It is cost-efficient for a tenant with a limited budget to establish a virtual Map Reduce cluster by renting multiple virtual private servers (VPSs) from a VPS provider. To provide an appropriate scheduling scheme for this type of computing environment, we propose in this paper a hybrid job-driven scheduling scheme (JoSS for short) from a tenant’s perspective. JoSS provides not only job level scheduling, but also map-task level scheduling and reduce-task level scheduling. JoSS classifies Map Reduce jobs based on job scale and job type and designs an appropriate scheduling policy to schedule each class of jobs. The goal is to improve data locality for both map tasks and reduce tasks, avoid job starvation, and improve job execution performance. Two variations of JoSS are further introduced to separately achieve a better map-data locality and a faster task assignment. We conduct extensive experiments to evaluate and compare the two variations with current scheduling algorithms supported by Hadoop. The results show that the two variations outperform the other tested algorithms in terms of map-data locality, reduce-data locality, and network overhead without incurring significant overhead. In addition, the two variations are separately suitable for different Map Reduce workload scenarios and provide the best job performance among all tested algorithms.
Survey of Parallel Data Processing in Context with MapReduce cscpconf
MapReduce is a parallel programming model and an associated implementation introduced by
Google. In the programming model, a user specifies the computation by two functions, Map and Reduce. The underlying MapReduce library automatically parallelizes the computation, and handles complicated issues like data distribution, load balancing and fault tolerance. The original MapReduce implementation by Google, as well as its open-source counterpart,Hadoop, is aimed for parallelizing computing in large clusters of commodity machines.This paper gives an overview of MapReduce programming model and its applications. The author has described here the workflow of MapReduce process. Some important issues, like fault tolerance, are studied in more detail. Even the illustration of working of Map Reduce is given. The data locality issue in heterogeneous environments can noticeably reduce the Map Reduce performance. In this paper, the author has addressed the illustration of data across nodes in a way that each node has a balanced data processing load stored in a parallel manner. Given a data intensive application running on a Hadoop Map Reduce cluster, the auhor has exemplified how data placement is done in Hadoop architecture and the role of Map Reduce in the Hadoop Architecture. The amount of data stored in each node to achieve improved data-processing performance is explained here.
Hadoop Mapreduce Performance Enhancement Using In-Node Combinersijcsit
While advanced analysis of large dataset is in high demand, data sizes have surpassed capabilities of
conventional software and hardware. Hadoop framework distributes large datasets over multiple
commodity servers and performs parallel computations. We discuss the I/O bottlenecks of Hadoop
framework and propose methods for enhancing I/O performance. A proven approach is to cache data to
maximize memory-locality of all map tasks. We introduce an approach to optimize I/O, the in-node
combining design which extends the traditional combiner to a node level. The in-node combiner reduces
the total number of intermediate results and curtail network traffic between mappers and reducers.
While advanced analysis of large dataset is in high demand, data sizes have surpassed capabilities of
conventional software and hardware. Hadoop framework distributes large datasets over multiple
commodity servers and performs parallel computations. We discuss the I/O bottlenecks of Hadoop
framework and propose methods for enhancing I/O performance. A proven approach is to cache data to
maximize memory-locality of all map tasks. We introduce an approach to optimize I/O, the in-node
combining design which extends the traditional combiner to a node level. The in-node combiner reduces
the total number of intermediate results and curtail network traffic between mappers and reducers.
Map-Reduce Synchronized and Comparative Queue Capacity Scheduler in Hadoop fo...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Exploration of Call Transcripts with MapReduce and Zipf’s LawTom Donoghue
This study implements a proof of concept
pipeline to capture web based call transcripts and produces
a word frequency dataset ready for textual analysis
Survey of Parallel Data Processing in Context with MapReduce cscpconf
MapReduce is a parallel programming model and an associated implementation introduced by
Google. In the programming model, a user specifies the computation by two functions, Map and Reduce. The underlying MapReduce library automatically parallelizes the computation, and handles complicated issues like data distribution, load balancing and fault tolerance. The original MapReduce implementation by Google, as well as its open-source counterpart,Hadoop, is aimed for parallelizing computing in large clusters of commodity machines.This paper gives an overview of MapReduce programming model and its applications. The author has described here the workflow of MapReduce process. Some important issues, like fault tolerance, are studied in more detail. Even the illustration of working of Map Reduce is given. The data locality issue in heterogeneous environments can noticeably reduce the Map Reduce performance. In this paper, the author has addressed the illustration of data across nodes in a way that each node has a balanced data processing load stored in a parallel manner. Given a data intensive application running on a Hadoop Map Reduce cluster, the auhor has exemplified how data placement is done in Hadoop architecture and the role of Map Reduce in the Hadoop Architecture. The amount of data stored in each node to achieve improved data-processing performance is explained here.
