A Study on Big Data Hadoop Map Reduce Job Scheduling (original) (raw)

A Survey on Hadoop-Mapreduce Environment with Scheduling Algorithms in Big Data

Hadoop and Map Reduce are the most efficient tools which are used to reduce the complexity of maintaining the big data set. MapReduce has been introduced by Google and it is an open source counterpart. Hadoop is focused for parallelizing computing in large distributed clusters of commodity machines. Thus the parallelizing data processing tool MapReduce has been gaining significance moment from both academy and industries. The objective of this survey is to study MapReduce with different algorithms to improve the performance in large dataset.

A Survey on Comparison of Job Scheduling Algorithms in Hadoop

2016

Cloud computing is emerging as a new trend to store and analyze data, Hadoop is a very good and efficient framework that can be used to process the data through map reduce programming paradigm. This paper provides the insight for the rise of big data and what role does the hadoop plays to handle this. The structure and architecture view of hadoop with all the components and Yarn architecture is discussed. Scheduling is allot the set of jobs to process in the corresponding slots based on the few job scheduling algorithms, comparative study of all the scheduling algorithms are made which would be helpful while processing the data.

A Survey on Job and Task Scheduling in Big Data

Bigdata handles the datasets which exceeds the ability of commonly used software tools for storing, sharing and processing the data. Classification of workload is a major issue to the Big Data community namely job type evolution and job size evolution. On the basis of job type, job size and disk performance, clusters are been formed with data node, name node and secondary name node. To classify the workload and to perform the job scheduling, mapreduce algorithm is going to be applied. Based on the performance of individual machine, workload has been allocated. Mapreduce has two phases for processing the data: map and reduce phases. In map phase, the input dataset taken is splitted into keyvalue pairs and an intermediate output is obtained and in reduce phase that key value pair undergoes shuffle and sort operation. Intermediate files are created from map tasks are written to local disk and output files are written to distributed file system of Hadoop. Scheduling of different jobs to different disks are identified after completing mapreduce tasks. Johnson algorithm is used to schedule the jobs and used to find out the optimal solution of different jobs. It schedules the jobs into different pools and performs the scheduling. The main task to be carried out is to minimize the computation time for entire jobs and analyze the performance using response time factors in hadoop distributed file system. Based on the dataset size and number of nodes which is formed in hadoop cluster, the performance of individual jobs are identified Keywords-hadoop; mapreduce; johnson algorithm;

Big Data Analysis and Its Scheduling Policy – Hadoop

This paper is deals with Parallel Distributed system. Hadoop has become a central platform to store big data through its Hadoop Distributed File System (HDFS) as well as to run analytics on this stored big data using its MapReduce component. Map Reduce programming model have shown great value in processing huge amount of data. Map Reduce is a common framework for data-intensive distributed computing of batch jobs. Hadoop Distributed File System (HDFS) is a Java-based file system that provides scalable and reliable data storage that is designed to span large clusters of commodity servers. In all Hadoop implementations, the default FIFO scheduler is available where jobs are scheduled in FIFO order with support for other priority based schedulers also. During this paper, we are going to study a Hadoop framework, HDFS design and Map reduce Programming model. And also various schedulers possible with Hadoop and provided some behavior of the current scheduling schemes in Hadoop on a locally deployed cluster is described.

A Survey on Job Scheduling in Big Data

Big Data Applications with Scheduling becomes an active research area in last three years. The Hadoop framework becomes very popular and most used frameworks in a distributed data processing. Hadoop is also open source software that allows the user to effectively utilize the hardware. Various scheduling algorithms of the MapReduce model using Hadoop vary with design and behavior, and are used for handling many issues like data locality, awareness with resource, energy and time. This paper gives the outline of job scheduling, classification of the scheduler, and comparison of different existing algorithms with advantages, drawbacks, limitations. In this paper, we discussed various tools and frameworks used for monitoring and the ways to improve the performance in MapReduce. This paper helps the beginners and researchers in understanding the scheduling mechanisms used in Big Data.

