Research Paper on Big Data Hadoop Map Reduce Job Scheduling

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1 Research Paper on Big Data Hadoop Map Reduce Job Scheduling N. Deshai 1, S. Venkataramana 2, Dr. G. P. Saradhi Varma 3 1, 2, 3 Department of Information Technology, S.R.K.R, JNTUK, India ABSTRACT: The era of giga-peta byte information figuring is turning into a reality in all clients' everyday life called Big Data. Because the intensive use of data turns into a truth in a number of scientific areas, for finding an effective plan for huge data computing systems has to turn into a multi-objective development. Processes of these huge data in distributing hardware clusters as clouds, so you need a powerful computing model like Hadoop and Map Reduce. In this Research paper, we have contemplated a few schedulers in Apache Hadoop open source in cloud condition, highlights and issues. the Most examinations have considered execution change from a solitary perspective (area information requesting, dataset precision, and so on.) however next to no understood writing in multi-target enhancements ( quality necessities, element arranging and dynamic natural adjustment), particularly in parallel and dispersed heterogeneous frameworks. Like Apache, Hadoop has two important aspects of Big Data: HDFS and MapReduce, for managing (Structured) ordered data and unstructured data. To design an algorithm is a key factor for selecting nodes are important, to optimize and acquire high performance in Big data. This papers provides a valuable overview of the work done on Hadoop Map Reduces and offers recommendations for updates. KEYWORDS: Big data, Hadoop, HDFS, Map Reduce, Scheduling I.BIG DATA INTRODUCTION We are working in the computerized advanced world information. The real world moving to big data day by day, where a massive data set from many of sources as Face book, Yahoo, Google, YouTube, Amazon, Microsoft and Twitter, ebay, diverse sensor systems, Airlines record, Global Position System, RFID per users, PC logs, Closed Circuit cameras, Internet of Things and bunches of other field sources. All these data makes complexities in dealing with and using. Lesser and lesser the comprehension of information is greater and greater it would progress to becoming. There are some planning viewpoints in huge information Hardtop and Map Reduce. s are in charge of errand task in Map Reduce where relegating the assignments to a specific information hub. The accessibility and the information area prompt the choice of the information hub which the preparing should be done to play out the Map and decrease. The design of an algorithm for a node to choosing is important to progress the Map Reduce working and optimize the performance. This paper deeds to offer a more widespread meaning of huge information is Big Data that captures its exclusive and character. Dealing with Big information faces "V" challenges High Volume: It refers to the huge information measures created by an alternative association in an incoherent way. The most current information measure in the world is the best known for obtaining huge information as volume. The estimated volume of the information is expressed in terabytes, in petbytes, and in zettabytes. High Variability: How to understand the distinctive words with the same meaning or a different meaning. How to understand different types of words with the same meaning or different meaning. High Variety: It refers to the types of information that now can use, with certainty 80% of the world's information is not structured (content, image, sound, video, etc.) with a huge information innovation, we can dissect and gather information of different types for example, messages, discussions on online networks, photographs, sensor information, video or voice accounts, recordings, clicks, information on machines and sensors. Copyright to IJIRCCE 103

2 High Visualization parallel, cones and circular network diagrams. Combine the multitude of different variables that are the result of the variety, speed and the very complex inter relationships between them, that not easy to develop meaningful visualization. Traditional images when we try to draw a trillion data points, we need different ways to display data, such as data grouping, tree maps, sunbeam, High Speed: Many data require fast processing, so the speed is based on the velocity at which they are generated, created or updated and travels at high speed around the world. Sometimes 1 minute is too late, so big data is time sensitive. The Facebook data store stores over 300 bytes of data, but the speed will take at which new data is created. High vulnerability: Huge information brings new security concerns. Insurance additionally implies building the notoriety and trust of the brand. Thorough security works on, including the utilization of cutting edge diagnostic abilities to oversee protection and security issues, can separate organizations