Hadoop Mapreduce Performance Enhancement Using In-Node Combinersijcsit
While advanced analysis of large dataset is in high demand, data sizes have surpassed capabilities of
conventional software and hardware. Hadoop framework distributes large datasets over multiple
commodity servers and performs parallel computations. We discuss the I/O bottlenecks of Hadoop
framework and propose methods for enhancing I/O performance. A proven approach is to cache data to
maximize memory-locality of all map tasks. We introduce an approach to optimize I/O, the in-node
combining design which extends the traditional combiner to a node level. The in-node combiner reduces
the total number of intermediate results and curtail network traffic between mappers and reducers.
While advanced analysis of large dataset is in high demand, data sizes have surpassed capabilities of
conventional software and hardware. Hadoop framework distributes large datasets over multiple
commodity servers and performs parallel computations. We discuss the I/O bottlenecks of Hadoop
framework and propose methods for enhancing I/O performance. A proven approach is to cache data to
maximize memory-locality of all map tasks. We introduce an approach to optimize I/O, the in-node
combining design which extends the traditional combiner to a node level. The in-node combiner reduces
the total number of intermediate results and curtail network traffic between mappers and reducers.
Map-Reduce Synchronized and Comparative Queue Capacity Scheduler in Hadoop fo...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Exploration of Call Transcripts with MapReduce and Zipf’s LawTom Donoghue
This study implements a proof of concept
pipeline to capture web based call transcripts and produces
a word frequency dataset ready for textual analysis
Design and Implementation of SOA Enhanced Semantic Information Retrieval web ...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Today’s era is generally treated as the era of data on each and every field of computing application huge amount of data is generated. The society is gradually more dependent on computers so large amount of data is generated in each and every second which is either in structured format, unstructured format or semi structured format. These huge amount of data are generally treated as big data. To analyze big data is a biggest challenge in current world. Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage and it generally follows horizontal processing. Map Reduce programming is generally run over Hadoop Framework and process the large amount of structured and unstructured data. This Paper describes about different joining strategies used in Map reduce programming to combine the data of two files in Hadoop Framework and also discusses the skewness problem associate to it.
MAP REDUCE BASED ON CLOAK DHT DATA REPLICATION EVALUATIONijdms
Distributed databases and data replication are effective ways to increase the accessibility and reliability of
un-structured, semi-structured and structured data to extract new knowledge. Replications offer better
performance and greater availability of data. With the advent of Big Data, new storage and processing
challenges are emerging.
To meet these challenges, Hadoop and DHTs compete in the storage domain and MapReduce and others in
distributed processing, with their strengths and weaknesses.
We propose an analysis of the circular and radial replication mechanisms of the CLOAK DHT. We
evaluate their performance through a comparative study of data from simulations. The results show that
radial replication is better in storage, unlike circular replication, which gives better search results.
Map Reduce Workloads: A Dynamic Job Ordering and Slot Configurationsdbpublications
MapReduce is a popular parallel computing paradigm for large-scale data processing in clusters and data centers. A MapReduce workload generally contains a set of jobs, each of which consists of multiple map tasks followed by multiple reduce tasks. Due to 1) that map tasks can only run in map slots and reduce tasks can only run in reduce slots, and 2) the general execution constraints that map tasks are executed before reduce tasks, different job execution orders and map/reduce slot configurations for a MapReduce workload have significantly different performance and system utilization. This survey proposes two classes of algorithms to minimize the make span and the total completion time for an offline MapReduce workload. Our first class of algorithms focuses on the job ordering optimization for a MapReduce workload under a given map/reduce slot configuration. In contrast, our second class of algorithms considers the scenario that we can perform optimization for map/reduce slot configuration for a MapReduce workload. We perform simulations as well as experiments on Amazon EC2 and show that our proposed algorithms produce results that are up to 15 - 80 percent better than currently unoptimized Hadoop, leading to significant reductions in running time in practice.
A New Multi-Dimensional Hyperbolic Structure for Cloud Service Indexingijdms
Nowadays, the structure of cloud uses the data processing of simple key value, which cannot adapted in
search of closeness effectively because of the lack of structures of effective indexes, and with the increase of
dimension, the structures of indexes similar to the tree existing could lead to the problem " of the curse of
dimension”. In this paper, we define a new cloud computing architecture in such global index, storage
nodes is based on a Distributed Hash Table (DHT). In order to reduce the cost of calculation, store
different services on the hyperbolic tree by using virtual coordinates thus that greedy routing based on
hyperbolic space properties in order to improve query storage and retrieving performance effectively. Next,
we perform and evaluate our cloud computing indexing structure based on a hyperbolic tree using virtual
coordinates taken in the hyperbolic plane. We show through our experimental results that we compare with
others clouds systems to show our solution ensures consistence and scalability for Cloud platform.