Survey on Data Processing and Scheduling in Hadoop

International Journal of Computer Applications, 2015

There is an explosion in the volume of data in the world. The amount of data is increasing by leaps and bounds. The sources are individuals, social media, organizations, etc. The data may be structured, semi-structured or unstructured. Gaining knowledge from this data and using it for competitive advantage is the primary focus of all the organizations. In the last few years Big Data has found its way in almost every field, from government to private sectors, industry to academia. The major challenges associated with Big Data are data organization, modeling, data analysis and retrieval. Hadoop is a widely used software framework used for the large scale management and analysis of data. The main components of Hadoop: HDFS and MapReduce, enable the distributed storage and processing of data over a large number of commodity servers. This paper provides an overview of MapReduce and its capabilities and discusses the related issues.

Empirical Study of Job Scheduling Algorithms in Hadoop MapReduce

Cybernetics and Information Technologies, 2017

Several Job scheduling algorithms have been developed for Hadoop-Map Reduce model, which vary widely in design and behavior for handling different issues such as locality of data, user share fairness, and resource awareness. This article focuses on empirically evaluating the performance of three schedulers: First In First Out (FIFO), Fair scheduler, and Capacity scheduler. To carry out the experimental evaluation, we implement our own Hadoop cluster testbed, consisting of four machines, in which one of the machines works as the master node and all four machines work as slave nodes. The experiments include variation in data sizes, use of two different data processing applications, and variation in the number of nodes used in processing. The article analyzes the performance of the job scheduling algorithms based on various relevant performance measures. The results of the experiments are evident of the performance being affected by the job scheduling parameters, the type of applicatio...

An efficient Mapreduce scheduling algorithm in hadoop

Abstract— Hadoop is a free java based programming framework that supports the processing of large datasets in a distributed computing environment. Mapreduce technique is being used in hadoop for processing and generating large datasets with a parallel distributed algorithm on a cluster. A key benefit of mapreduce is that it automatically handles failures and hides the complexity of fault tolerance from the user. Hadoop uses FIFO (FIRST IN FIRST OUT) scheduling algorithm as default in which the jobs are executed in the order of their arrival. This method suits well for homogeneous cloud and results in poor performance on heterogeneous cloud. Later the LATE (Longest Approximate Time to End) algorithm has been developed which reduces the FIFO's response time by a factor of 2.It gives better performance in heterogenous environment. LATE algorithm is based on three principles i) prioritising tasks to speculate ii) selecting fast nodes to run on iii)capping speculative tasks to prevent thrashing. It takes action on appropriate slow tasks and it could not compute the remaining time for tasks correctly and can't find the real slow tasks. Finally a SAMR (Self Adaptive MapReduce) scheduling algorithm is being introduced which can find slow tasks dynamically by using the historical information recorded on each node to tune parameters. SAMR reduces the execution time by 25% when compared with FIFO and 14% when compared with LATE.

Efficient Processing of Job by Enhancing Hadoop Mapreduce Framework

International Journal of Advanced Research in Computer Science

Cloud Computing uses Hadoop framework for processing BigData in parallel. The Hadoop Map Reduce programming paradigm used in the context of Big Data, is one of the popular approaches that abstract the characterstics of parallel and distributed computing which comes off as a solution to Big Data. Improving performance of Map Reduce is a major concern as it affects the energy efficiency. Improving the energy efficiency of Map Reduce will have significant impact on energy savings for data centers. There are many parameters that influence the performance of Map Reduce. Various parameters like scheduling, resource allocation and data flow have a significant impact on Map Reduce performance. Cloud Computing leverages Hadoop framework for processing BigData in parallel. Hadoop has certain limitations that could be exploited to execute the job efficiently. Efficient resource allocation remains a challenge in Cloud Computing MapReduce platforms. We propose a methodology which is an enhanced Hadoop architecture that reduces the computation cost associated with BigData analysis.

Comparative study of Job Schedulers in Hadoop Environment

International Journal of Advanced Research in Computer Science, 2017

Hadoop is a structure for BigData handling in distributed applications. Hadoop bunch is worked for running information intensive distributed applications. Hadoop distributed file system is the essential stockpiling territory for BigData. MapReduce is a model to total undertakings of a job. Task assignment is conceivable by schedulers. Schedulers ensure the reasonable assignment of assets among clients. At the point when a client presents a job, it will move to a job queue. From the job queue, job will be divided into tasks and distributed to various nodes. By the correct assignment of tasks, job finish time will decrease. This can guarantee better execution of the job. This paper gives the comparison of different Hadoop Job Schedulers. Keywords: Hadoop, HDFS, MapReduce, Scheduling, FIFO Scheduling, Fair Scheduling, Capacity Scheduling