Final Project Proposal. CSCI.6500 Distributed Computing over the Internet

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Final Project Proposal. CSCI.6500 Distributed Computing over the Internet"

Transcription

1 Final Project Proposal CSCI.6500 Distributed Computing over the Internet Qingling Wang Purpose Implement an application layer on Hybrid Grid Cloud Infrastructure to automatically or at least semi automatically determine the best environment settings for the tasks. It will simplify the procedure to deploy computational tasks on Cloud or Hybrid Grid Cloud Infrastructure, especially for the end user who knows a little about technical details of Cloud or Grid Computing. The user can get different computing performance by adjusting some parameters,, like time consuming, cost, etc. It doesn t need to restart the whole bunch of tasks. It will suspend to get the new settings, and resume computing from the stop point. 2. Background Cloud Computing There is no concrete definition for Cloud Computing yet. Generally speaking, it is a reliable, virtualized, internet based architecture to provide no demand computing, storage and many other services. Shared resources and virtualization are the key features. Cloud Computing has the following outstanding advantages. 1. Enable users to access data everywhere. 2. Provide on demand services 3. Provide more powerful computing capacities 4. Have a more reliable and flexible architecture, guarantee QoS for users. All the organizations are impressed with the computing, storage and all such powerful abilities. Indeed, Cloud provides on demand, scalable and fault tolerance services with globally scattered hardware resources. But Cloud Computing is not a universal method, architecture. It may give a low performance in some situation. 1. It is hard to evaluate exactly how many compute instances, data transfers still needed. 2. The coordination between different virtual machines will degrade the performance. 3. It s difficult to separate the data into independent data set. 4. For individual customer or some small group, it is still very expensive to use the public cloud computing services.

2 Usually, an existing grid has been used for years inside an organization. It is a waste to give up all the grid resources. The ideal way is to find a proper way and time to combine Grid with Cloud, such that it can maximize the computing capability with somehow lower cost. Hadoop The Apache Hadoop project develops open source software for reliable, scalable, distributed computing. It is a large scale distributed processing infrastructure designed to efficiently distribute large amounts of work across a set of machines. Hadoop MapReduce is a programming model and software framework for writing applications that rapidly process vast amounts of data in parallel on large clusters of compute nodes. The application is divided into small work units, and each unit can be executed in any computational node. The framework also has a distributed file system, which stores data on the various nodes. Thus, Hadoop MapReduce is suitable to test the performance of Grid, Cloud and Grid&Cloud (G&C) on different data sizes. Usually, one of the machines (physical machine in Grid, virtual machine in Cloud, physical or virtual machine in G&C) acts as the master, who is responsible for scheduling tasks among other machines (slaves). The master machine can also be responsible for executing tasks. End User s Concerns As an end user, they don t care what environment they are using. But the performance, cost and easy to use are the most important issues for a framework. Most paper about Cloud Computing is still focus on introducing what is Cloud Computing, the advantages on using Cloud Computing. Actually, the user, especially who has little knowledge about Grid/Cloud Computing needs more concrete results data to compare, to get a understanding with it. Thus, real experimental data would help a lot. 3. Scope Hadoop MapReduce, helps to get the experimental results on Grid, Cloud and Hybrid Grid Cloud. Computation intensive tasks, I will use a linear classification algorithm in Machine Learning domain, called Pocket Algorithm to run on Hadoop, and get results from different environments. Grid, RPI Grid, includes several single core and some dual core physical machines. Cloud, will run virtual machines on same physical machines as Grid, use Xen hosts the VM instances. 4. Framework

3 There are mainly three layers in this framework. Client Input Parser Initialize input Refine Demand Schedule Process Performance Evaluation Temporary Staging Storage XML Configure File Hadoop (MapReduce) Request Public Cloud Grid Cloud Xen Public Cloud Master The bottom layer is the whole physical environment. A Grid which is comprised of several single core or dual core severs. The Cloud uses the same server machines as the Grid, but run on Xen, such that the Cloud can host numbers of Virtual Instances. In this layer, there is also a public Cloud (i.e. Amazon EC2), from which we can get extra computing capabilities when necessary. The second layer is Hadoop, which is deployed on the bottom layer. We mainly use MapReduce, sometimes maybe HBase to execute the computational task. MapReduce is responsible for splitting the data, distributing it on different DataNode and collect the distributed results. HBase is used to store the intermediate results or some other useful

4 status info. The top layer is what we need to implement. It is responsible for interacting with end users. It is composed by three correlated components, Client Input Parser, Temporary Staging Storage and Schedule Process. 5. Method Generally speaking, there are two parts. The first part is to collect experimental results, and analyze the data, then get some valuable conclusion. We will use Hadoop to do the experiment. The Hadoop has an inbuilt command called jar, which supports executing the jar file. The experiment will be computation intensive. In the java project, we will use a famous algorithm, Pocket Algorithm from Machine Learning to separate two different sets of points (totally 2000 uniquely random points). It takes almost 2 hours to run in the laptop, and almost one hour in the server. So it is suitable for computation intensive experiment. We will collect all the statistics from this experiment on Grid, Cloud and Hybrid Grid Cloud. Then analyze the reason for different performance on different environment, and conclude what are the best settings for different input (size change, blocks splitting change and so on) and different requirements from end user. The second part is the implementation part. Based on the conclusion, we write an application layer with three related components, Client Input Parser, Schedule Process and Temporary Staging Storage. It automatic or at least semi automatically map user s demand on proper running environment, execute the task and return the results back. The user can also change the demand parameters during execution, but only on some time points. The Client Input Parser will take client s input and parse it into a XML file. The schedule process will take the XML file as input, based on the current environment computation capacity, and then give several practical plans to execute the task. The schedule process will analyze the pros and cons of each plan, mainly on aspects of time consuming, cost, fault tolerance and etc. It will return the analysis form back to user. Users can choose either plan they want by inputting from the application layer interface. The Schedule Process will choose the plan with best performance in case that the user doesn t choose any of the plans. During the execution, the user can reset their demand settings but only on some interruptible points. Then the Schedule process will suspend on this point, save all the parameters, states and other useful status into the Temporary Staging Storage. It generates a new configure file for the environment setting, which satisfies the end user s new requirements. Last, the Schedule process will resume and run Hadoop again to get new results. Sometimes, the computing capability inside the organization (this includes the Grid, the private Cloud) is not enough. We need to request the public Cloud Computing capabilities, like Amazon EC2. In our method, if all the feasible plans cannot satisfy client s requirement, we will then turn to consider public cloud. Then the cost and privacy will become the biggest

