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.
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.
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.
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.
Asserting that Big Data is vital to business is an understatement. Organizations have generated more and more data for years, but struggle to use it effectively. Clearly Big Data has more important uses than ensuring compliance with regulatory requirements. In addition, data is being generated with greater velocity, due to the advent of new pervasive devices (e.g., smartphones, tablets, etc.), social Web sites (e.g., Facebook, Twitter, LinkedIn, etc.) and other sources like GPS, Google Maps, heat/pressure sensors, etc.
Large amount of data are produced daily from various fields such as science, economics,
engineering and health. The main challenge of pervasive computing is to store and analyze large amount of
data.This has led to the need for usable and scalable data applications and storage clusters. In this article, we
examine the hadoop architecture developed to deal with these problems. The Hadoop architecture consists of
the Hadoop Distributed File System (HDFS) and Mapreduce programming model, which enables storage and
computation on a set of commodity computers. In this study, a Hadoop cluster consisting of four nodes was
created.Regarding the data size and cluster size, Pi and Grep MapReduce applications, which show the effect of
different data sizes and number of nodes in the cluster, have been made and their results examined.
The data management industry has matured over the last three decades, primarily based on relational database management system(RDBMS) technology. Since the amount of data collected, and analyzed in enterprises has increased several folds in volume, variety and velocityof generation and consumption, organisations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. In this paper we will trace the origin of new class of system called Hadoop to handle Big data.
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.
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.
Asserting that Big Data is vital to business is an understatement. Organizations have generated more and more data for years, but struggle to use it effectively. Clearly Big Data has more important uses than ensuring compliance with regulatory requirements. In addition, data is being generated with greater velocity, due to the advent of new pervasive devices (e.g., smartphones, tablets, etc.), social Web sites (e.g., Facebook, Twitter, LinkedIn, etc.) and other sources like GPS, Google Maps, heat/pressure sensors, etc.
Large amount of data are produced daily from various fields such as science, economics,
engineering and health. The main challenge of pervasive computing is to store and analyze large amount of
data.This has led to the need for usable and scalable data applications and storage clusters. In this article, we
examine the hadoop architecture developed to deal with these problems. The Hadoop architecture consists of
the Hadoop Distributed File System (HDFS) and Mapreduce programming model, which enables storage and
computation on a set of commodity computers. In this study, a Hadoop cluster consisting of four nodes was
created.Regarding the data size and cluster size, Pi and Grep MapReduce applications, which show the effect of
different data sizes and number of nodes in the cluster, have been made and their results examined.
The data management industry has matured over the last three decades, primarily based on relational database management system(RDBMS) technology. Since the amount of data collected, and analyzed in enterprises has increased several folds in volume, variety and velocityof generation and consumption, organisations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. In this paper we will trace the origin of new class of system called Hadoop to handle Big data.
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.
This presentation provides a comprehensive introduction to the Hadoop Distributed System, a powerful and widely used framework for distributed storage and processing of large-scale data. Hadoop has revolutionized the way organizations manage and analyze data, making it a crucial tool in the field of big data and data analytics.
In this presentation, we explore the key components and features of Hadoop, shedding light on the fundamental building blocks that enable its exceptional data processing capabilities. We cover essential topics, including the Hadoop Distributed File System (HDFS), MapReduce, YARN (Yet Another Resource Negotiator), and Hadoop Ecosystem components like Hive, Pig, and Spark.
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.
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.
Presentation regarding big data. The presentation also contains basics regarding Hadoop and Hadoop components along with their architecture. Contents of the PPT are
1. Understanding Big Data
2. Understanding Hadoop & It’s Components
3. Components of Hadoop Ecosystem
4. Data Storage Component of Hadoop
5. Data Processing Component of Hadoop
6. Data Access Component of Hadoop
7. Data Management Component of Hadoop
8.Hadoop Security Management Tool: Knox ,Ranger
Harnessing Hadoop: Understanding the Big Data Processing Options for Optimizi...Cognizant
A guide to using Apache Hadoop as your open source big data platform of choice, including the vendors that make various Hadoop flavors, related open source tools, Hadoop capabilities and suitable applications.