from the opposition and make solace and trust with the general population. Value: The potential estimation of huge information is colossal. Esteem is the fundamental wellspring of enormous information since it is very important for organizations that the IT foundation framework stores a substantial number of qualities in the database. Numerous factors can influence the execution of the framework as the change in equipment assets and programming calculations, the system throughput, the rightness of information, area (locality), dealing with, investigation, stockpiling, the protection of information and job setting up. Each one of these issues makes challenges in huge data set as Big Data. Apache Hadoop Open Source is most popular tools to handle the giga to peta data, which is extensively used in large application, since it can handle different kinds of data are structured data (table) or unstructured data. Hadoop open source can handle redundancy, scalability, parallel processing, and distributed architecture. The Map Reduce is the best component of Apache Hadoop open source, it schedule the jobs by different scheduling algorithms. The usage of Map Reduce program model, which can run applications in a group (cluster) that comprises of countless. It too gives a solid (reliable), scalable and distributed services. In many systems the major issue exists is scheduling in all types of computation, because best-handling of each task execution process in the system and the greatest allocation of resources. In Apache Hadoop open source schedule the jobs is a most active area of Big Data. This research target on many scheduling challenges in big data, and the design the algorithms for solve the all issues. This paper structure is as per the following. section II discus about associated work, section III examines Apache Hadoop system, section IV show the prerequisites from the compelling booking, section V examines various types of the scheduler in Hadoop-MapReduce. At long last, section VI presents conclusions. II. INTERRELATED WORKS In this section follows, we will show the interrelated work, This Research paper considered some Apache Hadoop open source scheduler types and present a path on how to progress Map Reduce job execution. The Hadoop schedulers focus on only homogeneous Hadoop clusters. The Hadoop, Map reduce schedulers are compared with some important features. A complete review focus on processing large dataset, it is put based on the Map Reduce program framework. It also a set of recognized and designed systems covered, for to offer a good software interfaces on the Apache Hadoop Map Reduce programming framework. The large data processing systems researched and also examine some valuable views associated to many types of applications scenarios of most popular Map Reduce programming framework. In this survey to improve the query execution performance as parallel wise using more popular Map Reduce programming framework and presents the set of Map Reduce software framework limitations, and few techniques to solve this. The job scheduling major issues and Apache Hadoop Map Reduce classification of scheduling algorithms presented in this paper. Also focused different resource monitoring tools and frameworks on how to help for getting best results from Hadoop Map Reduce programming model.the paper also covered useful examine of Map reduce scheduling algorithms working, classification of multidimensional framework, good quality requirements, types of scheduling, and working done in dynamic platform. Copyright to IJIRCCE 104

3 III. APACHE HADOOP Apache Hadoop open source is a software framework for handling huge data set based on distributed storage and efficient processing of computer clusters on commodity hardware. Hadoop framework can handle hardware failure automatically; it used in several applications such as Twitter, Face book, Google, Yahoo, YouTube and Amazon etc. Apache open source Hadoop has more popular core components for efficient storing and processing large datasets on distributed file system, Map Reduce framework in distributed and scalable.in Apache Hadoop splits the file into set of blocks (pieces) and each block length is 64 megabytes or 128 megabytes, these blocks are circulated across multiple nodes of cluster. To achieve efficient process the large data in locality, open source Hadoop apply Map reduce processing to multiple nodes regarding parallel and distributes data processing. To manipulate huge number of nodes, handle large data, to allow more speed processing of the large dataset in more efficiently, scalability than conservative structural design with fast networking. Conventional architecture with fast. Submit query via application Query Query Submitted to Mater Node Master Node Master Node uses Map process to Assign Tasks to Slave Nodes which Slave Nodes Structured Data User Machine After the Slave Nodes Completed their Tasks return results to Master Slave Nodes Unstructured Data Query Result Reduces process regroup the data assemble results Big Data Engine Figure1. Sequence in Hadoop framework A. Hadoop Distributed File System: Meta Data Ops Name Node Meta Data Client Read Data Nodes Block Ops Data Nodes Repli cation Write Rack 1 Client Rack 2 Figure 2. HDFS Architecture Copyright to IJIRCCE 105