5 issue here. 6. Timetable Date MileStone Experiment Preparation Finishing the MapReduce version of Pocket Algorithm Finishing the computation intensive experiments on all environments Implementation Part Implement Client Input Parser and Temporary Staging Storage of the Application Layer Implement Schedule Process of the Application Layer Test it, Case Study using this Application Layer Conclusion Conclusion, Future Work 7. Reference The reference paper is also what I will present. Hyunjoo Kim, Yaakoub el Khamra, Shantenu Jha, Manish Parashar, Exploring Application and Infrastructure Adaptation on Hybrid Grid Cloud Infrastructure, in HPDC 10: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, pages ,Chicago, Illinois, USA, 2010.

Lecture 32 Big Data. 1. Big Data problem 2. Why the excitement about big data 3. What is MapReduce 4. What is Hadoop 5. Get started with Hadoop

Lecture 32 Big Data. 1. Big Data problem 2. Why the excitement about big data 3. What is MapReduce 4. What is Hadoop 5. Get started with Hadoop Lecture 32 Big Data 1. Big Data problem 2. Why the excitement about big data 3. What is MapReduce 4. What is Hadoop 5. Get started with Hadoop 1 2 Big Data Problems Data explosion Data from users on social

More information

Hadoop Architecture. Part 1

Hadoop Architecture. Part 1 Hadoop Architecture Part 1 Node, Rack and Cluster: A node is simply a computer, typically non-enterprise, commodity hardware for nodes that contain data. Consider we have Node 1.Then we can add more nodes,

More information

Neptune. A Domain Specific Language for Deploying HPC Software on Cloud Platforms. Chris Bunch Navraj Chohan Chandra Krintz Khawaja Shams

Neptune. A Domain Specific Language for Deploying HPC Software on Cloud Platforms. Chris Bunch Navraj Chohan Chandra Krintz Khawaja Shams Neptune A Domain Specific Language for Deploying HPC Software on Cloud Platforms Chris Bunch Navraj Chohan Chandra Krintz Khawaja Shams ScienceCloud 2011 @ San Jose, CA June 8, 2011 Cloud Computing Three

More information

Chapter 7. Using Hadoop Cluster and MapReduce

Chapter 7. Using Hadoop Cluster and MapReduce Chapter 7 Using Hadoop Cluster and MapReduce Modeling and Prototyping of RMS for QoS Oriented Grid Page 152 7. Using Hadoop Cluster and MapReduce for Big Data Problems The size of the databases used in

More information

http://www.paper.edu.cn

http://www.paper.edu.cn 5 10 15 20 25 30 35 A platform for massive railway information data storage # SHAN Xu 1, WANG Genying 1, LIU Lin 2** (1. Key Laboratory of Communication and Information Systems, Beijing Municipal Commission

More information

Analysis and Research of Cloud Computing System to Comparison of Several Cloud Computing Platforms

Analysis and Research of Cloud Computing System to Comparison of Several Cloud Computing Platforms Volume 1, Issue 1 ISSN: 2320-5288 International Journal of Engineering Technology & Management Research Journal homepage: www.ijetmr.org Analysis and Research of Cloud Computing System to Comparison of

More information

Hadoop IST 734 SS CHUNG

Hadoop IST 734 SS CHUNG Hadoop IST 734 SS CHUNG Introduction What is Big Data?? Bulk Amount Unstructured Lots of Applications which need to handle huge amount of data (in terms of 500+ TB per day) If a regular machine need to

More information

CSE 590: Special Topics Course ( Supercomputing ) Lecture 10 ( MapReduce& Hadoop)

CSE 590: Special Topics Course ( Supercomputing ) Lecture 10 ( MapReduce& Hadoop) CSE 590: Special Topics Course ( Supercomputing ) Lecture 10 ( MapReduce& Hadoop) Rezaul A. Chowdhury Department of Computer Science SUNY Stony Brook Spring 2016 MapReduce MapReduce is a programming model

More information

UPS battery remote monitoring system in cloud computing

UPS battery remote monitoring system in cloud computing , pp.11-15 http://dx.doi.org/10.14257/astl.2014.53.03 UPS battery remote monitoring system in cloud computing Shiwei Li, Haiying Wang, Qi Fan School of Automation, Harbin University of Science and Technology

More information

Hadoop Scheduler w i t h Deadline Constraint

Hadoop Scheduler w i t h Deadline Constraint Hadoop Scheduler w i t h Deadline Constraint Geetha J 1, N UdayBhaskar 2, P ChennaReddy 3,Neha Sniha 4 1,4 Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore,

