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Publication:2017 Costa
Abstract MapReduce is a framework for processing la MapReduce is a framework for processing large data sets much used in the context of cloud computing. MapReduce implementations like Hadoop can tolerate crashes and file corruptions, but not arbitrary faults. Unfortunately, there is evidence that arbitrary faults do occur and can affect the correctness of MapReduce job executions. Furthermore, many outages of major cloud offerings have been reported, raising concerns about the dependence on a single cloud. In this paper we propose a novel execution system that allows to scale out MapReduce computations to a cloud-of-clouds, and tolerate arbitrary faults, malicious faults, and cloud outages. Our system, Chrysaor, is based on a fine-grained replication scheme that tolerates faults at the task level. Our solution has three important properties: it tolerates the above-mentioned classes of faults at reasonable cost; it requires minimal modifications to the users' applications; and it does not involve changes to the Hadoop source code. We performed an extensive evaluation of our system in Amazon EC2, showing that our fine-grained solution is efficient in terms of computation by recovering only faulty tasks. This is achieved without incurring a significant penalty for the baseline case (i.e., without faults) in most workloads. (i.e., without faults) in most workloads.
Author Pedro Costa + , Fernando Ramos + , Miguel Correia +
Booktitle IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)  +
Document Document for Publication-2017 Costa.pdf +
Key 2017 Costa  +
Month may  +
NumPubDate 2,017.05  +
Project Project:SUPERCLOUD +
ResearchLine Fault and Intrusion Tolerance in Open Distributed Systems (FIT) +
Title Chrysaor: Fine-Grained, Fault-Tolerant Cloud-of-Clouds MapReduce  +
Type inproceedings  +
Year 2017  +
Has improper value forThis property is a special property in this wiki. Url  +
Categories Publication  +
Modification dateThis property is a special property in this wiki. 18 January 2017 17:40:05  +
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