In So many Companies Using Hadoop for bigdata Analysis. So the Hadoop has some drawbacks. To Overcome the Drawbacks of Hadoop Introducing Mobile Agent Under JADE.

1. Improving Big
Data Analysis
Using Mobile
Agent

2. Team Information
Team Name: Ubuntu
Team Leader : A.Mohammed Adam
Team Member : M.Logeshwaren
Department : B.E Cse Prefinal Year
College : Mailam Engineering College

3. Hadoop Open Source Software
- Hadoop Enables Distributed, data
intensive and parallel applications by
dividing bigdata into smaller datablocks.
- The DataBlocks are divided into smaller
partitions in parallel. By using Hadoop,
there is no limit of storing &processing
data by computational technique called
Mapreduce.
- It enables Fault tolerant by replicationg
data on three or more machines to avoid
data loss, but this method cause some

4. HADOOP ARCHITECTURE
&WORKFLOW
1) Hadoop Architecture
2) HDFS(Hadoop Distributed File
System)
3) NameNode - Manage the MetaData
4) DataNode - stores data blocks on behalf
of local or remote clients
5) Job Tracker - talks to the NameNode to
determine thelocation of the data
6) Trace Tracker - manage the
execution of individual tasks on each slave

5. Hadoop Workflow

6. 6
Hadoop Works On Client/Server

7. Hadoop Drawbacks
1) Hadoop needs high memory and big
storage to apply replication technique.
2) Hadoop supports allocation of tasks only
and do not have strategy to support
scheduling of tasks.
3) Still single master (NameNode) which
requires care
4) Load time is long.

8. New Framework For Improving
Big Data Analysis Using Mobile
Agent
MapReduce Agent Mobility
(MRAM) to improve big data analysis and
overcome the drawbacks of Hadoop. The
proposed framework
is developed by using mobile agent and
MapReduce paradigm
under Java Agent Development Framework
(JADE).

9. Seven Reasons for using
mobileagents
1)Reduce the network load,
2)Overcome network latency,
3)Encapsulate protocols,
4)Execute asynchronously and
autonomously,
5)Adapt dynamically,
6)Naturally heterogeneous and robust, and
7)Fault-tolerant

10. BASIC CONCEPTS OF JADE AND
MOBILE AGENT
1)JADE (Called as Container)JADE
contains both the libraries required to
develop application agents and the run-
time environment that provides the basic
services.
2)Mobile Agent - A mobile agent (MA) is a
software abstraction that can migrate
during execution across a heterogeneous
or homogeneous network.

11. OUR PROPOSED FRAMEWORK

12. 3.
Executecode
5.
Executecode
Result
Code
Code
12
Mobile Agents

13. Third Step

14. Mobile Agent
The mobile agent is a Linux-based
appliance that lets you secure the type of
email content that is synchronized to
users' mobile devices when they
connect to the network. This includes
content in email messages, calendar
events, and tasks.

15. Analyzes
The mobile agent analyzes content when
users synchronize their mobile devices
to your organization's Exchange server.
If content or data being pushed to their
device breaches the organization's mobile
DLP policy, it is quarantined or permitted
accordingly.

16. How to work on Mobile Agent?
1. Installing the mobile agent software
2. Configuring the mobile agent
3. Configuring a mobile DLP policy

17. Real time Example using Mobile
Agent

18. Mobile Agent Requirements
Using OperatingSystem: GNU/Linux
Devices Used: 3G and wireless networks,
such as i-pads, Android mobile phones,
and i-phones.
Using Servers: Microsoft Exchange agent,
Data Security Management Server

19. Cost values for HadoopF, HadoopC and
MRAM when twomachines are failed

20. Advantages of MRAM
1) Support allocation and scheduling tasks.
2) Provides fault tolerance and don't need
high memory
or big disk to support it.
3) Load time for MRAM is less than that of
Hadoop.
4) Solve single master (centralized node)
problem by
using features of mobile agent.
5) Improve execution time because of no

21. What is Big Data ?
"Big data is a collection of data sets so large
and complex that it becomes difficult to
process using on-hand database
management tools or traditional data
processing applications. The challenges
include capture, curation, storage, search,
sharing, transfer, analysis, and
visualization."

22. Why BigData?

23. Big Data Characteristics
Big Data Vectors (3Vs)
- high-volume
amount of data
- high-velocity
Speed rate in collecting or acquiring or generating or processing of data
- high-variety
different data type such as audio, video, image data (mostly unstructured
data)

24. Cost Problem (example)
Cost of processing 1 Petabyte of
data with 1000 node ?
1 PB = 1015
B = 1 million gigabytes = 1 thousand terabytes
- 9 hours for each node to process 500GB at rate of 15MB/S
- 15*60*60*9 = 486000MB ~ 500 GB
- 1000 * 9 * 0.34$ = 3060$ for single run
- 1 PB = 1000000 / 500 = 2000 * 9 =
18000 h /24 = 750 Day
- The cost for 1000 cloud node each
processing 1PB
2000 * 3060$ = 6,120,000$

25. Zeta-Byte Horizon
 the total amount of global data is expected to grow to 2.7 zettabytes
during 2012. This is 48% up from 2011
Wrap Up
2012 2020
x50
 As of 2009, the entire World Wide Web was estimated to
contain close to 500 exabytes. This is a half zettabyte

26. Conclusion

27. References
[1]Hadoop web site, http://hadoop.apache.org/, Jan. 2014.
[2]Kala Karun. A, Chitharanjan. K, “A Review on Hadoop–HDFS
Infrastructure Extensions”, In Proceedings of IEEE Conference on Information
&Communication Technologies (ICT2013), pp.132-137,11-12 April, 2013, doi:
10.1109/CICT.2013.6558077.
[3] Jian Tan, Xiaoqiao Meng, Li Zhang, “Coupling Task Progress for
MapReduce Resource-Aware Scheduling”, In Proceedings of IEEE INFOCOM,
pp.1618-1626, 14-19 April,2013,doi:10.1109/INFCOM.2013.6566958
[4] Zhu, Nan; Liu, Xue; Liu, Jie; Hua, Yu, "Towards a cost-efficient MapReduce: Mitigating
power peaks for Hadoop clusters," Tsinghua Science and Technology, vol.19, no.1,
pp.24,32, Feb. 2014 doi: 10.1109/TST.2014.6733205.
[5] Anchalia, P.P.; Koundinya, A.K.; Srinath, N.K., "MapReduce Design of K-Means
Clustering Algorithm," International Conference onInformation Science and Applications
(ICISA), pp.1,5, 24-26 June 2013, doi:10.1109/ICISA.2013.6579448.
[6] S. Ghemawat, H. Gobioff, and S. Leung. “The google file system”, In Proceedings of the
nineteenth ACM symposium on Operating systems principles”, SOSP ’03, pp. 29–43, New
York, NY, USA, 2003.
[7] JADE web site, http://JADE.tilab.com, Jan. 2014.

28. Any Queries??????