On Traffic-Aware Partition and Aggregation in Map Reduce for Big Data Applica...dbpublications
The MapReduce programming model simplifies
large-scale data processing on commodity cluster by
exploiting parallel map tasks and reduces tasks.
Although many efforts have been made to improve
the performance of MapReduce jobs, they ignore the
network traffic generated in the shuffle phase, which
plays a critical role in performance enhancement.
Traditionally, a hash function is used to partition
intermediate data among reduce tasks, which,
however, is not traffic-efficient because network
topology and data size associated with each key are
not taken into consideration. In this paper, we study
to reduce network traffic cost for a MapReduce job
by designing a novel intermediate data partition
scheme. Furthermore, we jointly consider the
aggregator placement problem, where each
aggregator can reduce merged traffic from multiple
map tasks. A decomposition-based distributed
algorithm is proposed to deal with the large-scale
optimization problem for big data application and an
online algorithm is also designed to adjust data
partition and aggregation in a dynamic manner.
Finally, extensive simulation results demonstrate that
our proposals can significantly reduce network traffic
cost under both offline and online cases.
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCEcsandit
Big data analysis has become much popular in the present day scenario and the manipulation of
big data has gained the keen attention of researchers in the field of data analytics. Analysis of
big data is currently considered as an integral part of many computational and statistical
departments. As a result, novel approaches in data analysis are evolving on a daily basis.
Thousands of transaction requests are handled and processed everyday by different websites
associated with e-commerce, e-banking, e-shopping carts etc. The network traffic and weblog
analysis comes to play a crucial role in such situations where Hadoop can be suggested as an
efficient solution for processing the Netflow data collected from switches as well as website
access-logs during fixed intervals.
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.
Design Issues and Challenges of Peer-to-Peer Video on Demand System cscpconf
P2P media streaming and file downloading is most popular applications over the Internet.
These systems reduce the server load and provide a scalable content distribution. P2P
networking is a new paradigm to build distributed applications. It describes the design
requirements for P2P media streaming, live and Video on demand system comparison based on their system architecture. In this paper we described and studied the traditional approaches for P2P streaming systems, design issues, challenges, and current approaches for providing P2P VoD services.
Survey on Performance of Hadoop Map reduce Optimization Methodspaperpublications3
Abstract: Hadoop is a open source software framework for storage and processing large scale of datasets on clusters of commodity hardware. Hadoop provides a reliable shared storage and analysis system, here storage provided by HDFS and analysis provided by MapReduce. MapReduce frameworks are foraying into the domain of high performance of computing with stringent non-functional requirements namely execution times and throughputs. MapReduce provides simple programming interfaces with two functions: map and reduce. The functions can be automatically executed in parallel on a cluster without requiring any intervention from the programmer. Moreover, MapReduce offers other benefits, including load balancing, high scalability, and fault tolerance. The challenge is that when we consider the data is dynamically and continuously produced, from different geographical locations. For dynamically generated data, an efficient algorithm is desired, for timely guiding the transfer of data into the cloud over time for geo-dispersed data sets, there is need to select the best data center to aggregate all data onto given that a MapReduce like framework is most efficient when data to be processed are all in one place, and not across data centers due to the enormous overhead of inter-data center data moving in the stage of shuffle and reduce. Recently, many researchers tend to implement and deploy data-intensive and/or computation-intensive algorithms on MapReduce parallel computing framework for high processing efficiency.
Introduction to Big Data and Hadoop using Local Standalone Modeinventionjournals
Big Data is a term defined for data sets that are extreme and complex where traditional data processing applications are inadequate to deal with them. The term Big Data often refers simply to the use of predictive investigation on analytic methods that extract value from data. Big data is generalized as a large data which is a collection of big datasets that cannot be processed using traditional computing techniques. Big data is not purely a data, rather than it is a complete subject involves various tools, techniques and frameworks. Big data can be any structured collection which results incapability of conventional data management methods. Hadoop is a distributed example used to change the large amount of data. This manipulation contains not only storage as well as processing on the data. Hadoop is an open- source software framework for dispersed storage and processing of big data sets on computer clusters built from commodity hardware. HDFS was built to support high throughput, streaming reads and writes of extremely large files. Hadoop Map Reduce is a software framework for easily writing applications which process vast amounts of data. Wordcount example reads text files and counts how often words occur. The input is text files and the result is wordcount file, each line of which contains a word and the count of how often it occurred separated by a tab.
Design and Implementation of SOA Enhanced Semantic Information Retrieval web ...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Today’s era is generally treated as the era of data on each and every field of computing application huge amount of data is generated. The society is gradually more dependent on computers so large amount of data is generated in each and every second which is either in structured format, unstructured format or semi structured format. These huge amount of data are generally treated as big data. To analyze big data is a biggest challenge in current world. Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage and it generally follows horizontal processing. Map Reduce programming is generally run over Hadoop Framework and process the large amount of structured and unstructured data. This Paper describes about different joining strategies used in Map reduce programming to combine the data of two files in Hadoop Framework and also discusses the skewness problem associate to it.