4 It is implemented by Google as most popular distributed file system can handle the large dataset using commodity hardware clusters as more efficient and reliable. GFS is developed to produce tremendously high throughput, small latency and reduce each hardware failure in server. Google file system guide by, Hadoop open source Distributed File System use number of systems (machine) to keep many number of files. Through replicating factor on number of servers we obtain good reliability. Large data being stored on number of multiple nodes, then we can gain fast computation and more reliable process. To place the large dataset through HTT Protocol, then focus the client content accessing from web The Apache Hadoop open source Distributed File System has two components are One and only One is Name Node uses about block of data map to Data Nodes and operate files like file rename, manage file directories, file open and file close, also Name Node maintains namespace of files,store file operations by clients. various large number of Data Nodes are primary process is to create, delete number of data blocks and also manage replication factor. B. REDUCE The Hadoop programming model is Map Reduce is used to processing the large dataset in cluster as parallel. It was originally devepled by Google Company for large number of web searching applications. The Hadoop Map Reduce program framework is efficient can be used to data mining applications and machine learning tasks in different data centers. The main purposes of Map Reduce are to give the permission programmers to design and develop their own applications from the drawbacks of partitioning, parallel process and schedule the jobs. I N P U T D A T A INPUT INPUT INPUT INPUT <K, V> <K, V> <K, V> RESULT RESULT RESULT Figure 3. Map reduce Framework Majorly all jobs are submit to Hadoop Map Reduce, it splits into small divisions and Map Reduce design the tasks execute by Task Tracker its follow the computation in MapReduce phase. In Map Reduce program model the first phase is map it provides as Key k1 and value v1 pairs. The second phase is reducing it follows sort and shuffle process then give result. Every each pair allows one key per time then process data per key k1 to yield key value (k1, v1) pairs. This programming model used different schedulers to assign jobs from queue designed by Map Reduce Job Tracker to Task Tracker. Each scheduling will select the multiple nodes on what type of tasks are going to be executed.also reduce task carries summarized service, take care all types of communication services and to transfer large data across the system and one more thing it can reduce redundancy,handle fault tolerance situations. The scalability and system fault tolerance are major responsibility of Hadoop Map Reduce framework; also these are obtaining by to optimize the execution engine. Gains usually are only seen by parallel data tasks acting on different dataset parts by multi-threaded implementations.the Hadoop Map reduce distributing shuffle process can Optimize the communication service cost. Through multithreaded implementation to handle the different datasets tasks as parallel. If reduce the total cost communication service on shuffle distributed process. First Step is Map: To execute the user (worker) nodes of local data by map method then result (output) is write to temporary (private) storage. Second Step is Shuffle: To reallocate the data of worker nodes regarding output keys, hence total data associated to few keys are placed on the same node. Copyright to IJIRCCE 106

5 Third Step is Reduce: to process each group output data regarding key in parallel by worker node. IV. SCHEDULING CONSIDERATIONS, ATRIBUTES AND IMPLEMENTATION PROPOSALS To efficiently design set of schedule algorithms, to reach and progress some major requirements are High quality service, fast response, data availability, efficient utilization of resources like communication, system hardwares, fairness and good throughput. So it is critical to design such scheduling algorithms for fulfill the requirement because high complexity. So Map Reduce scheduling algorithms are created to making the best required single attribute features. Also it can be classify like adaptive means datasets, tasks, attributes, resources, and workloads and nonadaptive means fixed types. Data placement and Locality The Hadoop Map Reduce scheduler; the foremost decision is to arrange the compute methods close to the datasets. For the reason that it can decrease the input output of clusters in disk, also finally it will raise the