Modeling and Analysis of e-Learning

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Modeling and Analysis of e-Learning

Surya Bahadur KathayatSurya Bahadur Kathayat

Advisor: Dr. Nandana RajathevaAdvisor: Dr. Nandana Rajatheva

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E-Learning

dicole.org moodle.org OurWeb (Kurhila, 2006) EDUCO (Kurhila et al. 2003) WebCT.com APPLE (Jin et al., 2004) LL2 (Brue et al., 2005) Edutella (Nilsson et al., 2005) ALM for group communication (Scribe,

Bayeux, Brog)

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E-Learning

GROUPING OF LEARNERS

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E-LEARNING CONTENTS &

SERVICES

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E-Learning - technologies

Client-Server based e-Learning model

Peer-to-Peer based e-Learning model

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E-Learning - technologies

• Limitations of C/S based systems: content/infrastructure based; overhead, scalability, interactivity, collaboration; resource sharing

• Lack of efficient use of P2P technologies in e-Learning, lack of consideration of the Interest of users in the e-learning environment, almost all the present day groups require apriori planning.

• Existing grouping mechanism in structured P2P are either based on tree or mesh. No existing models for group merging, group splitting. Existing mechanisms are having limited fault tolerance level. No group adaptation mechanisms for e-Learning

(ResourceNet, USA., 2005; Keegan et al., 2005; Kurhila et al., 2003, Paulsen, 2003, Fernando, 2005; Rowstronand and Druschel, 2001; Nowell et al., 2003; Clarke, 2000; Clarke, 2001. Jin et al., 2004; Brue et al., 2005; Nilsson et al., 2005)

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Objective

MVRING BASED GROUP COMMUNICATION PROTOCOL (design, implementation and evaluation) CONSISTING OF GROUP ADAPTATION ALGORITHMS (interest based grouping, number of virtual groups formation, merging/splitting of common interest groups, group maintenance etc) FOR THE E-LEARNING DESIGN USING STRUCTURED PEER-TO-PEER TECHNOLOGIES

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E-Learning – Abstract Model

LEARNERS

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E-LEARNING CONTENTS &

SERVICES

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Technological Infrastructure

TCP/IP

Pastry

Network storage

File Sharing

Internet/Network Layer

Structured P2P Protocol(overlay network)

P2P application layer*

No need to change any infrastructure, just implement on the top of the application layer

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Technological Infrastructure

Structured P2P platform - Pastry Each peer (on Internet or Application

identified by IP address+Port in local machine) will run a application software and specify its interest

Facilitates efficient routing Programming Languages used

Java – JDK 1.4.2 NS-2 for simulation considering large

number of nodes

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MVRing based application layer multicasting protocol ALM protocol with group adaptation

algorithms Ring formation mechanism MVRing formation mechanism Data delivery mechanism with node

heterogeneity Merge/Split mechanism Group maintenance mechanisms Duplicate data detection mechanism

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Quantitative analysis

Definitions

Propositions

Theorems

Summary


Definitions Tree, Ring, Chordal Ring, MVRing, Fault

tolerance level, Hop count Propositions

Using TDP, delivery of packet from source node to destinations traveling across ‘E’ links takes ‘2E-1’ Time Frames (TFs)

Network delay bound (NDB) of a ring having N number of nodes is of the order of O(N)

Network delay bound (NDB) of a tree having N number of nodes is of the order of O(logN)

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Definitions

Propositions

Theorems

Summary

Theorems NDB of MVRing is comparable with that

of general tree (with proposed data delivery mechanism with duplicate data rejection)

Data delivery mechanism proposed MVRing is twice fault tolerant than that of general Tree

Routing delay in MVRing scheme will be improved by ‘X’ times (no of MVR neighbors) compared to original single ring provided that all single-hop path length are equal.Higher fault tolerance

level and Comparable latency

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CASE A: Internet Environment (Tested In Tc LAB) 1 to 35 Users in a group having internet connection

CASE B: Network simulator (Large Number of Nodes) - 50 Routers 50, 150, 500 nodes as hosts in groups T-S Topology for Internet Modeling (GT-ITM)

Concentrate on latency, fault tolerance, node degree, node stress/traffic Comparison of the result with the traditional group

communication models (if applicable) - Tree Based protocol in the Structured P2P Network

Performance Evaluation

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Results – Latency, group size 15

0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14

receiving node sequence

late

ncy

(m

sec)

src n2 MVRing

src n2 Scribe

src n3 MVRing

src n3 Scribe

src n4 MVRing

src n4 Scribe

src n5 MVRing

src n5 Scribe

src n6 MVRing

src n6 Scribe

src n7 MVRing

src n7 Scribe

Latency in MVRing and Scribe based 15-member multicast group with one of nodes 2 to 7 as source node at a time and other remaining 14 nodes as receiving

nodes

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Results – Latency, group size 15