MAP REDUCE BASED ON CLOAK DHT DATA REPLICATION EVALUATIONijdms
Distributed databases and data replication are effective ways to increase the accessibility and reliability of
un-structured, semi-structured and structured data to extract new knowledge. Replications offer better
performance and greater availability of data. With the advent of Big Data, new storage and processing
challenges are emerging.
To meet these challenges, Hadoop and DHTs compete in the storage domain and MapReduce and others in
distributed processing, with their strengths and weaknesses.
We propose an analysis of the circular and radial replication mechanisms of the CLOAK DHT. We
evaluate their performance through a comparative study of data from simulations. The results show that
radial replication is better in storage, unlike circular replication, which gives better search results.
Map Reduce Workloads: A Dynamic Job Ordering and Slot Configurationsdbpublications
MapReduce is a popular parallel computing paradigm for large-scale data processing in clusters and data centers. A MapReduce workload generally contains a set of jobs, each of which consists of multiple map tasks followed by multiple reduce tasks. Due to 1) that map tasks can only run in map slots and reduce tasks can only run in reduce slots, and 2) the general execution constraints that map tasks are executed before reduce tasks, different job execution orders and map/reduce slot configurations for a MapReduce workload have significantly different performance and system utilization. This survey proposes two classes of algorithms to minimize the make span and the total completion time for an offline MapReduce workload. Our first class of algorithms focuses on the job ordering optimization for a MapReduce workload under a given map/reduce slot configuration. In contrast, our second class of algorithms considers the scenario that we can perform optimization for map/reduce slot configuration for a MapReduce workload. We perform simulations as well as experiments on Amazon EC2 and show that our proposed algorithms produce results that are up to 15 - 80 percent better than currently unoptimized Hadoop, leading to significant reductions in running time in practice.
A New Multi-Dimensional Hyperbolic Structure for Cloud Service Indexingijdms
Nowadays, the structure of cloud uses the data processing of simple key value, which cannot adapted in
search of closeness effectively because of the lack of structures of effective indexes, and with the increase of
dimension, the structures of indexes similar to the tree existing could lead to the problem " of the curse of
dimension”. In this paper, we define a new cloud computing architecture in such global index, storage
nodes is based on a Distributed Hash Table (DHT). In order to reduce the cost of calculation, store
different services on the hyperbolic tree by using virtual coordinates thus that greedy routing based on
hyperbolic space properties in order to improve query storage and retrieving performance effectively. Next,
we perform and evaluate our cloud computing indexing structure based on a hyperbolic tree using virtual
coordinates taken in the hyperbolic plane. We show through our experimental results that we compare with
others clouds systems to show our solution ensures consistence and scalability for Cloud platform.
On Traffic-Aware Partition and Aggregation in Map Reduce for Big Data Applica...dbpublications
The MapReduce programming model simplifies
large-scale data processing on commodity cluster by
exploiting parallel map tasks and reduces tasks.
Although many efforts have been made to improve
the performance of MapReduce jobs, they ignore the
network traffic generated in the shuffle phase, which
plays a critical role in performance enhancement.
Traditionally, a hash function is used to partition
intermediate data among reduce tasks, which,
however, is not traffic-efficient because network
topology and data size associated with each key are
not taken into consideration. In this paper, we study
to reduce network traffic cost for a MapReduce job
by designing a novel intermediate data partition
scheme. Furthermore, we jointly consider the
aggregator placement problem, where each
aggregator can reduce merged traffic from multiple
map tasks. A decomposition-based distributed
algorithm is proposed to deal with the large-scale
optimization problem for big data application and an
online algorithm is also designed to adjust data
partition and aggregation in a dynamic manner.
Finally, extensive simulation results demonstrate that
our proposals can significantly reduce network traffic
cost under both offline and online cases.
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCEcsandit
Big data analysis has become much popular in the present day scenario and the manipulation of
big data has gained the keen attention of researchers in the field of data analytics. Analysis of
big data is currently considered as an integral part of many computational and statistical
departments. As a result, novel approaches in data analysis are evolving on a daily basis.
Thousands of transaction requests are handled and processed everyday by different websites
associated with e-commerce, e-banking, e-shopping carts etc. The network traffic and weblog
analysis comes to play a crucial role in such situations where Hadoop can be suggested as an
efficient solution for processing the Netflow data collected from switches as well as website
access-logs during fixed intervals.
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.
Design Issues and Challenges of Peer-to-Peer Video on Demand System cscpconf
P2P media streaming and file downloading is most popular applications over the Internet.