LARGE-SCALE DATA PROCESSING USING MAPREDUCE IN CLOUD COMPUTING ENVIRONMENTijwscjournal
The computer industry is being challenged to develop methods and techniques for affordable data processing on large datasets at optimum response times. The technical challenges in dealing with the increasing demand to handle vast quantities of data is daunting and on the rise. One of the recent processing models with a more efficient and intuitive solution to rapidly process large amount of data in parallel is called MapReduce. It is a framework defining a template approach of programming to perform large-scale data computation on clusters of machines in a cloud computing environment. MapReduce provides automatic parallelization and distribution of computation based on several processors. It hides the complexity of writing parallel and distributed programming code. This paper provides a comprehensive systematic review and analysis of large-scale dataset processing and dataset handling challenges and
requirements in a cloud computing environment by using the MapReduce framework and its open-source implementation Hadoop. We defined requirements for MapReduce systems to perform large-scale data processing. We also proposed the MapReduce framework and one implementation of this framework on Amazon Web Services. At the end of the paper, we presented an experimentation of running MapReduce
system in a cloud environment. This paper outlines one of the best techniques to process large datasets is MapReduce; it also can help developers to do parallel and distributed computation in a cloud environment.
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.
AN OVERVIEW OF BIGDATA AND HADOOP . THE ARCHITECHTURE IT USES AND THE WAY IT WORKS ON THE DATA SETS. THE SIDES ALSO SHOW THE VARIOUS FIELDS WHERE THEY ARE MOSTLY USED AND IMPLIMENTED
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Mission to Decommission: Importance of Decommissioning Products to Increase E...
D04501036040
1. ISSN (e): 2250 – 3005 || Vol, 04 || Issue, 5 || May – 2014 ||
International Journal of Computational Engineering Research (IJCER)
www.ijceronline.com Open Access Journal Page 36
A Review on HADOOP MAPREDUCE-A Job Aware Scheduling
Technology
Silky Kalra1,
Anil lamba2
1 M.Tech Scholar, Computer Science & Engineering, Haryana Engineering College Jagadhari, Haryana,
2 Associate Professor, Department of Computer Science & Engineering, Haryana Engineering College
Jagadhari, Haryana
I. INTRODUCTION
MapReduce is currently the most famous framework for data intensive computing. MapReduce is
motivated by the demands of processing huge amounts of data from a web environment. MapReduce provides
an easy parallel programming interface in a distributed computing environment. Also MapReduce deals with
fault tolerance issues for managing multiple processing nodes. The most powerful feature of MapReduce is its
high scalability that allows user to process a vast amount of data in a short time. There are many fields that
benefit from MapReduce, such as Bioinformatics, machine learning, scientific analysis, web data analysis,
astrophysics, and security. There are some implemented systems for data intensive computing, such as Hadoop
An open source framework, Hadoop resembles the original MapReduce.
With increasing use of the Internet, Internet attacks are on the rise. Distributed Denial-of-Service
(DDoS) in particular is increasing more. There are four main ways to protect against DDoS attacks: attack
prevention, attack detection, attack source identification, and attack reaction. DDoS attack is one such threat
which is distributed form of Denial of Service attack in which service is consumed by an attacker and legitimate
user can not use the service. DDoS attack is one such threat which is distributed form of Denial of Service
attack in which service is consumed by attacker and legitimate user can not use the service. We can find a
solution against DDoS attack, but they are based on a single host and lacks performance so here Hadoop system
for distributed processing is used.
II. HADOOP
The GMR( Google map reduce) was invented by Google back in their earlier days so they could
usefully index all the rich textural and structural information they were collecting, and then present meaningful
and actionable results to users. MapReduce( you map the operation out to all of those servers and then you
reduce the results back into a single result set), is a software paradigm for processing a large data set in a
distributed parallel way. Since Google’s MapReduce and Google file system (GFS) are proprietary, an open-
source MapReduce software project, Hadoop, was launched to provide similar capabilities of the Google’s
MapReduce platform by using thousands of cluster nodes[1]. Hadoop distributed file system (HDFS) is also an
important component of Hadoop, that corresponds to GFS. Hadoop consists of two core components: the job
management framework that handles the map and reduces tasks and the Hadoop Distributed File System
(HDFS). Hadoop's job management framework is highly reliable and available, using techniques such as
replication and automated restart of failed tasks.