More information

Apache Hadoop. Alexandru Costan

Apache Hadoop. Alexandru Costan 1 Apache Hadoop Alexandru Costan Big Data Landscape No one-size-fits-all solution: SQL, NoSQL, MapReduce, No standard, except Hadoop 2 Outline What is Hadoop? Who uses it? Architecture HDFS MapReduce Open

More information

Sector vs. Hadoop. A Brief Comparison Between the Two Systems

Sector vs. Hadoop. A Brief Comparison Between the Two Systems Sector vs. Hadoop A Brief Comparison Between the Two Systems Background Sector is a relatively new system that is broadly comparable to Hadoop, and people want to know what are the differences. Is Sector

More information

Open source Google-style large scale data analysis with Hadoop

Open source Google-style large scale data analysis with Hadoop Open source Google-style large scale data analysis with Hadoop Ioannis Konstantinou Email: ikons@cslab.ece.ntua.gr Web: http://www.cslab.ntua.gr/~ikons Computing Systems Laboratory School of Electrical

More information

Cloud computing - Architecting in the cloud

Cloud computing - Architecting in the cloud Cloud computing - Architecting in the cloud anna.ruokonen@tut.fi 1 Outline Cloud computing What is? Levels of cloud computing: IaaS, PaaS, SaaS Moving to the cloud? Architecting in the cloud Best practices

More information

An Open MPI-based Cloud Computing Service Architecture

An Open MPI-based Cloud Computing Service Architecture An Open MPI-based Cloud Computing Service Architecture WEI-MIN JENG and HSIEH-CHE TSAI Department of Computer Science Information Management Soochow University Taipei, Taiwan {wjeng, 00356001}@csim.scu.edu.tw

More information

International Journal of Engineering Research & Management Technology

International Journal of Engineering Research & Management Technology International Journal of Engineering Research & Management Technology March- 2015 Volume 2, Issue-2 Survey paper on cloud computing with load balancing policy Anant Gaur, Kush Garg Department of CSE SRM

More information

Daniel J. Adabi. Workshop presentation by Lukas Probst

Daniel J. Adabi. Workshop presentation by Lukas Probst Daniel J. Adabi Workshop presentation by Lukas Probst 3 characteristics of a cloud computing environment: 1. Compute power is elastic, but only if workload is parallelizable 2. Data is stored at an untrusted

More information

Hadoop. MPDL-Frühstück 9. Dezember 2013 MPDL INTERN

Hadoop. MPDL-Frühstück 9. Dezember 2013 MPDL INTERN Hadoop MPDL-Frühstück 9. Dezember 2013 MPDL INTERN Understanding Hadoop Understanding Hadoop What's Hadoop about? Apache Hadoop project (started 2008) downloadable open-source software library (current

More information

Hadoop: A Framework for Data- Intensive Distributed Computing. CS561-Spring 2012 WPI, Mohamed Y. Eltabakh

Hadoop: A Framework for Data- Intensive Distributed Computing. CS561-Spring 2012 WPI, Mohamed Y. Eltabakh 1 Hadoop: A Framework for Data- Intensive Distributed Computing CS561-Spring 2012 WPI, Mohamed Y. Eltabakh 2 What is Hadoop? Hadoop is a software framework for distributed processing of large datasets

More information

Infrastructure as a Service (IaaS)

Infrastructure as a Service (IaaS) Infrastructure as a Service (IaaS) (ENCS 691K Chapter 4) Roch Glitho, PhD Associate Professor and Canada Research Chair My URL - http://users.encs.concordia.ca/~glitho/ References 1. R. Moreno et al.,

More information

The Quest for Conformance Testing in the Cloud

The Quest for Conformance Testing in the Cloud The Quest for Conformance Testing in the Cloud Dylan Yaga Computer Security Division Information Technology Laboratory National Institute of Standards and Technology NIST/ITL Computer Security Division

More information

Reference Architecture and Best Practices for Virtualizing Hadoop Workloads Justin Murray VMware

Reference Architecture and Best Practices for Virtualizing Hadoop Workloads Justin Murray VMware Reference Architecture and Best Practices for Virtualizing Hadoop Workloads Justin Murray ware 2 Agenda The Hadoop Journey Why Virtualize Hadoop? Elasticity and Scalability Performance Tests Storage Reference

More information

Introduction to Cloud Computing

Introduction to Cloud Computing Introduction to Cloud Computing Cloud Computing I (intro) 15 319, spring 2010 2 nd Lecture, Jan 14 th Majd F. Sakr Lecture Motivation General overview on cloud computing What is cloud computing Services

More information

A STUDY ON HADOOP ARCHITECTURE FOR BIG DATA ANALYTICS

A STUDY ON HADOOP ARCHITECTURE FOR BIG DATA ANALYTICS A STUDY ON HADOOP ARCHITECTURE FOR BIG DATA ANALYTICS Dr. Ananthi Sheshasayee 1, J V N Lakshmi 2 1 Head Department of Computer Science & Research, Quaid-E-Millath Govt College for Women, Chennai, (India)

More information

Elastic Cloud Computing in the Open Cirrus Testbed implemented via Eucalyptus

Elastic Cloud Computing in the Open Cirrus Testbed implemented via Eucalyptus Elastic Cloud Computing in the Open Cirrus Testbed implemented via Eucalyptus International Symposium on Grid Computing 2009 (Taipei) Christian Baun The cooperation of and Universität Karlsruhe (TH) Agenda

