CCS (Cluster Computing System) is coming to solve the problems of standard technology. Whose, objective is to improve the performance/power efficiency of a single processor for storing and mining the large data sets, using the parallel programming to read and process the massive data sets on multiple disks and CPUs. The thing which makes these systems somewhat performant than the standard technology is the physical organization of computing nodes in the cluster. Currently, this kind of cluster does not entirely solve the problem because it comes with its challenges, which are Node failures, Computations, Network Bottleneck, and Distributed programming. All these problems are coming when we are mining and storing the massive volume of data using cluster computing. To solve these challenges, Google invented a new Big Data framework of data processing called MapReduce, to manage large scale data processing across large clusters of commodity servers. The paper outlines the running of CCS and presents its challenges in this era of Big Data. Moreover, it introduces the most popular Big Data solutions proposed to overcome the CCS challenges. Also, it shows how Big Data technologies solve CCS issues. Generally, the main goal of this work is to provide a better understanding of the challenges of CCS and identify the essential big data solutions in this increasingly important area.

Big Data Solutions Proposed for Cluster Computing Systems Challenges: A survey

Hadoop is an open-source framework written by java and used for bigdata processing. It consists of two main components: HadoopDistributed File System (HDFS) and MapReduce. HDFS is used tostore data while MapReduce is used to distribute and process anapplication tasks in a distributed processing form. Recently, severalresearchers employ Hadoop for processing big data. The resultsindicate that Hadoop performs well with Large Files (files larger thanData Node block size). Nevertheless, Hadoop performance decreaseswith small files that are less than its block size. This is because, smallfiles consume the memory of both the DataNode and the NameNode,and increases the execution time of the applications (i.e. decreasesMapReduce performance). In this paper, the problem of the small filesin Hadoop is defined and the existing approaches to solve this problemare classified and discussed. In addition, some open points that mustbe considered when thinking of a better approach to improve theHadoop performance when processing the small files.

https://mjeer.journals.ekb.eg/article_62728_c818f3f951476c6005647f9ba7364efd.pdf

Impact of Small Files on Hadoop Performance: Literature Survey and Open Points

Hadoop Distributed File System (HDFS) is developed to efficiently store and handle the vast quantity of files in a distributed environment over a cluster of computers. Various commodity hardware fo...

Performance Study on Indexing and Accessing of Small File in Hadoop Distributed File System

Hadoop is a distributed computing framework written in Java and used to deal with big data; it is designed to handle large files. Handling the small files leads to some problems in Hadoop performance. Several approaches are used to overcome the housing and the access efficiency problems of the small files in Hadoop. These approaches are Hadoop Archive Files, Federated NameNodes, Batch file consolidation, Sequence files, HBase, and S3DistCp. All these approaches have some advantages, but they have some limitations as the slow in converting all the HDFS Files to Sequence Files and the lack of correlations between the files stored in the Sequence Files. In this paper, we proposed an enhancement of the Sequence files approach called Small Files Search and Aggregation Node (SFSAN) approach. Our proposed approach improves the Hadoop performance by overcoming some of the limitations of the Sequence Files approach and keeping up its advantages.

SFSAN Approach for Solving the Problem of Small Files in Hadoop

There are various challenges in healthcare sector one of them is medication Error. Impact of this medication error reflects on the patient's health and quality of care. This paper is going to present a clinical decision support system based on Evidence based treatment to reduce the medication errors during writing prescription. System is built on Hive. According to the analysis of Patient's medical history and current evidences, required drug is suggested. This system is helpful for doctors as well as patients to get information about a particular drug.

Reduction of medication errors while prescribing using evidence based treatment

Hadoop deals with big data which is an open source Java framework. There are two core components in it namely: HDFS (Hadoop distributed file system) is the ability of a system to continue normal operation against hardware or software faults using inexpensive hardware and which stocks huge extent of data another one is MapReduce is a processing technique and programming model done in lateral and scattered manner. Hadoop does not perform well for short data because huge amount of short data could be greater task on the NameNode of HDFS which inturn its execution time is prolonged for which MapReduce is encountered. While dealing with great amount of short data as it is particularly designed to handle huge amount of data, hadoop experienced with a performance cost. This analysis permits the indetail description of HDFS, actual ways to deal with the problems along with proposed approach to handle short data files and short data file problems. In proposed approach, small files are merged using programming model on hadoop known as MapReduce. By this approach of Hadoop performance of handling small files which is larger than block size is improved. We also propose a Traffic analyzer with the combination of Hadoop and Map-Reduce paradigm. The joint of Hadoop and MapReduce programming tools makes it possible to provide batch analysis in minimum response time and in memory computing capacity in order to process log in a high available, efficient and stable way.

A Big Data MapReduce Hadoop distribution architecture for processing input splits to solve the small data problem

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A Big Data MapReduce Hadoop distribution architecture for processing input splits to solve the small data problem