throughput. This paper wished-for a k-means classical method of cluster analysis by putting the more relevant datasets in inside the data center during the implementation and preparing stage. To analysis the mutually dependent association between datasets and jobs, follows the multilevel task replication to decrease the size of middle data and to send data centers and also increase the job processing progress reduce the length of intermediate data to convert among dataset and improve the performance of running datasets workflows. To overcome stagger issue by using replication factor until to complete the total tasks in Hadoop Map Reduce model. B. Synchronization To transfer Map function outputs (intermediate) submit to as Reducer input, also keys and values pairs are together as keys based on synchronization. This synchronization service attain by Map Reduce shuffle and sort methods, also it needs circulating sorting service must involve every Map Reduce iterations. The problem becoming a major, in different datasets due to various capabilities and configuration of every node, also influence various map and reduce finishing time. In Map Reduce framework the reduce process unable to terminate unless each map function has completed, a general optimization to reduce function to drag the data from map function if completed. C. Throughput It plays a major role in Map Reduce tasks because it is best features of Hadoop open source. Throughput is number of Task completed in unit of time regarding some characteristics and every Map Reduce scheduler must produce good throughput along with high data locality and utilization. D. Response Time: The interacting is the sum of time required to react to an administration ask. The service here is a task user must be operated within the response time is the service time and waiting time The service time varies for a specific request little if the work pressure increases. The response time is important factor in QOS (quality of service) for every planner in MapReduce Jobs and should be with some tolerance related to the amount of work sent. E. Availability It is a type of reliability metrics, it can be defined as the average time between failures and repetitions of user requests, or it is the time when the system supports and continues to execute to comply with future requests. All work must be completed completely and the planner must check it. This is a type of reliability measure. Repeat the client request between failures, this is also the length of the system that keeps the query and continues to work to meet the user's future demands. The total tasks must be completed after this verification via the programmer. Copyright to IJIRCCE 107

6 F. Resource Utilization In many scientific areas the resource major feature is utilization. Majorly we maintain maximum number of resources and efficient processing tools to handle massive data but in these is lack in our process. The Map Reduce scheduler must consume resources efficiently and particularly the scheduler should utilize their sources effectively, especially the nearest system hardware closes the communication employment. To optimize and manage resources to focus number of tasks in clusters then achieving good results in our systems. G. Fairness It is more significant feature which is through different types of Hadoop s. Fairness is to share the set of resources and task execution as poor fairly, uniformally, based on priority levels, size, and execution time between Map Reduce Jobs. V. SCHEDULERS IN HADOOP-REDUCE In this part we will show the job made in the Map Reduce scheduling regarding the fairness, priority, resource, time, utilization and deadline of the system. If the scheduling process is static means offline since the allotments completed earlier than to start the execution or Dynamic means online since the allotments completed at the execution time regarding the lively atmosphere and desirable set of resources. Many times the scheduler is real-time computation system based on Storm distributed whenever manage the tasks regarding resource utilization. A.FIFO : It is the most defaulting Map Reduce scheduling model working based on First-in first-out queue, when the jobs are schedule by the assigning order to Task Tracker nodes in Apache Hadoop open source. It can be used different systems, it can t accept priority task due to first come first serve base. If jobs are stay to turn as each job will use total cluster. Major drawback is to decrease the data locality; also small queries will wait for results in unfair time. This paper target on task locality improves of FIFO scheduler by Apache Hadoop open source framework, presents the progress Task Locality scheduler. Tasks will be arrange and served too many job queues regarding the most feasible threshold value of job locality. B. Fair It is developed by Face Book Company to obtain good fairness among all jobs, allocate every desire