More information

MapReduce and Hadoop Distributed File System

MapReduce and Hadoop Distributed File System MapReduce and Hadoop Distributed File System 1 B. RAMAMURTHY Contact: Dr. Bina Ramamurthy CSE Department University at Buffalo (SUNY) bina@buffalo.edu http://www.cse.buffalo.edu/faculty/bina Partially

More information

Welcome to the unit of Hadoop Fundamentals on Hadoop architecture. I will begin with a terminology review and then cover the major components

Welcome to the unit of Hadoop Fundamentals on Hadoop architecture. I will begin with a terminology review and then cover the major components Welcome to the unit of Hadoop Fundamentals on Hadoop architecture. I will begin with a terminology review and then cover the major components of Hadoop. We will see what types of nodes can exist in a Hadoop

More information

marlabs driving digital agility WHITEPAPER Big Data and Hadoop

marlabs driving digital agility WHITEPAPER Big Data and Hadoop marlabs driving digital agility WHITEPAPER Big Data and Hadoop Abstract This paper explains the significance of Hadoop, an emerging yet rapidly growing technology. The prime goal of this paper is to unveil

More information

USING VIRTUAL MACHINE REPLICATION FOR DYNAMIC CONFIGURATION OF MULTI-TIER INTERNET SERVICES

USING VIRTUAL MACHINE REPLICATION FOR DYNAMIC CONFIGURATION OF MULTI-TIER INTERNET SERVICES USING VIRTUAL MACHINE REPLICATION FOR DYNAMIC CONFIGURATION OF MULTI-TIER INTERNET SERVICES Carlos Oliveira, Vinicius Petrucci, Orlando Loques Universidade Federal Fluminense Niterói, Brazil ABSTRACT In

More information

NoSQL and Hadoop Technologies On Oracle Cloud

NoSQL and Hadoop Technologies On Oracle Cloud NoSQL and Hadoop Technologies On Oracle Cloud Vatika Sharma 1, Meenu Dave 2 1 M.Tech. Scholar, Department of CSE, Jagan Nath University, Jaipur, India 2 Assistant Professor, Department of CSE, Jagan Nath

More information

A Middleware Strategy to Survive Compute Peak Loads in Cloud

A Middleware Strategy to Survive Compute Peak Loads in Cloud A Middleware Strategy to Survive Compute Peak Loads in Cloud Sasko Ristov Ss. Cyril and Methodius University Faculty of Information Sciences and Computer Engineering Skopje, Macedonia Email: sashko.ristov@finki.ukim.mk

More information

Problem Solving Hands-on Labware for Teaching Big Data Cybersecurity Analysis

Problem Solving Hands-on Labware for Teaching Big Data Cybersecurity Analysis , 22-24 October, 2014, San Francisco, USA Problem Solving Hands-on Labware for Teaching Big Data Cybersecurity Analysis Teng Zhao, Kai Qian, Dan Lo, Minzhe Guo, Prabir Bhattacharya, Wei Chen, and Ying

More information

Introduction to Cloud Computing

Introduction to Cloud Computing Introduction to Cloud Computing Qloud Demonstration 15 319, spring 2010 3 rd Lecture, Jan 19 th Suhail Rehman Time to check out the Qloud! Enough Talk! Time for some Action! Finally you can have your own

More information

Contents. Preface Acknowledgements. Chapter 1 Introduction 1.1

Contents. Preface Acknowledgements. Chapter 1 Introduction 1.1 Preface xi Acknowledgements xv Chapter 1 Introduction 1.1 1.1 Cloud Computing at a Glance 1.1 1.1.1 The Vision of Cloud Computing 1.2 1.1.2 Defining a Cloud 1.4 1.1.3 A Closer Look 1.6 1.1.4 Cloud Computing

More information

From Grid Computing to Cloud Computing & Security Issues in Cloud Computing

From Grid Computing to Cloud Computing & Security Issues in Cloud Computing From Grid Computing to Cloud Computing & Security Issues in Cloud Computing Rajendra Kumar Dwivedi Assistant Professor (Department of CSE), M.M.M. Engineering College, Gorakhpur (UP), India E-mail: rajendra_bhilai@yahoo.com

More information

Keywords Distributed Computing, On Demand Resources, Cloud Computing, Virtualization, Server Consolidation, Load Balancing

Keywords Distributed Computing, On Demand Resources, Cloud Computing, Virtualization, Server Consolidation, Load Balancing Volume 5, Issue 1, January 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Survey on Load

More information

Cloud Computing based on the Hadoop Platform

Cloud Computing based on the Hadoop Platform Cloud Computing based on the Hadoop Platform Harshita Pandey 1 UG, Department of Information Technology RKGITW, Ghaziabad ABSTRACT In the recent years,cloud computing has come forth as the new IT paradigm.

