1. P ERFORMANCE E VALUATION OF B ASE S TATION A PPLICATION O PTIMIZER Nana Ginzbourg Instructor: Dr. Ronit Nossenson Internal Instructor: Dr. Tami Tamir

2. O VERVIEW Introduction LTE Network NS 2 Simulator Base Station Application Optimizer Implementation Model – network topology Traffic generation and cache implementation Simulation Trace analyzers Results Conclusions and Future work

3. I NTRODUCTION Cellular operator take advantage of new technologies and offer rich services to advanced handset Backhaul lines are still limited to 2 Mbps capacity Leads to backhaul bottleneck problem

4. P OSSIBLE S OLUTIONS Backhaul lines upgrade Application solution Data reduction No need in upgrade Integrated solutions Base Station Application Optimizer Analyze and optimize user data in application level Prevent unnecessary data travelling through backhaul network Less painful upgrade

5. P ROJECT G OALS Implement Base Station Application Optimizer over an LTE network in NS-2 simulator Evaluate solution performance in various simulations: Realistic mixture of applications Different cache hit rates Changing network load – number of users Different queue sizes

6. LTE N ETWORK

7. B ASE S TATION A PPLICATION O PTIMIZER

8. NS 2 S IMULATOR Open Source event driven simulator History: 1989 – REAL network simulator 1995 – VINT Project with DARPA Support a big variety of protocols and keeps growing Simulator Implementation: Backend in C++ - compiled hierarchy Frontend in Otcl – interpreter with similar hierarchy Simulation scripts -.tcl

9. NS 2 S IMULATOR

10. B ASE S TATION A PPLICATION O PTIMIZER I MPLEMENTATION Open source: http://code.google.com/p/base-station-application- optimizer/ http://code.google.com/p/base-station-application- optimizer/ Four stages: Network Topology definition Traffic generation Configurations and simulations over different parameters Trace analysis

11. N ETWORK T OPOLOGY D EFINITION Wired Nodes eNode-B Serving Gateway PDN-GW Server Wireless Nodes User equipment DropTail queuing algorithm Oversubscription factor at S1 interface is 1/3

12. T RAFFIC G ENERATION APPLICATIONS D ISTRIBUTION

13. T RAFFIC G ENERATION IN NS 2 ParametersRate (kbps) ProtocolPacket Size (bytes) Generator 64UDP200CBRVoIP ON – 200ms OFF – 2000ms Shape – 1.3 300TCP1040Pareto Web ON – 2000ms OFF – 2000ms Shape – 1.5 600TCP1300ParetoVideo ON – 2000ms OFF – 200ms Shape – 1.7 100TCP1500ParetoFile

14. T RAFFIC G ENERATION C ACHE I MPLEMENTATION

15. C ONFIGURATION AND S IMULATIONS All simulation parameters are configurable Command line Configuration.tcl Run simulations using main.tcl and override relevant parameters Number of end users Buffer size Hit rates per application Users distribution per application

16. T RACES A NALYSIS

17. T OTAL B ANDWIDTH A NALYZER Output: total number of bytes per application type over S1 interface Implementation: Combination of shell and awk scripts Summarize packets sizes over S1 interface using source and destination id Use flow id to separate results per application type and hit rate scenarios

18. B ANDWIDTH PER SECOND A NALYZER Output: Total number of bytes per second per application type over the S1 interface Implementation: Awk script Use an array[simulation_length_seconds] Summarize packet sizes according to source and destination nodes, flow id and packet size.

19. E ND -T O -E ND DELAY AND D ROPPED STATISTICS ANALYZER Output: End-to-end delay Dropped packets percentage Implementation: Array per application type Packet id indicates the index in the array Send event -> save start time Receive event -> save end time Dropped event -> update with -1

20. R ESULTS Primary Simulation 20, 50 and 100 users User Equipment links buffer size – 10 packets Cache Hit rate impact of results Standard hit rates: 20% web, 40% video and files Increase and decrease hit rate by 10% Buffer Size impact on results Increase and decrease buffer size in 25% Keep medium hit rate

21. T OTAL TRANSFERRED KB WITH 20, 50 & 100 CONCURRENT USERS

22. D ATA R EDUCTION P ER A PPLICATION T YPE

23. D ATA R EDUCTION O VER T IME

24. D ATA R EDUCTION F ACTOR Reduction Factor Traditional KB OPTIMIZED KB Application Users 1.19181265152487 Web 20 2.00803161402263Video 2.00499097249302File Sharing 1.0043190 VoIP 1.801526713847242 Total 1.26519829414002 Web 50 1.7218964561100899Video 1.671241815742733File Sharing 1.00129566129570VoIP 1.5937876672387204 Total 1.33964349727680 Web 100 1.1525056612176196Video 1.3620294261490109File Sharing 0.98294793302259VoIP 1.2357942284696243Total

25. D ROPPED S TATISTICS Optimized KB Traditional KB Application Users % Dropped# Drop% Dropped# Drops 0.00%0 0 Web 20 0.00%0 0 Video 0.00%0 0 File Sharing 0.00%00.05%19 VoIP 0.00%0 19 Total 0.07%1090.07%114 Web 50 0.02%1880.05%291 Video 0.03%1050.06%249 File Sharing 0.06%620.06%65 VoIP 0.03%4640.06%719 Total 0.05%1651.96%5954 Web 100 0.03%6031.57%12333 Video 0.05%3741.50%9528 File Sharing 0.10%2634.96%12503 VoIP 0.04%14052.04%40318 Total

