Modeling and Analysis of e-Learning

1. Modeling and Analysis of e-Learning Advisor: Dr. Nandana Rajatheva Surya Bahadur Kathayat

2. 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)

3. GROUPING OF LEARNERS E-LEARNING CONTENTS & SERVICES MANAGEMENTMECHANISMS TECHNOLOGIES E-Learning

4. Client-Server based e-Learning model Peer-to-Peer based e-Learning model E-Learning - technologies

5. 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)

6. 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

7. E-Learning –Abstract Model LEARNERS E-LEARNING CONTENTS & SERVICES MANAGEMENT TECHNOLOGIES

8. Network storage File Sharing * P2P application layer Structured P2P Protocol (overlay network) Pastry Internet/Network Layer TCP/IP Technological Infrastructure • No need to change any infrastructure, just implement on the top of the application layer

9. 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

10. 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

11. Quantitative analysis • 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)

12. Quantitative analysis • 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

13. Performance Evaluation • 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

14. 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 Results – Latency, group size 15

15. 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 Results – Latency, group size 15

16. Average group multicast latency in a MVRing and Scribe based group of size n=10 Average group multicast latency in a MVRing and Scribe based group of size n=15 Average group multicast latency in a MVRing and Scribe basedgroup of size n=20 Average group multicast latency in a MVRing and Scribe basedgroup of size n=25 Results – latency summary

17. Standard deviation of latency in a MVRing and Scribe basedgroup of size n=15 Standard deviation of latency in a MVRing and Scribe basedgroup of size n=10 Standard deviation of latency in a MVRing and Scribe basedgroup of size n=20 Standard deviation of latency in a MVRing and Scribe basedgroup of size n=25 Results - standard deviation of latency

18. Host Nodes T-S Internet Model Router Nodes • 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 Stub Domain Transit Domain

19. MVRing and Optimal ring latency comparison Results – Latency, using NS-2 • Group size 500, 150,50 (Appendix G) • Source node 240th • number of packets sent 10

20. Latency comparison for ring, MVRing and MST (minimum spanning tree) for groups size of 50, 150 and 500 nodes Results - Latency, using NS-2

21. Results – Fault tolerance • Node 1 is the source node and node 21 leave the group unexpectedly in a group of size 25 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

22. Results – Fault tolerance • 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

23. Figure: Node degree profile in MVRing and Scribe based 15-member group for same group Ids (“mytopic”). Results – Node degree • Interest based Group having size 15 is created and node degree is noted down in MVRing and Scribe schemes

24. Figure: Node degree profile in MVRing and Scribe based group for group size 30. Results – Node degree • Interest of the Group (i.e. groupId) is varied keeping the group size identical (i.e. 30)

25. Results – Joining traffic profile • Two scenarios • 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 • 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

26. Per node joining traffic profile in MVRing and Scribe based group of different sizes. Results – Joining traffic profile

27. Results – Joining traffic profile Avg. packets/sec 1.66 Avg. packet size 751 bytes Packets received 605 Avg. packets/sec 4.06 Avg. packet size 672 bytes Packets received 1474 Packets Packets 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

28. Results – Multicast traffic profile • Two scenarios for groups of size 5, 10, 15 and 20 • Firstly, multicast traffic on a node is measured that sends the data to the multicast group • 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

29. Per node multicast data traffic profile in MVRing and Scribe based group of different sizes. Results – Multicast traffic profile • Multicast traffic on a source node is measured that sends 10 packets of data to a multicast group

30. Scribe data traffic in a node when a node multicasts 10 packets to a group of size 20 MVRing data traffic in a node when a node multicasts 10 packets to a group of size 20 Results – Multicast traffic profile Avg. packets/sec 7.73 Avg. packet size 648 bytes Packets received 853 Avg. packets/sec 7.64 Avg. packet size 624 bytes Packets received 844 Packets Packets

31. Multicast data traffic profile received in MVRing and Scribe based groups of different sizes. Results – Multicast traffic profile • Multicast traffic on a receiver node is measured when a any other source node sends 10 packets of data to a multicast group

32. Results – Multicast traffic profile Avg. packets/sec 8.69 Avg. packet size 604 bytes Packets 942 Avg. packets/sec 7.76 Avg. packet size 645 bytes Packets 844 Packets Packets Scribe data traffic in a node when a node received 10 packets multicasted by any other member in a group of size 20 MVRing data traffic in a node when a node received 10 packets multicasted by any other member in a group of size 20

33. 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

34. 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 Results - Node heterogeneity • Multicast traffic on a node (considering weak and powerful) with different sizes of MVRing and Scribe based groups

35. Results - Node heterogeneity Avg. packets/sec 1.97 Avg. packet size 704 bytes Packets received 326 Avg. packets/sec 3.127 Avg. packet size 681 bytes Packets received 518 Packets Packets 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 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

36. 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

37. 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.

38. 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

39. 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)

40. Thank You