More information

Resource Scalability for Efficient Parallel Processing in Cloud

Resource Scalability for Efficient Parallel Processing in Cloud Resource Scalability for Efficient Parallel Processing in Cloud ABSTRACT Govinda.K #1, Abirami.M #2, Divya Mercy Silva.J #3 #1 SCSE, VIT University #2 SITE, VIT University #3 SITE, VIT University In the

More information

ESS event: Big Data in Official Statistics. Antonino Virgillito, Istat

ESS event: Big Data in Official Statistics. Antonino Virgillito, Istat ESS event: Big Data in Official Statistics Antonino Virgillito, Istat v erbi v is 1 About me Head of Unit Web and BI Technologies, IT Directorate of Istat Project manager and technical coordinator of Web

More information

Contents. 1. Introduction

Contents. 1. Introduction Summary Cloud computing has become one of the key words in the IT industry. The cloud represents the internet or an infrastructure for the communication between all components, providing and receiving

More information

MapReduce and Hadoop Distributed File System V I J A Y R A O

MapReduce and Hadoop Distributed File System V I J A Y R A O MapReduce and Hadoop Distributed File System 1 V I J A Y R A O The Context: Big-data Man on the moon with 32KB (1969); my laptop had 2GB RAM (2009) Google collects 270PB data in a month (2007), 20000PB

More information

Accelerating and Simplifying Apache

Accelerating and Simplifying Apache Accelerating and Simplifying Apache Hadoop with Panasas ActiveStor White paper NOvember 2012 1.888.PANASAS www.panasas.com Executive Overview The technology requirements for big data vary significantly

More information

Role of Cloud Computing in Big Data Analytics Using MapReduce Component of Hadoop

Role of Cloud Computing in Big Data Analytics Using MapReduce Component of Hadoop Role of Cloud Computing in Big Data Analytics Using MapReduce Component of Hadoop Kanchan A. Khedikar Department of Computer Science & Engineering Walchand Institute of Technoloy, Solapur, Maharashtra,

More information

Cyber Forensic for Hadoop based Cloud System

Cyber Forensic for Hadoop based Cloud System Cyber Forensic for Hadoop based Cloud System ChaeHo Cho 1, SungHo Chin 2 and * Kwang Sik Chung 3 1 Korea National Open University graduate school Dept. of Computer Science 2 LG Electronics CTO Division

More information

White Paper. Big Data and Hadoop. Abhishek S, Java COE. Cloud Computing Mobile DW-BI-Analytics Microsoft Oracle ERP Java SAP ERP

White Paper. Big Data and Hadoop. Abhishek S, Java COE. Cloud Computing Mobile DW-BI-Analytics Microsoft Oracle ERP Java SAP ERP White Paper Big Data and Hadoop Abhishek S, Java COE www.marlabs.com Cloud Computing Mobile DW-BI-Analytics Microsoft Oracle ERP Java SAP ERP Table of contents Abstract.. 1 Introduction. 2 What is Big

More information

Open Cloud System. (Integration of Eucalyptus, Hadoop and AppScale into deployment of University Private Cloud)

Open Cloud System. (Integration of Eucalyptus, Hadoop and AppScale into deployment of University Private Cloud) Open Cloud System (Integration of Eucalyptus, Hadoop and into deployment of University Private Cloud) Thinn Thu Naing University of Computer Studies, Yangon 25 th October 2011 Open Cloud System University

More information

Sriram Krishnan, Ph.D. sriram@sdsc.edu

Sriram Krishnan, Ph.D. sriram@sdsc.edu Sriram Krishnan, Ph.D. sriram@sdsc.edu (Re-)Introduction to cloud computing Introduction to the MapReduce and Hadoop Distributed File System Programming model Examples of MapReduce Where/how to run MapReduce

More information

From Grid Computing to Cloud Computing & Security Issues in Cloud Computing

From Grid Computing to Cloud Computing & Security Issues in Cloud Computing From Grid Computing to Cloud Computing & Security Issues in Cloud Computing Rajendra Kumar Dwivedi Department of CSE, M.M.M. Engineering College, Gorakhpur (UP), India 273010 rajendra_bhilai@yahoo.com

More information

DATA SECURITY MODEL FOR CLOUD COMPUTING

DATA SECURITY MODEL FOR CLOUD COMPUTING DATA SECURITY MODEL FOR CLOUD COMPUTING POOJA DHAWAN Assistant Professor, Deptt of Computer Application and Science Hindu Girls College, Jagadhri 135 001 poojadhawan786@gmail.com ABSTRACT Cloud Computing

More information

Corso di Reti di Calcolatori L-A. Cloud Computing

Corso di Reti di Calcolatori L-A. Cloud Computing Università degli Studi di Bologna Facoltà di Ingegneria Corso di Reti di Calcolatori L-A Cloud Computing Antonio Corradi Luca Foschini Some Clouds 1 What is Cloud computing? The architecture and terminology

More information

INTRODUCTION & CONCEPTS. Definition of Cloud Computing Service Models Deployment Models... 23

INTRODUCTION & CONCEPTS. Definition of Cloud Computing Service Models Deployment Models... 23 Contents I INTRODUCTION & CONCEPTS 17 1 Introduction to Cloud Computing 19 11 Introduction 111 Definition of Cloud Computing 20 12 Characteristics of Cloud Computing 20 13 Cloud Models 22 131 132 Service

More information

Research Article Hadoop-Based Distributed Sensor Node Management System

Research Article Hadoop-Based Distributed Sensor Node Management System Distributed Networks, Article ID 61868, 7 pages http://dx.doi.org/1.1155/214/61868 Research Article Hadoop-Based Distributed Node Management System In-Yong Jung, Ki-Hyun Kim, Byong-John Han, and Chang-Sung

More information

Distributed Framework for Data Mining As a Service on Private Cloud

Distributed Framework for Data Mining As a Service on Private Cloud RESEARCH ARTICLE OPEN ACCESS Distributed Framework for Data Mining As a Service on Private Cloud Shraddha Masih *, Sanjay Tanwani** *Research Scholar & Associate Professor, School of Computer Science &

More information

Understanding Microsoft Storage Spaces

Understanding Microsoft Storage Spaces S T O R A G E Understanding Microsoft Storage Spaces A critical look at its key features and value proposition for storage administrators A Microsoft s Storage Spaces solution offers storage administrators