resources to each job through equal fairness sharing. This scheduler design the every job at minimum processing time to complete very quickly based on equivalent distribution of resources. All users have pools along with at least desire sharing of resources and select the unused slot by assign the running tasks. This scheduler allocates the resources to every application on totally equivalent sharing. This scheduler working on memory only by default and organize the work based on memory and CPU. It used the total cluster if only single application is working. Idle or free resource assigned to the tasks of applications, the cluster should be arranged between fixed numbers of users. This scheduler follows priority to assign weights for finding resources and allocate to the apps. It has a default queue is called as default it is share among total users And create each user name on each queue along with minimum sharing ability, minimum quota of slots, remaining slots means idle slots will assigned to next pool. With Fair scheduler each application can be run by default, but the total running apps are limited regarding configuration file of each user queue. It advantage whenever user present more apps at a one time, also we can increase the scheduler performance by minimize the intermediate data producing or switching. There are no chance applications failing because waiting the scheduler queue up to complete the past users request. Copyright to IJIRCCE 108

7 C. Capacity It is presented by Yahoo, which is developed to handle complex clusters, also it can be apply to number of applications when more number of clients are there, it designed especially to make efficient resource allotments of each computation between number of clients and schedule the task allotment on domain resource. Also this scheduler handles every job regarding user demands and memory utilization. Also many queues are designed in the place of pools, to assign the tasks according to queues of each client and to make Map Reduce queue slot size. The remaining service of each queue is contributed among who queue needs. Once the task has submitted then get a priority order on each arrive task regarding time. The high priority level tasks will obtain the resources earlier than low priority tasks. Every cluster capacity is allocate among clients, not between tasks as like fair scheduler. We can manage the waiting time in capacity scheduler later than preemption is apply to remaining queues job whenever fair sharing is low. D. Delay It is designed by Face book, the delay occurs from an amount of time waiting to get the data on the local node skip if in case not available then subsequent jobs. The purpose is to overcome the efficient share locality drawback. If your task is scheduled and will not launch hey lack of job, lack of amount of time and reminding costs will launch its job instead, the intention of relational is to multiplex hafiz clusters based on statistical e it has a minimum influence on fairness the gain very high data locality. The Reallotment of resources is done by deleted the remaining tasks from old jobs to arrange a place for your jobs, and wait for a task to complete to allocated slots to new jobs, but the drawback is working performance by removing the how many tasks are not useful because to wait for the jobs. From small jobs, many locality problems will occur because the file has low input data. So it contains a little number of blocks read, small drawback will happen whenever is the job follows ascending order list the head should be controlled one of the tasks is run on the remaining empty slot then whatever the node working is going on. The important slot problem is that there is a chance for a job to be allocated to the same slot again I shall use. Finally, the designers follow delay scheduling because fairness is free meanwhile to increase data locality by many jobs to stay for scheduling choice with efficient local data node. Tosca allotment has a free from job selection limitation. E. LATE LATE scheduler (Longest surmised time to end) came initially from the Speculative undertaking execution tunable component, where singular jobs can settle on the choice of permit it or not, a default is permit. The LATE scheduler displayed in make an effort to all tasks identified with certain jobs completed when conceivable, since that the job will complete totally if the greater part has done. At the point when the undertaking runs gradually because of a few factors like clack of number of resources, the scheduler finds this assignment also, attempt to dispatch another identical task copy as a backup which may finish quickly and the increase the execution. LATE scheduler searches for the deferral an issue as execution issue, not from a dependability purpose of view, not look for the reasons of this postponement doesn't attempt to analyze and settle moderate running assignments and software misconfiguration. Paper