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h-MapReduce: A Framework for Workload Balancing in MapReduce
2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), 2013The big data analytics community has accepted MapReduce as a programming model for processing massive data on distributed systems such as a Hadoop cluster. MapReduce has been evolving to improve its performance. We identified skewed workload among workers in the MapReduce ecosystem.
Xiaowei Xu +2 more
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Assessing MapReduce for Internet Computing: A Comparison of Hadoop and BitDew-MapReduce
2012 ACM/IEEE 13th International Conference on Grid Computing, 2012MapReduce is emerging as an important programming model for data-intensive application. Adapting this model to desktop grid would allow taking advantage of the vast amount of computing power and distributed storage to execute new range of application able to process enormous amount of data.
Lu, Lu +3 more
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Proceedings of the 2nd IKDD Conference on Data Sciences, 2015
We begin with a sketch of how MapReduce works and how MapReduce algorithms differ from general parallel algorithms. While algorithm analysis usually centers on the serial or parallel running time of the algorithms that solve a given problem, in the MapReduce world, the critical issue is a tradeoff between interprocessor communication and the parallel ...
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We begin with a sketch of how MapReduce works and how MapReduce algorithms differ from general parallel algorithms. While algorithm analysis usually centers on the serial or parallel running time of the algorithms that solve a given problem, in the MapReduce world, the critical issue is a tradeoff between interprocessor communication and the parallel ...
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Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation, 2016
By abstracting away the complexity of distributed systems, large-scale data processing platforms—MapReduce, Hadoop, Spark, Dryad, etc.—have provided developers with simple means for harnessing the power of the cloud. In this paper, we ask whether we can automatically synthesize MapReduce-style distributed programs from input–output examples.
SmithCalvin, AlbarghouthiAws
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By abstracting away the complexity of distributed systems, large-scale data processing platforms—MapReduce, Hadoop, Spark, Dryad, etc.—have provided developers with simple means for harnessing the power of the cloud. In this paper, we ask whether we can automatically synthesize MapReduce-style distributed programs from input–output examples.
SmithCalvin, AlbarghouthiAws
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Correlation clustering in MapReduce
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014Correlation clustering is a basic primitive in data miner's toolkit with applications ranging from entity matching to social network analysis. The goal in correlation clustering is, given a graph with signed edges, partition the nodes into clusters to minimize the number of disagreements.
CHIERICHETTI, FLAVIO +2 more
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IEEE Internet Computing, 2017
Don't throw the MapReduce baby out with the bath water! MapReduce represents a specific instance of a general class of data-parallel dataflow languages, in which computations are conceptualized as directed graphs, where vertices represent operations on records that flow along the directed edges.
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Don't throw the MapReduce baby out with the bath water! MapReduce represents a specific instance of a general class of data-parallel dataflow languages, in which computations are conceptualized as directed graphs, where vertices represent operations on records that flow along the directed edges.
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Proceedings of the second international workshop on MapReduce and its applications, 2011
The MapReduce model of computation and systems that realize the model have simplified large-scale data processing. Recently, Google introduced other models of computation and systems to simplify data processing for a broader class of important computations.
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The MapReduce model of computation and systems that realize the model have simplified large-scale data processing. Recently, Google introduced other models of computation and systems to simplify data processing for a broader class of important computations.
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2016
In this chapter we consider situations in which a single host computer is inadequate because the data volume or processing demand exceeds the capacity of the host. A popular solution distributes the data and computations across a network of computers or a short-lived network created for the task (a cluster).
Brian Steele +2 more
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In this chapter we consider situations in which a single host computer is inadequate because the data volume or processing demand exceeds the capacity of the host. A popular solution distributes the data and computations across a network of computers or a short-lived network created for the task (a cluster).
Brian Steele +2 more
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Proceedings of the 22nd International Conference on World Wide Web, 2013
JSMapReduce is an implementation of MapReduce which exploits the computing power available in the computers of the users of a web platform by giving tasks to the JavaScript engines of their web browsers. This article describes the implementation of JSMapReduce exploiting HTML 5 features, the heuristics it uses for distributing tasks to workers, and ...
Philipp Langhans +2 more
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JSMapReduce is an implementation of MapReduce which exploits the computing power available in the computers of the users of a web platform by giving tasks to the JavaScript engines of their web browsers. This article describes the implementation of JSMapReduce exploiting HTML 5 features, the heuristics it uses for distributing tasks to workers, and ...
Philipp Langhans +2 more
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Fault Tolerance in MapReduce: A Survey
2016MapReduce-based systems have emerged as a prominent framework for large-scale data analysis, having fault tolerance as one of its key features. MapReduce has introduced simple yet efficient mechanisms to handle different kinds of failures including crashes, omissions, and arbitrary failures.
Memishi, Bunjamin +3 more
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