executions. Furthermore, many individual cloud outages have been reported, raising concerns about depending on a single cloud. We present a MapReduce runtime that tolerates arbitrary faults and runs in a set of clouds at a reasonable cost in terms of computation and execution time. The main challenge is to avoid sending through the internet the huge amount of data that would normally be exchanged between map and reduce tasks. © 2012 IEEE.

AB - MapReduce is a framework for processing large data sets largely used in cloud computing. MapReduce implementations like Hadoop can tolerate crashes and file corruptions, but there is evidence that general arbitrary faults do occur and can affect the correctness of job executions. Furthermore, many individual cloud outages have been reported, raising concerns about depending on a single cloud. We present a MapReduce runtime that tolerates arbitrary faults and runs in a set of clouds at a reasonable cost in terms of computation and execution time. The main challenge is to avoid sending through the internet the huge amount of data that would normally be exchanged between map and reduce tasks. © 2012 IEEE.

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DO - 10.1109/SRDS.2012.46

M3 - Conference contribution

SP - 448

EP - 453

BT - Proceedings of the IEEE Symposium on Reliable Distributed Systems

ER -

On the feasibility of byzantine fault-tolerant mapreduce in clouds-of-clouds

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