ABSTRACT:
Big data technology remodels many business organization perspective on the data. conventionally, a
data framework was like a gatekeeper for data access. such frameworks were built as monolithic “scale
up”, self contained appliances. Any added scale required added resources, which often exponentially
multiplies cost. One of the key approaches that have been at the center of the big data technology
landscape is Hadoop. This research paper includes detailed view of various important components of
Hadoop, job aware scheduling algorithms for mapreduce framework, various DDOS attack and defense
methods.
KEYWORDS: Distributed Denial-of-Service (DDOS), Hadoop, Job aware scheduling, Mapreduce,
2. A Review On HADOOP MAPREDUCE-A Job Aware Scheduling Technology
www.ijceronline.com Open Access Journal Page 37
2.1 Hadoop cluster architecture
A small Hadoop cluster includes a single master and multiple worker nodes. The master node consists
of a JobTracker, TaskTracker, NameNode and DataNode. A slave or worker node acts as both a DataNode and
TaskTracker, though it is possible to have data-only worker nodes and compute-only worker nodes. These are
normally used only in nonstandard applications. Hadoop requires Java Runtime Environment (JRE) 1.6 or
higher. The standard start-up and shutdown scripts require Secure Shell to be set up between nodes in the
cluster.
In a larger cluster, the HDFS is managed through a dedicated NameNode server to host the file system
index, and a secondary NameNode that can generate snapshots of the namenode's memory structures, thus
preventing file-system corruption and reducing loss of data.
Figure 1. shows Hadoop multinode cluster architecture which works in distributed manner for MapReduce
problem.
Similarly, a standalone JobTracker server can manage job scheduling. In clusters where the Hadoop MapReduce
engine is deployed against an alternate file system, the NameNode, secondary NameNode and DataNode
architecture of HDFS is replaced by the file-system-specific equivalent.
2.2 Hadoop distributed file system
HDFS stores large files (typically in the range of gigabytes to terabytes[13]
) across multiple machines. It
achieves the reliability by replicating the data across multiple hosts, and hence does theoretically not require
RAID storage on hosts (but to increase I/O performance some RAID configurations are still useful). With the
default replication value, 3, data is stored on three nodes: two on the same rack, and one on a different rack.
The HDFS file system includes a so-called secondary namenode, which misleads some people into
thinking [
citation needed]
that when the primary namenode goes offline, the secondary namenode takes over. In
fact, the secondary namenode regularly connects with the primary namenode and builds snapshots of the
primary namenode's directory information, which the system then saves to local or remote directories. These
checkpoint images can be used to restart a failed primary namenode without having to replay the entire journal
of file-system actions, then to edit the log to create an up-to-date directory structure. Because the namenode is
the single point for storage and management of metadata, it can become a bottleneck for supporting a huge
number of files, especially a large number of small files. HDFS Federation, a new addition, aims to tackle this
problem to a certain extent by allowing multiple name-spaces served by separate namenodes.
An advantage of using HDFS is data awareness between the job tracker and task tracker. The job
tracker schedules map or reduce jobs to task trackers with an awareness of the data location. For example: if
node A contains data (x,y,z) and node B contains data (a,b,c), the job tracker schedules node B to perform map
or reduce tasks on (a,b,c) and node A would be scheduled to perform map or reduce tasks on (x,y,z). This
reduces the amount of traffic that 1111goes over the network and prevents unnecessary data transfer.
3. A Review On HADOOP MAPREDUCE-A Job Aware Scheduling Technology
www.ijceronline.com Open Access Journal Page 38
2.3 Hadoop V/S Dbms
However, no compelling reason to choose MR over a database for traditional database workloads
MapReduce is designed for one-off processing tasks
– Where fast load times are important
– No repeated access. It decomposes queries into sub-jobs, schedules them with different policies.
DBMS HADOOP
In a centralized database system, you’ve got
one big disk connected to four or eight or 16
big processors. But that is as much
horsepower as you can bring to bear
In a Hadoop cluster, every one of those servers has two or four
or eight CPUs. You can run your indexing job by sending your
code to each of the dozens of servers in your cluster, and each
server operates on its own little piece of the data. Results are
then delivered back to you in a unified whole.
