Implementation of Big-Data Application Using the MapReduce Framework

Authors: Niesh Jaiswa, Prof. Mayank Bhatt

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Abstract

In cloud computing, data is moved to a remotely located cloud server. Cloud server faithfully stores the data and return back to the owner whenever needed. Data and computation integrity and security are major concerns for users of cloud computing facilities. Today\'s clouds typically place centralized, universal trust in all the cloud\'s nodes.Hadoop is founded on MapReduce, which is among the most popular programming items for huge knowledge analysis in a parallel computing environment. In this paper, we reward a particular efficiency analysis, characterization, and evaluation of Hadoop MapReduce WordCount utility.

Introduction

Yesteryear decade features seen your rise regarding cloud calculating [1], an arrangement where businesses in addition to individual users utilize hardware, storage space, and software program of 3rd party companies named cloud providers rather than running their very own computing commercial infrastructure. Cloud calculating offers customers the illusion of needing infinite calculating resources, of which they can use all the or less than they have to have, without being forced to concern themselves with exactly how those resources are offered or maintained [2]. The derivation of big knowledge is indistinct and there are a lot of definitions on huge data. For examples, Matt Aslett outlined massive knowledge as “tremendous data is now virtually universally understood to refer to the recognition of larger business intelligence through storing, processing, and examining data that was previously ignored because of problem of normal data management applied sciences” [5]. Recently, the term of giant data has got a brilliant momentum from governments, industry and research communities. In [6], significant information is outlined as a term that encompasses using tactics to capture, approach, analyze and visualize potentially significant datasets in a cheap timeframe now not obtainable to usual IT applied sciences. The figure below would throw more light to your understanding.

Conclusion

Map-Reduce, proposed in this paper provides an online, on-demand and closed-loop solution to managing these faults. The control loop in word count mitigates performance penalties through early detection of anomalous conditions on slave nodes. Anomaly detection is performed through a novel sparse-coding based method that achieves high true positive and true negative rates and can be trained using only normal class (or anomaly-free) data. The local, decentralized nature of the sparsecoding models ensures minimal computational overhead and enables usage in both homogeneous