Arun Kumar.M

1vi11is005

ISE, VIT

Agenda

1. INTRODUCTION

2. MOTIVATION AND RELATED WORK

3. ISSUES AND CHALLENGES

4. THE PROPOSED APPROACHES

5. CONCLUSION

AGENDA

• INTRODUCTION

• CLOUD COMPUTING

• BIG DATA

• HADOOP,HDFS AND MAP REDUCE

• BIG DATA APPLICATIONS AND ADVANTAGES

• ISSUES AND CHALLENGES

• THE PROPOSED APPROACHES

• CONCLUSION

• REFERENCES

AGENDA

INTRODUCTION

• The main focus is on security issues in

cloud computing that are associated

with big data.

• In order to analyze complex data it is

very important to securely store,

manage and share large amounts of

complex data.

Cloud Computing

• The goal of Cloud Computing is to make use of

increasing computing power to execute millions of

instructions per second.

• Cloud Computing consists of a front end and back

end.

• Applications which use Cloud Computing are Gmail, Google Calendar, Google Docs and Dropbox etc.

CLOUD COMPUTING

BIG DATA

• Big Data is the word used

to describe massive

volumes of structured

and unstructured data.

• Examples of Big Data are

Credit card transactions

with respect to a Bank,

and Facebook.

THREE CHARACTERISTICS OF BIG DATA

THE OTHER TWO DIMENSIONS WITH RESPECT TO BIG DATA

Variability Complexity

HADOOP

• Hadoop, which is Java-based

programming framework supports the

processing of large sets of data in a

distributed computing environment.

• Hadoop Framework is used by popular

companies like Google, Yahoo, Amazon

and IBM etc.

HADOOP DISTRIBUTED FILE SYSTEM (HDFS)

• It links together file systems on local nodes to

make it into one large file system.

• HDFS is a file system written in Java for the

Hadoop framework.

MAP REDUCE

• A MapReduce program is

composed of a Map()

procedure and a Reduce()

procedure

• The MapReduce

framework consists of a

single master JobTracker

and one slave TaskTracker

per cluster-node.

BIG DATA APPLICATIONS

Manufacturing and Bioinformatics are the two

major areas of big data applications.

BIG DATA ADVANTAGES

• Data analytics

• The software packages provide a rich

set of tools and options to analyze the

threats

• Errors within the organization are known

instantly.

NEED OF SECURITY IN BIG DATA

• Many companies are

using the technology to

store data about their

company.

• For making big data

secure, techniques such

as encryption, logging,

honeypot detection must

be necessary.

ISSUES AND CHALLENGES

• Data Protection

• Internode Communication

• Administrative Rights for Nodes

• Authentication of Applications and Nodes

• Logging

• Traditional Security Tools

THE PROPOSED APPROACHES

• File Encryption

• Network Encryption

• Logging

• Software Format and Node Maintenance

• Nodes Authentication

• Rigorous System Testing of Map Reduce Jobs

• Honeypot Nodes

• Access Control

CONCLUSION

The security is an important aspect for organizations

running on these cloud environments.

Using proposed approaches, cloud environments

can be secured for complex business operations.

• Venkata Narasimha Inukollu, Sailaja Arsi andSrinivasa Rao Ravuri Security issues with big datain cloud computing (IJNSA), Vol.6, No.3, May2014.

• N, Gonzalez, Miers C, Redigolo F, Carvalho T,Simplicio M, de Sousa G.T, and Pourzandi M. "AQuantitative Analysis of Current SecurityConcerns and Solutions for Cloud Computing.".Athens:2011., pp 231 – 238, Nov. 29 2011- Dec. 12011.

• Zhao, Yaxiong , and Jie Wu. "Dache: A data awarecaching for big-data applications using theMapReduce framework." INFOCOM, 2013Proceedings IEEE, Turin, Apr 14-19, 2013.

• Changqing Ji, Yu Li, Wenming Qiu, UchechukwuAwada, Keqiu Li: Big Data Processing in CloudComputing Environments, 2012 InternationalSymposium on Pervasive Systems.

• www.google.com

REFERENCES

Presented by Arun Kumar.M