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12 13 14

receiving node sequence

late

ncy (

msec)

src n8 MVRing

src n8 Scribe

src n9 MVRing

src n9 Scribe

src n10 MVRing

src n10 Scribe

src n11 MVRing

src n11 Scribe

src n12 MVRing

src n12 Scribe

src n13 MVRing

src n13 Scribe

Latency in MVRing and Scribe based 15-member multicast group with one of nodes 8 to 13 as source node at a time and other remaining 14 nodes as

receiving nodes

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Results – latency summary

0

50

100

150

200

250

1 3 5 7 9 11 13 15 17 19

nodes

aver

age

late

ncy

(m

sec)

MVRing Scribe

0

500

1000

1500

1 3 5 7 9 11 13 15 17 19 21 23 25

nodes

aver

age

late

ncy

(m

sec)

MVRing Scribe

Average group multicast latency in a MVRing and Scribe based group of size n=10




020406080

100120

1 2 3 4 5 6 7 8 9

nodes

aver

age

late

ncy

(m

sec)

MVRing Scribe

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12 13 14

nodes

aver

age

late

ncy

(m

sec)

MVRing Scribe

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Results - standard deviation of latency

Standard deviation of latency in a MVRing and Scribe based group of size n=25


0

50

100

150

200

250

300

350

1 3 5 7 9 11 13 15 17 19 21 23 25

nodes

Std

. Dev

iati

on

MVRing

Scribe

01020304050607080

1 3 5 7 9 11 13 15 17 19

nodes

std

. dev

iati

on

MVRing

Scribe



0

10

20

30

40

1 2 3 4 5 6 7 8 9

nodes

std

. de

via

tio

n

MVRing

Scribe

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8 9 10 11 12 13 14

nodes

std

.dev

iati

on

MVRing

Scribe

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T-S Internet Model

Host Nodes

Router Nodes

Transit Domain

Stub Domain

Configuration

2 Mbps Duplex Link

Random link delay up to 450 ms

Drop tail queue

no. of CBR traffic sources and sinks

Distance Vector unicast routing protocol

Kruskal Algorithm for Minimum spanning tree

Greedy Algorithm for Optimal Ring

MVRing on the top of optimal ring

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Results – Latency, using NS-2

MVRing and Optimal ring latency comparison

0

1000

2000

3000

4000

5000

6000

1 51 101 151 201 251 301 351 401 451

nodes

late

nc

y(m

se

c)

Ring MVRing

Group size 500, 150,50 (Appendix G) Source node 240th

number of packets sent 10

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Results - Latency, using NS-2

Latency comparison for ring, MVRing and MST (minimum spanning tree) for groups size of 50, 150 and 500 nodes

0

1000

2000

3000

for n=50 for n=150 for n=500

nodes

late

ncy (

msec)

Mvring Ring MST

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Results – Fault tolerance

MVRing packets received/lost due unexpected node failure in a group size of

25 nodes

Scribe packets received/lost due unexpected node failure in a group size of

25 nodes

05

1015202530

1 3 5 7 9 11 13 15 17 19 21 23 25

nodes numners

pac

ket

nu

mb

ers

packets received after tree recover

packets lost due to node failure

packets received before the effect of node failure

0

5

10

15

20

25

30

1 3 5 7 9 11 13 15 17 19 21 23 25

node numbers

pack

et n

umbe

rs

packets received in recovered MVRing



Node 1 is the source node and node 21 leave the group unexpectedly in a group of size 25

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Results – Fault tolerance

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

node numbers

pac

ket

nu

mb

ers

packet received after tree recover

packet lost due to node failure


0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

node numbers

pack

et n

umbe

rs

packet received after MVRing recover



Node 1 is the source node and node 11 leave the group unexpectedly in a group of size 20

MVRing packets received/lost due unexpected node failure in a group size of

20 nodes

Scribe packets received/lost due unexpected node failure in a group size of

20 nodes

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Results – Node degree

0

2

4

6

8

10

12

14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

nodes

no

de

de

gre

e

MVRing

Scribe

Figure: Node degree profile in MVRing and Scribe based 15-member group for same group Ids (“mytopic”).

Interest based Group having size 15 is created and node degree is noted down in MVRing and Scribe schemes

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Results – Node degree

Figure: Node degree profile in MVRing and Scribe based group for group size 30.