These systems reduce the server load and provide a scalable content distribution. P2P
networking is a new paradigm to build distributed applications. It describes the design
requirements for P2P media streaming, live and Video on demand system comparison based on their system architecture. In this paper we described and studied the traditional approaches for P2P streaming systems, design issues, challenges, and current approaches for providing P2P VoD services.
Survey on Performance of Hadoop Map reduce Optimization Methodspaperpublications3
Abstract: Hadoop is a open source software framework for storage and processing large scale of datasets on clusters of commodity hardware. Hadoop provides a reliable shared storage and analysis system, here storage provided by HDFS and analysis provided by MapReduce. MapReduce frameworks are foraying into the domain of high performance of computing with stringent non-functional requirements namely execution times and throughputs. MapReduce provides simple programming interfaces with two functions: map and reduce. The functions can be automatically executed in parallel on a cluster without requiring any intervention from the programmer. Moreover, MapReduce offers other benefits, including load balancing, high scalability, and fault tolerance. The challenge is that when we consider the data is dynamically and continuously produced, from different geographical locations. For dynamically generated data, an efficient algorithm is desired, for timely guiding the transfer of data into the cloud over time for geo-dispersed data sets, there is need to select the best data center to aggregate all data onto given that a MapReduce like framework is most efficient when data to be processed are all in one place, and not across data centers due to the enormous overhead of inter-data center data moving in the stage of shuffle and reduce. Recently, many researchers tend to implement and deploy data-intensive and/or computation-intensive algorithms on MapReduce parallel computing framework for high processing efficiency.
Introduction to Big Data and Hadoop using Local Standalone Modeinventionjournals
Big Data is a term defined for data sets that are extreme and complex where traditional data processing applications are inadequate to deal with them. The term Big Data often refers simply to the use of predictive investigation on analytic methods that extract value from data. Big data is generalized as a large data which is a collection of big datasets that cannot be processed using traditional computing techniques. Big data is not purely a data, rather than it is a complete subject involves various tools, techniques and frameworks. Big data can be any structured collection which results incapability of conventional data management methods. Hadoop is a distributed example used to change the large amount of data. This manipulation contains not only storage as well as processing on the data. Hadoop is an open- source software framework for dispersed storage and processing of big data sets on computer clusters built from commodity hardware. HDFS was built to support high throughput, streaming reads and writes of extremely large files. Hadoop Map Reduce is a software framework for easily writing applications which process vast amounts of data. Wordcount example reads text files and counts how often words occur. The input is text files and the result is wordcount file, each line of which contains a word and the count of how often it occurred separated by a tab.
Performance evaluation and estimation model using regression method for hadoo...redpel dot com
Performance evaluation and estimation model using regression method for hadoop word count.
for more ieee paper / full abstract / implementation , just visit www.redpel.com
Big Data Storage System Based on a Distributed Hash Tables Systemijdms
The Big Data is unavoidable considering the place of the digital is the predominant form of communication in the daily life of the consumer. The control of its stakes and the quality its data must be a priority in order not to distort the strategies arising from their treatment in the aim to derive profit. In order to achieve this, a lot of research work has been carried out companies and several platforms created. MapReduce, is one of the enabling technologies, has proven to be applicable to a wide range of fields. However, despite its importance recent work has shown its limitations. And to remedy this, the Distributed Hash Tables (DHT) has been used. Thus, this document not only analyses the and MapReduce implementations and Top-Level Domain (TLD)s in general, but it also provides a description of a model of DHT as well as some guidelines for the planification of the future research
Cache mechanism to avoid dulpication of same thing in hadoop system to speed ...eSAT Journals
Abstract Cloud computing provides a proper platform for hosting large-scale data-intensive applications. MapReduce is a programming model as well as a framework that supports the model. The main idea of the MapReduce model is to hide details of parallel execution and allow users to focus only on data processing strategies. Hadoop is an open-source implementation for MapReduce. For storage and analysis of online or streaming data which is big in size. Most organization are moving toward Apaches Hadoop HDFS. Applications like log processors, search engines etc. ueses hadoop Map reduce for computing and HDFS for storage. Hadoop is popular for analysis, storage and processing of very large data but require to make changes in hadoop system. There is no mechanism to identify duplicate computations which increase processing time and unnecessary data transmission .To co-locate related files by considering content and using locality sensitive hashing algorithm. By storing related files in same cluster using cache mechanism which improve data locality mechanism and avoids repeated execution of task, both helps to speed up execution of hadoop. Keywords-Distributed file system, Datanode, Locality Sensitive Hashing
Leveraging Map Reduce With Hadoop for Weather Data Analytics iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
There is a growing trend of applications that ought to handle huge information. However, analysing huge information may be a terribly difficult drawback nowadays. For such data many techniques can be considered. The technologies like Grid Computing, Volunteering Computing, and RDBMS can be considered as potential techniques to handle such data. We have a still in growing phase Hadoop Tool to handle such data also. We will do a survey on all this techniques to find a potential technique to manage and work with Big Data.