More information

2) Xen Hypervisor 3) UEC

2) Xen Hypervisor 3) UEC 5. Implementation Implementation of the trust model requires first preparing a test bed. It is a cloud computing environment that is required as the first step towards the implementation. Various tools

More information

University of Messina, Italy

University of Messina, Italy University of Messina, Italy IEEE MoCS 2011 Kerkyra - Greece June 28, 2011 Dr. Massimo Villari mvillari@unime.it Cross Cloud Federation Federated Cloud Scenario Cloud Middleware Model: the Stack The CLEVER

More information

Analytics in the Cloud. Peter Sirota, GM Elastic MapReduce

Analytics in the Cloud. Peter Sirota, GM Elastic MapReduce Analytics in the Cloud Peter Sirota, GM Elastic MapReduce Data-Driven Decision Making Data is the new raw material for any business on par with capital, people, and labor. What is Big Data? Terabytes of

More information

Investigating the Pricing Impact on the QoS of a Server Farm Deployed on the Cloud

Investigating the Pricing Impact on the QoS of a Server Farm Deployed on the Cloud Investigating the Pricing Impact on the QoS of a Server Farm Deployed on the Cloud A.M.D Aljohani 1,2, D.R.W Holton 2 and Irfan Awan 2 1 Tabuk university, Saudi Arabia 2 university of Bradford, England

More information

A programming model in Cloud: MapReduce

A programming model in Cloud: MapReduce A programming model in Cloud: MapReduce Programming model and implementation developed by Google for processing large data sets Users specify a map function to generate a set of intermediate key/value

More information

How to Do/Evaluate Cloud Computing Research. Young Choon Lee

How to Do/Evaluate Cloud Computing Research. Young Choon Lee How to Do/Evaluate Cloud Computing Research Young Choon Lee Cloud Computing Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing

More information

Network-Aware Scheduling of MapReduce Framework on Distributed Clusters over High Speed Networks

Network-Aware Scheduling of MapReduce Framework on Distributed Clusters over High Speed Networks Network-Aware Scheduling of MapReduce Framework on Distributed Clusters over High Speed Networks Praveenkumar Kondikoppa, Chui-Hui Chiu, Cheng Cui, Lin Xue and Seung-Jong Park Department of Computer Science,

More information

Hadoop Parallel Data Processing

Hadoop Parallel Data Processing MapReduce and Implementation Hadoop Parallel Data Processing Kai Shen A programming interface (two stage Map and Reduce) and system support such that: the interface is easy to program, and suitable for

More information

Lambda Architecture for Batch and Real- Time Processing on AWS with Spark Streaming and Spark SQL. May 2015

Lambda Architecture for Batch and Real- Time Processing on AWS with Spark Streaming and Spark SQL. May 2015 Lambda Architecture for Batch and Real- Time Processing on AWS with Spark Streaming and Spark SQL May 2015 2015, Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document

More information

Hadoop Distributed File System Propagation Adapter for Nimbus

Hadoop Distributed File System Propagation Adapter for Nimbus University of Victoria Faculty of Engineering Coop Workterm Report Hadoop Distributed File System Propagation Adapter for Nimbus Department of Physics University of Victoria Victoria, BC Matthew Vliet

More information

Integrating Big Data into the Computing Curricula

Integrating Big Data into the Computing Curricula Integrating Big Data into the Computing Curricula Yasin Silva, Suzanne Dietrich, Jason Reed, Lisa Tsosie Arizona State University http://www.public.asu.edu/~ynsilva/ibigdata/ 1 Overview Motivation Big

More information

BIG DATA USING HADOOP

BIG DATA USING HADOOP + Breakaway Session By Johnson Iyilade, Ph.D. University of Saskatchewan, Canada 23-July, 2015 BIG DATA USING HADOOP + Outline n Framing the Problem Hadoop Solves n Meet Hadoop n Storage with HDFS n Data

More information

Scalable Services for Digital Preservation

Scalable Services for Digital Preservation Scalable Services for Digital Preservation A Perspective on Cloud Computing Rainer Schmidt, Christian Sadilek, and Ross King Digital Preservation (DP) Providing long-term access to growing collections

More information

Log Mining Based on Hadoop s Map and Reduce Technique

Log Mining Based on Hadoop s Map and Reduce Technique Log Mining Based on Hadoop s Map and Reduce Technique ABSTRACT: Anuja Pandit Department of Computer Science, anujapandit25@gmail.com Amruta Deshpande Department of Computer Science, amrutadeshpande1991@gmail.com

More information

Data Semantics Aware Cloud for High Performance Analytics

Data Semantics Aware Cloud for High Performance Analytics Data Semantics Aware Cloud for High Performance Analytics Microsoft Future Cloud Workshop 2011 June 2nd 2011, Prof. Jun Wang, Computer Architecture and Storage System Laboratory (CASS) Acknowledgement

More information

Big Data. White Paper. Big Data Executive Overview WP-BD-10312014-01. Jafar Shunnar & Dan Raver. Page 1 Last Updated 11-10-2014

Big Data. White Paper. Big Data Executive Overview WP-BD-10312014-01. Jafar Shunnar & Dan Raver. Page 1 Last Updated 11-10-2014 White Paper Big Data Executive Overview WP-BD-10312014-01 By Jafar Shunnar & Dan Raver Page 1 Last Updated 11-10-2014 Table of Contents Section 01 Big Data Facts Page 3-4 Section 02 What is Big Data? Page