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0

5

10

15

20

25

30

1 6 11 16 21 26

nodes

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de

deg

ree

MVRing Scribe with topic "hihi" Scribe with topic "kk"

Scribe with topic "hi" Scribe with topic "zoo" Scribe with topic "lab"

Interest of the Group (i.e. groupId) is varied keeping the group size identical (i.e. 30)

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Results – Joining traffic profile

Two scenarios1. One node is made to join to already

existing group (of sizes 4, 9, 14 and 19) and joining traffic is measured in case of MVRing and Scribe

2. Numbers of users are made to join the group having only a group creator as existing user. Joining traffic profile is measured for different groups of sizes 5, 10, 15 and 20

More results on Appendix J

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0

20

40

60

80

100

5 10 15 20

Group size

Per

no

de

join

ing

tra

ffic

(K

Byt

es)

MVRing Scribe

Per node joining traffic profile in MVRing and Scribe based group of different sizes.


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Avg. packets/sec 4.06Avg. packet size 672 bytesPackets received 1474

MVRing joining traffic profile in a group of size 20 when 19 members join in a group created by

a creator


Scribe joining traffic profile in a group of size 20 when 19 members join in a group created

by a creator

Pac

ket

s

Pac

ket

s

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Results – Multicast traffic profile

Two scenarios for groups of size 5, 10, 15 and 20 1. Firstly, multicast traffic on a node is measured

that sends the data to the multicast group

2. Secondly, any one source node is made to multicast the data in to the group and traffic profile at non-source nodes is observed and measured

More results on Appendix K

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0

200000

400000

600000

5 10 15 20

Group size

Byt

es R

ecei

ved

MVRing Scribe

Per node multicast data traffic profile in MVRing and Scribe based group of different sizes.


Multicast traffic on a source node is measured that sends 10 packets of data to a multicast group

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MVRing data traffic in a node when a node multicasts 10 packets to a group

of size 20

Scribe data traffic in a node when a node multicasts 10 packets to a group

of size 20

Pac

kets

Pac

ket

s



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0

200000

400000

600000

5 10 15 20

Group Size

Byt

es r

ecei

ved

MVRing Scribe

Multicast data traffic profile received in MVRing and Scribe based groups of different sizes.

Multicast traffic on a receiver node is measured when a any other source node sends 10 packets of data to a multicast group

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Avg. packets/sec 7.76Avg. packet size 645 bytesPackets 844

MVRing data traffic in a node when a node received 10 packets multicasted by any other member in a group of size 20

Avg. packets/sec 8.69Avg. packet size 604 bytesPackets 942

Scribe data traffic in a node when a node received 10 packets multicasted by any

other member in a group of size 20

Pac

ket

s

Pac

ket

s

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Results - Node heterogeneity

Allowing the node to mention whether it has sufficient resources or not

Under the identical scenario (same groupId, same number of users in a group, same amount of data multicasting in a group, same source node in a group, etc), traffic overhead on a node is measured in two modes i.e. firstly node is considered to have sufficient resources and secondly node is considered as weak node and has insufficient resources.

Detail results on Appendix L

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Comparison of node traffic when it is assumed to have sufficient resources and insufficient resources; source node is multicasting 10 packets of data to a MVRing

based groups

0100000200000300000400000500000

5 15 25

Group Size

Byt

es r

ecei

ved

Powerful node Weak node

Multicast traffic on a node (considering weak and powerful) with different sizes of MVRing and Scribe based groups

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Pac

ket

s


Node traffic when it is assumed to have sufficient resources; source node is multicasting 10 packets of data to a

MVRing based group of size 5



Node traffic when it is assumed to have insufficient resources; source node is multicasting 10 packets of data to a

MVRing based group of size 5

Pac

ket

s

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Result - Implementations

Group Merging Two groups at a time

Group Splitting Any member of group can initiate to split

Group Maintenance Expected/unexpected node departure from

group RP shifting Merging/Splitting and etc

More results in Appendix M, example of group merging implementation & validation process is below.

RP, new leader, updated neighbors, number of users in a group etc are checked and verified

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Conclusion

E-Learning in P2P environment New MV Ring based Approach for ALM

More fault tolerant Better node degree distribution Comparable latency Comparable multicast traffic profile with high

joining traffic For synchronous, more interactive learning,

efficient resource utilization than traditional e-Learning

Strong Alternative to traditional class room based learning…that current C/S based e-Learning lacking to be.

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Conclusion

Limitations/Extension Consideration of Security and Privacy as major

issues Reducing the joining traffic cost

Future work : E-Learning GRID Modified MVRing based protocol to grid

environment will provide an extremely powerful infrastructure allowing users to collaborate in various learning contexts and to share learning materials, learning processes, learning systems, and experiences

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Papers/Presentations

Published/Accepted/Submitted South Asian Network Operators Group –SANOG 7

(Accepted for workshop Presentation), Mumbai, India Published: International Conference On Distance Education

– ICODE 2006 Conference, Mascot, Oman Published: Web Information Systems and Technologies –

WEBIST 2006 Conference, Setúbal, Portugal IEEE Conference on Networks (ICON -2006), Singapore

(Submitted)

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Modeling and Analysis of e-Learning

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