Sharing of cluster resources among multiple Workflow Applicationsijcsit
Many computational solutions can be expressed as workflows. A Cluster of processors is a shared
resource among several users and hence the need for a scheduler which deals with multi-user jobs
presented as workflows. The scheduler must find the number of processors to be allotted for each workflow
and schedule tasks on allotted processors. In this work, a new method to find optimal and maximum
number of processors that can be allotted for a workflow is proposed. Regression analysis is used to find
the best possible way to share available processors, among suitable number of submitted workflows. An
instance of a scheduler is created for each workflow, which schedules tasks on the allotted processors.
Towards this end, a new framework to receive online submission of workflows, to allot processors to each
workflow and schedule tasks, is proposed and experimented using a discrete-event based simulator. This
space-sharing of processors among multiple workflows shows better performance than the other methods
found in literature. Because of space-sharing, an instance of a scheduler must be used for each workflow
within the allotted processors. Since the number of processors for each workflow is known only during
runtime, a static schedule can not be used. Hence a hybrid scheduler which tries to combine the advantages
of static and dynamic scheduler is proposed. Thus the proposed framework is a promising solution to
multiple workflows scheduling on cluster.
Similar to Hybrid Job-Driven Meta Data Scheduling for BigData with MapReduce Clusters and Internet Approach (20)
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
ML for identifying fraud using open blockchain data.pptx
Hybrid Job-Driven Meta Data Scheduling for BigData with MapReduce Clusters and Internet Approach
1. IDL - International Digital Library Of
Technology & Research
Volume 1, Issue 6, June 2017 Available at: www.dbpublications.org
International e-Journal For Technology And Research-2017
IDL - International Digital Library 1 | P a g e Copyright@IDL-2017
Hybrid Job-Driven Meta Data Scheduling for
BigData with MapReduce Clusters and
Internet Approach
MOHAMMED JABEER 1
, Ms. LELAVATHI H V 2
Department of Information Science & Engineering
1 MTech, Student - RNSIT, Bangaluru, India
2 Guide & Associate Professor - RNSIT, Bangaluru, India
Abstract: It is cost-efficient for a tenant with a
limited budget to establish a virtual Map Reduce
cluster by renting multiple virtual private servers
(VPSs) from a VPS provider. To provide an
appropriate scheduling scheme for this type of
computing environment, we propose in this paper a
hybrid job-driven scheduling scheme (JoSS for
short) from a tenant’s perspective. JoSS provides
not only job level scheduling, but also map-task
level scheduling and reduce-task level scheduling.
JoSS classifies Map Reduce jobs based on job scale
and job type and designs an appropriate scheduling
policy to schedule each class of jobs. The goal is to
improve data locality for both map tasks and
reduce tasks, avoid job starvation, and improve job
execution performance. Two variations of JoSS are
further introduced to separately achieve a better
map-data locality and a faster task assignment. We
conduct extensive experiments to evaluate and
compare the two variations with current scheduling
algorithms supported by Hadoop. The results show
that the two variations outperform the other tested
algorithms in terms of map-data locality, reduce-
data locality, and network overhead without
incurring significant overhead. In addition, the two
variations are separately suitable for different Map
Reduce workload scenarios and provide the best
job performance among all tested algorithms.
Index Terms — MapReduce, Hadoop, virtual
MapReduce cluster, map-task scheduling, reduce-
task scheduling.
INTRODUCTION
Mapreduce is a suitable program did by google to
have a notice of data in subsequent manner,it is
simple,can be adapted even during any internal
failures,and mainly its an open source and they are
used by big companies which play with the data
and main business with data,Its also used in
machine learning,bio informatics, space research
etc., The other qualities is that,it helps in coding
with less pressure ,it guides them to build a good
blueprint or interface and many other tasks in
parallel. Ordinarily, a MapReduce bunch comprises
of an arrangement of product machines/hubs
situated on a few racks and connected with each
other in a Land area network The creator calls this
a traditional MapReduce bunch. Because of the
way that building and keeping up a regular
MapReduce group is expensive for a
man/association with a constrained spending plan,
an option route is to set up a virtual MapReduce
bunch by leasing a MapReduce system from a
MapReduce specialist and co- leasing different
virtual servers from a supplier (e.g.,
LinodeorFuture Hosting ). Each VPS is individual
particular working framework and circle
framework. Because of a few reasons, for example,
accessibility giving of a storage center or asset
shortageon a mainstream storage center, an
inhabitant may lease private servers from various
storage centers worked by same supplier to build
up MapReduce bunch. So the authors show interest
on MapReduce group of this sort. For a
man/association that sets up a customary group,
delineate territory in the bunch is arranged into hub
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International e-Journal For Technology And Research-2017
IDL - International Digital Library 2 | P a g e Copyright@IDL-2017
area, rack region, and off-rack since the
individual/association knows of the physical
connection among all networks and all situations.