More information

Open Access Research of Massive Spatiotemporal Data Mining Technology Based on Cloud Computing

Open Access Research of Massive Spatiotemporal Data Mining Technology Based on Cloud Computing Send Orders for Reprints to reprints@benthamscience.ae 2244 The Open Automation and Control Systems Journal, 2015, 7, 2244-2252 Open Access Research of Massive Spatiotemporal Data Mining Technology Based

More information

IMCM: A Flexible Fine-Grained Adaptive Framework for Parallel Mobile Hybrid Cloud Applications

IMCM: A Flexible Fine-Grained Adaptive Framework for Parallel Mobile Hybrid Cloud Applications Open System Laboratory of University of Illinois at Urbana Champaign presents: Outline: IMCM: A Flexible Fine-Grained Adaptive Framework for Parallel Mobile Hybrid Cloud Applications A Fine-Grained Adaptive

More information

A Service for Data-Intensive Computations on Virtual Clusters

A Service for Data-Intensive Computations on Virtual Clusters A Service for Data-Intensive Computations on Virtual Clusters Executing Preservation Strategies at Scale Rainer Schmidt, Christian Sadilek, and Ross King rainer.schmidt@arcs.ac.at Planets Project Permanent

More information

A REVIEW PAPER ON THE HADOOP DISTRIBUTED FILE SYSTEM

A REVIEW PAPER ON THE HADOOP DISTRIBUTED FILE SYSTEM A REVIEW PAPER ON THE HADOOP DISTRIBUTED FILE SYSTEM Sneha D.Borkar 1, Prof.Chaitali S.Surtakar 2 Student of B.E., Information Technology, J.D.I.E.T, sborkar95@gmail.com Assistant Professor, Information

More information

Grid Computing Vs. Cloud Computing

Grid Computing Vs. Cloud Computing International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 6 (2013), pp. 577-582 International Research Publications House http://www. irphouse.com /ijict.htm Grid

More information

Introduction to HDFS. Prasanth Kothuri, CERN

Introduction to HDFS. Prasanth Kothuri, CERN Prasanth Kothuri, CERN 2 What s HDFS HDFS is a distributed file system that is fault tolerant, scalable and extremely easy to expand. HDFS is the primary distributed storage for Hadoop applications. Hadoop

More information

Associate Professor, Department of CSE, Shri Vishnu Engineering College for Women, Andhra Pradesh, India 2

Associate Professor, Department of CSE, Shri Vishnu Engineering College for Women, Andhra Pradesh, India 2 Volume 6, Issue 3, March 2016 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Special Issue

More information

Viswanath Nandigam Sriram Krishnan Chaitan Baru

Viswanath Nandigam Sriram Krishnan Chaitan Baru Viswanath Nandigam Sriram Krishnan Chaitan Baru Traditional Database Implementations for large-scale spatial data Data Partitioning Spatial Extensions Pros and Cons Cloud Computing Introduction Relevance

More information

Weekly Report. Hadoop Introduction. submitted By Anurag Sharma. Department of Computer Science and Engineering. Indian Institute of Technology Bombay

Weekly Report. Hadoop Introduction. submitted By Anurag Sharma. Department of Computer Science and Engineering. Indian Institute of Technology Bombay Weekly Report Hadoop Introduction submitted By Anurag Sharma Department of Computer Science and Engineering Indian Institute of Technology Bombay Chapter 1 What is Hadoop? Apache Hadoop (High-availability

More information

PERFORMANCE ANALYSIS OF PaaS CLOUD COMPUTING SYSTEM

PERFORMANCE ANALYSIS OF PaaS CLOUD COMPUTING SYSTEM PERFORMANCE ANALYSIS OF PaaS CLOUD COMPUTING SYSTEM Akmal Basha 1 Krishna Sagar 2 1 PG Student,Department of Computer Science and Engineering, Madanapalle Institute of Technology & Science, India. 2 Associate

More information

Reduction of Data at Namenode in HDFS using harballing Technique

Reduction of Data at Namenode in HDFS using harballing Technique Reduction of Data at Namenode in HDFS using harballing Technique Vaibhav Gopal Korat, Kumar Swamy Pamu vgkorat@gmail.com swamy.uncis@gmail.com Abstract HDFS stands for the Hadoop Distributed File System.

More information

Elastic Management of Cluster based Services in the Cloud

Elastic Management of Cluster based Services in the Cloud First Workshop on Automated Control for Datacenters and Clouds (ACDC09) June 19th, Barcelona, Spain Elastic Management of Cluster based Services in the Cloud Rafael Moreno Vozmediano, Ruben S. Montero,

More information

Leveraging BlobSeer to boost up the deployment and execution of Hadoop applications in Nimbus cloud environments on Grid 5000

Leveraging BlobSeer to boost up the deployment and execution of Hadoop applications in Nimbus cloud environments on Grid 5000 Leveraging BlobSeer to boost up the deployment and execution of Hadoop applications in Nimbus cloud environments on Grid 5000 Alexandra Carpen-Amarie Diana Moise Bogdan Nicolae KerData Team, INRIA Outline

More information

GraySort and MinuteSort at Yahoo on Hadoop 0.23

GraySort and MinuteSort at Yahoo on Hadoop 0.23 GraySort and at Yahoo on Hadoop.23 Thomas Graves Yahoo! May, 213 The Apache Hadoop[1] software library is an open source framework that allows for the distributed processing of large data sets across clusters