To DBMS researchers, programming model
doesn’t feel new
Hadoop MapReduce is a new way of thinking about
programming large distributed systems
Schemas:
DBMS require them
Schemas:
– MapReduce doesn’t require them
– Easy to write simple MR problems
– No logical data independence
Table1. Comparison of Approaches
III. DISTRIBUTED DENIAL OF SERVICE ATTACK
A denial-of-service attack (DoS attack) is an attempt to make a computer resource unavailable to its
intended users[1]. Distributed denial-of-service attack (DDoS attack) is a kind of DoS attack where attackers are
distributed and targeting a victim. It generally consists of the concerted efforts of a person, or multiple people to
prevent an Internet site or service from functioning efficiently or at all, temporarily or indefinitely.
3.1 How DDoS Attack works
DDoS attack is performed by infected machines called bots and a group of bots is called a botnet. This
bots (Zombie) are controlled by an attacker by installing malicious code or software which acts as per command
passed by an attacker. Bots are ready to attack any time upon receiving commands from the attacker. Many
types of agents have scanning capability that permit to identify open port of a range of machines. When the
scanning is finished, the agent takes the list of machines with open port and launches vulnerability-specific
scanning to detect machines with un-patched vulnerability. If the agent found a machine with vulnerability, it
could launch an attack to install another agent on the machine.
4. A Review On HADOOP MAPREDUCE-A Job Aware Scheduling Technology
www.ijceronline.com Open Access Journal Page 39
Fig 2. shows DDoS attack architecture, explaining its working mechanism.
IV. FAIR SCHEDULER
The core idea behind the fair scheduler is to assign resources to jobs such that on average over time, each job gets
an equal share of the available resources. This behavior allows for some interactivity among Hadoop jobs and permits
greater responsiveness of the Hadoop cluster to the variety of job types submitted.
The number of jobs active at one time can also be constrained, if desired, to minimize congestion and allow work
to finish in a timely manner. To ensure fairness, each user is assigned to a pool. In this way, if one user submits many jobs,
he or she can receive the same share of cluster resources as all other users (independent of the work they have submitted).
V. 5. RELATED STUDY
In [1], Prashant Chauhan, Abdul Jhummarwala , Manoj Pandya in December, 2012 provided an overview of
Hadoop. This type of computing can have a homogeneous or a heterogeneous platform and hardware. The concept of cloud
computing and virtualization has derived much momentum and has turned a more popular phrase in information technology.
Many organizations have started implementing these new technologies to further cut down costs through improved machine
utilization, reduced administration time and infrastructure costs. Cloud computing also confronts challenges. One of such
problem is DDoS attack so in this paper author will focus on DDoS attack and how to overcome from it using honeypot. For
this here open source tools and software are used. Typical DDoS solution mechanism is a single host oriented and in this
paper focused on a distributed host oriented solution that meets scalability.
In [2], Jin-Hyun Yoon, Ho-Seok Kang and Sung-Ryul Kim, in 2012, proposed a technique called "triangle
expectation‖ is used, which works to find the sources of the attack so that they can be identified and blocked. To analyze a
large amount of collecting network connection data, a sampling technique has been used and the proposed technique is
verified by experiments.
In [3], B. B. Gupta, R. C. Joshia, Manoj Misra, in 2009, the main aim of this paper is First is to demonstrate a
comprehensive study of a broad range of DDoS attacks and defense methods proposed to fight with them. This provides a
better understanding of the problem, current solution space, and future research scope to fight down against DDoS attacks.
Second is to offer an integrated solution for entirely defending against flooding DDoS attacks at the Internet Service
Provider (ISP) level.
In [4], Yeonhee Lee, Youngseok Lee, in 2011 proposed a novel DDoS detection method based on Hadoop that
implements an HTTP GET flooding detection algorithm in MapReduce on the distributed computing platform.
In [5], Matei Zaharia, Dhruba Borthakur, Joydeep Sen Sarma, Khaled Elmeleegy, Scott Shenker, Ion Stoica, in
April 2009, provided an overview of Sharing a MapReduce cluster between users. It is attractive because it enables statistical
multiplexing (lowering costs) and allows users to share a common large data set. They evolved two simple techniques, delay
scheduling and copy-compute splitting, which improve throughput and response times by factors of 2 to 10. Although we
concentrate on multi-user workloads, our techniques can also increase throughput in a single-user, FIFO workload by a
factor of 2.