Guide to High Performance Distributed Computing: Case by K.G. Srinivasa, Anil Kumar Muppalla

By K.G. Srinivasa, Anil Kumar Muppalla

This well timed text/reference describes the improvement and implementation of large-scale disbursed processing structures utilizing open resource instruments and applied sciences. finished in scope, the booklet offers cutting-edge fabric on development excessive functionality dispensed computing platforms, delivering useful suggestions and top practices in addition to describing theoretical software program frameworks. beneficial properties: describes the basics of establishing scalable software program platforms for large-scale facts processing within the new paradigm of excessive functionality dispensed computing; provides an outline of the Hadoop atmosphere, via step by step guide on its set up, programming and execution; experiences the fundamentals of Spark, together with resilient disbursed datasets, and examines Hadoop streaming and dealing with Scalding; offers unique case reviews on ways to clustering, information class and regression research; Explains the method of making a operating recommender process utilizing Scalding and Spark.

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Additional info for Guide to High Performance Distributed Computing: Case Studies with Hadoop, Scalding and Spark

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Google, for example, bundles a range of business services under the banner Google Apps. This development is enabled by agreed standards for software services The term Cloud Computing is used to represent distributed computing as an utility. A cloud forms a blanket over a set a Internet based application, storage, and computing services enough to support most users’ needs. This allows the users to dispense their local storage and computing resources. The cloud also promotes the uses of any resource as a service be it software or hardware.

When the log becomes too large a checkpoint is created on the data in the main-memory so that it can be immediately restored back in the main-memory. Consequently, the I/O of the GFS cluster is reduced significantly. This organization allows a single master to control a few hundred chunk servers, which is a considerable size for a single cluster. By subsequently organizing a service such as Google into smaller services that are mapped onto clusters, it is not hard to imagine that a huge collection of clusters can be made to work together.

For example, it is convenient for users to control their wash- 26 1 Introduction ing machines or their entertainment systems from their phones. Similarly, washing machines can signal back to the user about the status of the wash. The user can use three forms of wireless communication for his needs. First, using wireless LAN that can extend up to 100m (a building floor). This LAN is connected to the Internet via an access point or gateway. Secondly, using mobile(cellular) telephone, which is connected to cellular towers, in-turn connected to the Internet.

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