address the perception of Hadoop s homogeneity assumption leads to wrong and occasionally again more speculative implementation in the heterogeneous background can decrease execution not exactly acquired with injured speculation. Additionally, can guide serious execution corruption at the point when its understand assumption is broken down. It is strong to heterogeneity in heterogeneous conditions data centers. LATE is set up to three standards: determination of quick nodes to run, to speculate priority level jobs, and thrashing prevention action by topping speculative tasks. G. Deadline Constraint The deadline scheduler works on the user-specified time limitations, tries to meet working deadlines, and increases the usage of the system. The scheduler will calculate the minimum number of Hadoop map and reduce the slots needed to Copyright to IJIRCCE 109

8 complete the job (cost). If the scheduler capacity test fails, the user will be informed to enter another deadline value and, if approved, then scheduled the jobs. H. Resource Aware Today, there are different heterogeneous nodes in the system that gives diversity in the allocation of nodes. Other types of scheduler did not consider the availability of resources in detail and assigned a fixed size of resources. Resource aware scheduler considers the availability of resources for task scheduling. The scheduler is based on the use and confinement of resources in the machine, tries to minimize the consumption of these resources and increases the use. Configure each node to indicate the input output rate of the disk and the actual processing power available on each machine in the cluster, and online modification of the available slot machine capacity. Each Task Tracker node keeps track of the hardware resources. Disk the input/output channel can have a significant impact on Map-Reduce tasks. Virtual Machine Management Status Monitors Page error virtual machine-induced disk hit (machine load indicator). There are two possible mechanisms in conscious resources Job Tracker Programming: 1) Dynamic announcement of available free slots calculation locations configured in each Task Tracker node. The improved response time of the job where no task will reach the bottleneck of resource consumption. 2) Free Slot Priorities / Filtering, where the cluster administrators configure a maximum amount of calculation Locations per node at the time of configuration. I. HPCA based Node Selection scheduling Algorithm Health, Priority, Capacity and Availability (HPCA) node-based selection algorithm proposes the creation of a queue Nodes available to accept new tasks with optimized node selection task to give the execution performance of the node, also gives redundancy mechanism to overcome the failure of the execution task. It Consider how to select the resource / node based on your success rate at work, job priority and default priority of the resource chosen to perform the work, the capacity of the selected resource and availability of resources. J. Round Robin The round robin with a various feedback algorithm attempt to solve the problem of the FIFO scheduler, where the work begins in the order of presenting and this reason the work present later to start later. Even though the round robin scheduler, the lately submit the job, you will receive a quick response and it will start with a small delay. This scheduler can minimize the normal response time. In initiate the task scheduling algorithm based on improvements Rounded robin weighted, Hadoop's task planning puts transmit the weight update rules by analyzing the tasks to be allocate a weight to each queue and schedule assignments Different sub queue depending on the weight. K. Other s There are many other improvements and improvements in the scheduling algorithms of Hadoop works; we will demonstrate some of them:this paper, proposed a Bayesian classifier job scheduling algorithm stand on scheduler learning in executing the operating state and performing self-classification. In two efficient schedulers, it is proposed: the first proposes to act as a high priority interactive service in the virtualization level, and the second as part of Hadoop to help batch processing jobs to reach the deadline. The method finds the most excellent servers that will provide the excellent performance to Hadoop tasks, and will schedule arriving jobs efficiently by scheduling with the deadline. Combination of these types of schedulers provides a type of expectedness and security in using resourceintensive tasks in Hadoop with web-intensive latency applications. In the proposed research paper, a workflow that requires a lot of data Optimization Algorithm (PDWA) used for heterogeneous, that calculate the systems are processing on the basis of data flows. The algorithm provides reduced latency with an increase of performance the graph of the application task is divided so that the inter-partition the Copyright to IJIRCCE 110