In any case, for an inhabitant who sets up a virtual
MapReduce group, the occupant just knows each
server’s Internet address and the storage center
places Other data, for example, machine and
network that has server has a place with is
unreleased by the supplier. Consequently, from the
occupant's perspective, the guide information
territory bunch can just be classified into 3 stages
• Server-area, which is private and implies a guide
assignment and itsinput information are situated
together.
• Cen-area, which implies guide assignment, its
input are inside the same storage center, yet not
together.
• off-Cen, which implies a guide assignment and its
inputare situated at various Storage centers.
Besides, lessen information region is once in a
while tended to in a customary MapReduce group
because decreasing the space between a diminish
errand and its information coming guide
undertakings in a network is troublesome.
However, it can be done using the proposed
algorithm group including various datacenters. In
request to give a fitting planning plan to an
inhabitant to accomplish a high guide and-decrease
information area and enhance work execution in
his/her virtual MapReduce bunch, so the creators
propose a half and half employment driven booking
plan by giving booking in levels: work, outline, and
lessen assignment. JoSS groups MapReduce
occupations into either substantial or little
employments in light of each employment's
information normal storage center size bunch, and
immediate characterizes little occupations of the
same outline or lessen overwhelming in view of the
proportion between each occupation decrease
input measure and the employment guide input
estimate. At that point JoSS utilizes a specific
booking strategy to plan each class of employments
with the end goal that the relating system
movement produced amid occupation execution
(particularly for between datacenter activity) can be
decreased, and the comparing work execution can
be moved forward. What more, creators gave
varieties of JoSS, named
JoSS-T and JoSS-J, to ensure a quick errand to
expand the VPS-territory, individually. Creators
execute JoSS-T and JoSS-J in Hadoop-0.20.2 and
lead broad analyses to contrast them and a few
known planning calculations upheld by calculation,
booking calculation, and Capacity booking
calculation.
OBJECTIVES
The JoSS strategy for planning Map-Reduce
employments in a virtual MapReduce group
comprising of an arrangement of Servers leased
from a Servers supplier. Not quite the same as
present MapReduce planning calculations, JoSS
takes both the guide information territory and
diminish information area of a virtual MapReduce
bunch into thought. JoSS orders occupations into
three employment sorts, i.e., little guide substantial
occupation, little decrease overwhelming
employment, and extensive occupation, and
acquainted proper arrangements with calendar each
kind of occupation. What more, the two varieties of
JoSS are additionally acquainted with individually
accomplish a quick undertaking task and enhance
the Servers-territory. The broad test comes about
show that both JoSS-T and JoSS-J give a superior
guide information area, accomplish a higher
decrease information region, and cause a great deal
less between datacenter arrange movement as
contrasted and current planning calculations
utilized by Hadoop.The occupations of a
MapReduce workload are all little to the
fundamental virtual MapReduce bunch, utilizing
JoSS-T is more appropriate than alternate
calculations since JoSS-T gives the most limited
employment TT. Then again, when the occupations
of a The algorithm little to the virtual The
algorithm group, embracing JoSS-J is more fitting
since it prompts the most limited workload
turnaround time. Moreover, the two varieties of
JoSS have a tantamount load adjust and don force a
huge overhead on the Hadoop ace server contrasted
and alternate calculations.
About the Unformatted content information
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International e-Journal For Technology And Research-2017
IDL - International Digital Library 3 | P a g e Copyright@IDL-2017
For Unformatted text data the best example is text
data; A content document is a sort of PC record
that is organized as a grouping of content. A
content record exists inside a PC document
framework. The finish of a content document is
regularly indicated by setting at least one unique
characters, known as an end-of- record marker,
after the last line in a content document. On present
day working frameworks, for example, Windows
and Unix-like frameworks, content documents don
contain any unique EOF character.
Arrangements of content information
On most working frameworks the name content
record alludes to document organize that permits
just plain content substance with next to no
arranging ,Such records can be seen and altered on
content terminals or in straightforward word
processors. Content documents more often than not
have the MIME sort content / plain quot typically
with extra data demonstrating an encoding.
Windows content documents.