More information

Apache Hama Design Document v0.6

Apache Hama Design Document v0.6 Apache Hama Design Document v0.6 Introduction Hama Architecture BSPMaster GroomServer Zookeeper BSP Task Execution Job Submission Job and Task Scheduling Task Execution Lifecycle Synchronization Fault

More information

IMPLEMENTING PREDICTIVE ANALYTICS USING HADOOP FOR DOCUMENT CLASSIFICATION ON CRM SYSTEM

IMPLEMENTING PREDICTIVE ANALYTICS USING HADOOP FOR DOCUMENT CLASSIFICATION ON CRM SYSTEM IMPLEMENTING PREDICTIVE ANALYTICS USING HADOOP FOR DOCUMENT CLASSIFICATION ON CRM SYSTEM Sugandha Agarwal 1, Pragya Jain 2 1,2 Department of Computer Science & Engineering ASET, Amity University, Noida,

More information

Tutorial: Big Data Algorithms and Applications Under Hadoop KUNPENG ZHANG SIDDHARTHA BHATTACHARYYA

Tutorial: Big Data Algorithms and Applications Under Hadoop KUNPENG ZHANG SIDDHARTHA BHATTACHARYYA Tutorial: Big Data Algorithms and Applications Under Hadoop KUNPENG ZHANG SIDDHARTHA BHATTACHARYYA http://kzhang6.people.uic.edu/tutorial/amcis2014.html August 7, 2014 Schedule I. Introduction to big data

More information

Introduction to Cloud Computing

Introduction to Cloud Computing Discovery 2015: Cloud Computing Workshop June 20-24, 2011 Berkeley, CA Introduction to Cloud Computing Keith R. Jackson Lawrence Berkeley National Lab What is it? NIST Definition Cloud computing is a model

More information

WORKFLOW ENGINE FOR CLOUDS

WORKFLOW ENGINE FOR CLOUDS WORKFLOW ENGINE FOR CLOUDS By SURAJ PANDEY, DILEBAN KARUNAMOORTHY, and RAJKUMAR BUYYA Prepared by: Dr. Faramarz Safi Islamic Azad University, Najafabad Branch, Esfahan, Iran. Workflow Engine for clouds

More information

Maximizing Hadoop Performance and Storage Capacity with AltraHD TM

Maximizing Hadoop Performance and Storage Capacity with AltraHD TM Maximizing Hadoop Performance and Storage Capacity with AltraHD TM Executive Summary The explosion of internet data, driven in large part by the growth of more and more powerful mobile devices, has created

More information

Sistemi Operativi e Reti. Cloud Computing

Sistemi Operativi e Reti. Cloud Computing 1 Sistemi Operativi e Reti Cloud Computing Facoltà di Scienze Matematiche Fisiche e Naturali Corso di Laurea Magistrale in Informatica Osvaldo Gervasi ogervasi@computer.org 2 Introduction Technologies

More information

Hadoop Data Warehouse Manual

Hadoop Data Warehouse Manual Ruben Vervaeke & Jonas Lesy 1 Hadoop Data Warehouse Manual To start off, we d like to advise you to read the thesis written about this project before applying any changes to the setup! The thesis can be

More information

PaRFR : Parallel Random Forest Regression on Hadoop for Multivariate Quantitative Trait Loci Mapping. Version 1.0, Oct 2012

PaRFR : Parallel Random Forest Regression on Hadoop for Multivariate Quantitative Trait Loci Mapping. Version 1.0, Oct 2012 PaRFR : Parallel Random Forest Regression on Hadoop for Multivariate Quantitative Trait Loci Mapping Version 1.0, Oct 2012 This document describes PaRFR, a Java package that implements a parallel random

More information

Index Terms : Load rebalance, distributed file systems, clouds, movement cost, load imbalance, chunk.

Index Terms : Load rebalance, distributed file systems, clouds, movement cost, load imbalance, chunk. Load Rebalancing for Distributed File Systems in Clouds. Smita Salunkhe, S. S. Sannakki Department of Computer Science and Engineering KLS Gogte Institute of Technology, Belgaum, Karnataka, India Affiliated

More information

INTRODUCTION TO CLOUD COMPUTING

INTRODUCTION TO CLOUD COMPUTING INTRODUCTION TO CLOUD COMPUTING EXISTING PROBLEMS Application Platform Hardware CONTENTS What is cloud computing Key technologies enabling cloud computing Hardware Internet technologies Distributed computing

More information

Big Data - Infrastructure Considerations

Big Data - Infrastructure Considerations April 2014, HAPPIEST MINDS TECHNOLOGIES Big Data - Infrastructure Considerations Author Anand Veeramani / Deepak Shivamurthy SHARING. MINDFUL. INTEGRITY. LEARNING. EXCELLENCE. SOCIAL RESPONSIBILITY. Copyright

More information

Cloud Storage Solution for WSN in Internet Innovation Union

Cloud Storage Solution for WSN in Internet Innovation Union Cloud Storage Solution for WSN in Internet Innovation Union Tongrang Fan, Xuan Zhang and Feng Gao School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

More information

Big Data Storage Architecture Design in Cloud Computing

Big Data Storage Architecture Design in Cloud Computing Big Data Storage Architecture Design in Cloud Computing Xuebin Chen 1, Shi Wang 1( ), Yanyan Dong 1, and Xu Wang 2 1 College of Science, North China University of Science and Technology, Tangshan, Hebei,

More information

Infomatics. Big-Data and Hadoop Developer Training with Oracle WDP

Infomatics. Big-Data and Hadoop Developer Training with Oracle WDP Big-Data and Hadoop Developer Training with Oracle WDP What is this course about? Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools

More information