9 movement of the data is minimized. Each partition is assigned to the execution node that performs minimal execution time for this partition PDWA also favors partial tasks duplication for reducing latency. The paper is different from the equality between the restrictions of the users, focuses on the goal of balancing the workload and the delay satisfaction of requests from Map Reduce users. Scheduling algorithms proposed with a fixed approximation related to balance the server workload and also meets the requirements of Response time related to Map Reduce jobs. The implemented the algorithms are designed with a resource manager as an open Implementation of the Hadoop source. In consider arranging slot configuration, it is initially fixed and static. This proposes a better Hadoop system called Fair-efficient Slot-configuration scheduler for Apache Hadoop Clusters (FRESH) to improve configuration of the dynamic location configuration, and suitably assign tasks to available slots. Achieving what is right and effective Slot configurations and scheduling for Hadoop clusters. Proposal try to perform two main tasks: decide on the slot configuration when deciding how many cards / reduce hallways are appropriate, and assign assignment / reduction tasks to the available spaces. This paper check the application level indications with differentiated priorities of flow processing tasks that allow Application Specific Distributed Flow Processing Systems (DSPS) resource scheduling. Suggest a general method for improve resource scheduling based on priorities in flow diagrams DSPS, gives application developers the ability to improve DSPS graphics with priority metadata and by introduce an extensible set of priority schemes to manage using DSPS extended automatically. Put Met scheduler before the FIFO scheduler to improve its performance. I also attempt to create a real-time type scheduler to Map Reduce, to reduce the limitation of others schedulers. Avoid accepting jobs that will guide to the deadline fails and attempts to improve cluster uses. When a new work of Map Reduce, the admission controller arrives determines if it is possible to scheduler the new job without compromising the assurance of previously accepted work. A feedback driver it is developed to keep the admission controller updated. Table I Hadoop s Fetures Name of the Default FIFO Fair Capacity LATE Delay Speculative Execution NA NA NA YES Head of line problem Sticky Slots Locality Problem Job Allocation Imple mented Remarks NA NA YES Statically YES Homoge system, Static allocatio n, no YES YES YES for small jobs Statically YES Homoge Static NA NA YES Statically YES Homoge Static, non primitive NA NA YES Statically YES Homoge Heteroge neous,static NA NA NA Improved compared to Fair scheduler Statically YES Homoge Static Copyright to IJIRCCE 111

10 Dynamic Priority Resource Aware Deadline Constraint NA NA NA NO Dynamic YES Homoge Heteroge neous, Dynamic NA NA NA YES Both YE S NA NA NA YES for small jobs Dynamic Not in real time Homoge Heteroge neous, Dynamic Homoge Static Learning Enabled NA NA YES Both COSHH NA YES YES NO Dynamic Not in real time Aware NA NA NA NO Dynamic YES YES Homoge Heteroge neous, Dynamic Homoge Static Homoge Static Table 2. Hadoop s Properties Copyright to IJIRCCE 112

11 VI. CONCLUSIONS This paper examines schedulers the attribution of work and schedulers in Hadoop platform There are several types of Hadoop schedulers that are not suitable for all types of systems. The selection will be oriented according to the requirements of the users and remaining the resources. Our investigation may be the basis of the recommendation to the given environment and requirements. This paper summarizes many scheduling algorithms that give the characteristics of each method and how different types of system can be schedulers by proper schedulers and said that the coming time is going heterogeneous systems and the research gap between homogeneous and heterogeneous systems. In the future, we wish to offer a dynamic template framework that contains different p scheduling algorithms and you can select in online the most suitable programming algorithm for each state of the environment. REFERENCES [1] A.Verma et al., "ARIA: Automatic Resource Inference And Allocation for MapReduce environments", 8th Autonomic computing ACM, [2] S.Bardhan and D. A. Menasce, "Queuing Network Models to Predict the Completion Time of the Map Phase of Map Reduce Jobs," ICMG, [3] J.V.Gautam, et al.,"a survey on job scheduling algorithms in Big data processing",(icecct), 2015 IEEE, , 2015 [4] Hadoop: Retrieved [5] Casavant et al., "A taxonomy of scheduling in general-purpose distributed computing systems",ieee Transactions, 1988, pp [6] D. Yoo and K. M. Sim. "A comparative review of job scheduling for MapReduce. " Cloud Computing and Intelligence Systems, IEEE, Copyright to IJIRCCE 113

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