MS-DOS and Windows utilize a typical content
record organize, content isolated by a two-character
blend: carriage return (CR) and line bolster (LF). It
is basic content not to be ended with a CR-LF
marker, and numerous word processors (counting
Notepad) consequently embed at end On Windows
working frameworks, a record is viewed content
document if the postfix of the document is Be that
as it may, numerous different postfixes are utilized
for content records with particular purposes Unix
content files On Unix-like working frameworks
content records configuration is unequivocally
depicted: POSIX characterizes a content document
as a record that contains characters sorted out into
at least zero lines, where lines are arrangements of
at least zero non newline characters in addition to
an ending newline character ordinarily LF. Also,
POSIX characterizes a printable record as a content
document whose characters is printable or space or
delete as per territorial principles. This avoids
control characters, which are not printable.
EXPERIMENTAL RESULTS
In this chapter it explain the results of JoSS project
which is running in the Netbean IDE tool using the
java, java swing, AWT languages. In completion of
JoSS project it takes four modules which are
explained above here only the results of those
modules are explained.
After the successful valid user the next process is
importing the data sets, the numbers of links of
files are stored in the databases just in this process
need to extract from the databases by selecting the
link.
The data to be extracted from the internet always
the system must be connected to the internet while
running the JoSS project if its connected to internet
en it gets validates.
If the system is not connected to internet while
running the JoSS project it displays the window by
saying no internet connection as shown below.
After the validating datasets the next step is
Importing the datasets where it will imports all the
meta data from the link which is selected. To all
4. IDL - International Digital Library Of
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Volume 1, Issue 6, June 2017 Available at: www.dbpublications.org
International e-Journal For Technology And Research-2017
IDL - International Digital Library 4 | P a g e Copyright@IDL-2017
these steps to be continued the system must and
should connected to the internet.
The next step is the validate data step where it
contains all the information about the file of data
that is all the upper case letters(A-Z) and all the
lowest case letters(a-z) in the file and all the
characters, words and sentences in the file. It is the
point where the user ready to send the data to the
destination machine along with known IP address;
if the IP address is unknown then it may prone to
error.
In IaaS big data processing the
processing can be uni processing or parallel
processing first the link to be selected and it
will ask for connection to server, when it
connect to the server then shows all the details
of the particular link of data. Such as total
number of files in process if it is uni process
means only one file, total data scanned, and
total data stored. The link of file is applied for
processing by applying job scheduling. By
clicking on the button connect for parallel
processing the server is connected to internet
and a window is pop up saying that start
server, the scheduling may be different
depending on the processor such as first come
first serve, earliest time scheduling, and round
robin etc, for parallel processing there are
number of links of files to be selected, each
job will get the particular resources for
processing.
SIMULATION
In simulation of JoSS project the map data
locality results are displayed for both uni process
and parallel process. For uni process the processing
time is less when compared to the parallel
processing because of single link process faster
when compared to more files links, even the
network traffic is less in the uni processing than the
parallel processing where as both the map task and
reduce task are good enough for both uni and
parallel processing.Even the system where the JoSS
poject is running the systems network IP address is
taken fro both the uni processing and parallel
processing, The development of extraordinary scale
registering frameworks and the information blast
have introduced an uncommon open door for the
examination of frameworks at a quickly expanding
scale, any-sided quality and granularity. This
outlook change requires an intermixing of consider
the possibility than and information examination
approaches, however the universes of Simulation
and Big Data have so far been to a great extent
isolated.
CONCLUSION
The JoSS technique for booking Map-
Reduce occupations in a virtual MapReduce bunch
comprising of an arrangement of VPSs leased from
a VPS supplier. Not quite the same as present
MapReduce planning calculations, JoSS takes both
the guide information region and lessen
information territory of a virtual MapReduce group
into thought. JoSS arranges occupations into three
employment sorts, i.e., little guide overwhelming
occupation, little decrease substantial occupation,
and extensive occupation, and acquainted fitting
approaches with calendar each kind of
employment. What's more, the two varieties
of JoSS (i.e., JoSS-T and JoSS-J) are additionally
acquainted with individually accomplish a quick
errand task and enhance the VPS-area. The broad
trial comes about exhibit that both JoSS-T and
JoSS-J give a superior guide information area,
accomplish a higher diminish information territory,
and cause a great deal less between datacenter
organize activity as contrasted and current planning
calculations utilized by Hadoop.The occupations of
a MapReduce workload are all little to the
fundamental virtual MapReduce group, utilizing
JoSS-T is more appropriate than alternate
calculations since JoSS-T gives the most limited
5. IDL - International Digital Library Of
Technology & Research
Volume 1, Issue 6, June 2017 Available at: www.dbpublications.org
International e-Journal For Technology And Research-2017
IDL - International Digital Library 5 | P a g e Copyright@IDL-2017
employment turnaround time. Then again, when
the employments of a MapReduce workload are not
all little to the virtual MapReduce group, receiving
JoSS-J is more fitting since it prompts the most
brief workload turnaround time. What more, the
two varieties of JoSS have a similar load adjust and
don force a noteworthy overhead on the Hadoop
ace server contrasted and alternate calculations.
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