26. A VERAGE D ELAY Reduction % Traditional KB OPTIMIZED KB Application Users 15.56%0.46230.3904 Web 20 75.35%0.46210.1139 Video 48.93%0.46220.2360 File Sharing 0.06%0.46120.4609 VoIP 20.45%0.46360.3688 Web 50 68.92%0.46330.1440 Video 39.39%0.46340.2809 File Sharing 0.23%0.46280.4617 VoIP 26.16%0.47130.3480 Web 100 69.26%0.47100.1448 Video 39.87%0.47100.2832 File Sharing 0.86%0.46910.4651 VoIP

27. I MPACT OF C ACHE HIT RATE ON D ATA R EDUCTION F ACTOR High Hit Rate Medium Hit Rate Low Hit Rate Application Users 1.53 1.191.21 Web 20 1.99 2.001.60 Video 2.01 2.002.01 File Sharing 1.00 VoIP 1.87 1.801.61 Total 1.27 1.261.27 Web 50 1.89 1.721.58 Video 1.89 1.671.68 File Sharing 1.00 VoIP 1.72 1.591.53 Total 1.27 1.331.19 Web 100 1.27 1.15 Video 1.52 1.361.28 File Sharing 0.98 VoIP 1.33 1.231.19 Total

28. I MPACT OF C ACHE HIT RATE ON D ROP P ACKETS P ERCENTAGE % Drop High Hit Rate % Drop Low Hit Rate %Drop Traditional Application 0.15%0.26%1.96% Web 0.05%0.12%1.57% Video 0.12%0.20%1.50% File Sharing 0.23%0.46%4.96% VoIP 0.09%0.17%2.04% Total

29. I MPACT OF C ACHE HIT RATE ON A VERAGE D ELAY % Reduction High Hit Rate % Reduction Medium Hit Rate % Reduction Low Hit Rate Application 25.24%26.16%19.89% Web 73.32%69.26%67.37% Video 46.57%39.87%36.55% File Sharing 0.98%0.86%0.40% VoIP

30. I MPACT OF BUFFER S IZE ON DATA REDUCTION FACTOR Large Buffer Size Medium Buffer Size Small Buffer Size Application Users 1.531.191.53 Web 20 1.992.001.99 Video 2.012.002.01 File Sharing 1.00 VoIP 1.871.801.87 Total 1.271.261.27 Web 50 1.901.721.91 Video 1.891.671.89 File Sharing 1.00 VoIP 1.721.591.73 Total 1.281.331.26 Web 100 1.281.151.30 Video 1.571.361.51 File Sharing 0.98 VoIP 1.341.231.33 Total

31. I MPACT OF BUFFER SIZE ON DROPPED PACKETS PERCENTAGE % Drop Optimized Large Buffer % Drop Traditional Large Buffer % Drop Optimized Small buffer %Drop Traditional Small buffer Application 0.05%0.11%0.09%2.17% Web 0.05%0.16%0.05%1.70% Video 0.09%0.24%0.12%1.75% File Sharing 0.16%0.17%0.21%4.09% VoIP 0.07%0.18%0.08%2.10% Total

32. I MPACT OF BUFFER SIZE ON A VERAGE PACKETS DELAY % Reduction Large Buffer Size % Reduction Medium Buffer Size % Reduction Small Buffer Size Application 25.20%26.16%25.31% Web 73.30%69.26%73.48% Video 46.55%39.87%46.57% File Sharing 1.00%0.86%1.08% VoIP

33. C ONCLUSIONS Open Source implementation of LTE model Configurable Easy to install and use Provides functionalities like different application traffic generation and users distribution Base Station Application Optimizer implementation Trace Analyzers Performance Evaluation: Total bytes transferred reduction factor: 1.2-1.8 Average delay reduced by 75% Packet loss percentage reduced from 2% to 0.05%

34. F UTURE W ORK Add User mobility support: Implement basic handover process in NS 2 Support several deployment modes of Base Station Application Optimizer in an LTE network Implement new traffic generator in NS 2

35. R EFERENCES Patrick Donegan, "Backhaul Strategies for Mobile Carriers", In Heavy Reading, Vol. 4 No. 4, 2006. Ronit Nossenson, “Base Station Application Optimizer”, The International Conference on Data Communication Networking 2010 (DCNET), Athens, Greece. Holma, H., and Toskala, A., LTE for UMTS – OFDMA and SC- FDMA Based Radio Access, John Wiley & Sons Ltd, United Kingdom, 2009. Qin-long Qiu, Jian Chen, Ling-di Ping, Qi-fei Zhang, Xue-zeng Pan, 2009, LTE/SAE Model and its Implementation in NS 2, 2009 Fifth International Conference on Mobile Ad-hoc and Sensor Networks, Fujian, China, pp. 299-303. LTE implementation: http://code.google.com/p/lte-model/ http://code.google.com/p/lte-model/ The Network Simulator (NS2), http://isi.edu/nsnam/ns/

36. R EFERENCES Introduction to Network simulator NS 2" by Teerawat Issariyakul and Ekram Hossain, 2009 Springer Science+Business Media, LLC Project source code and results: http://code.google.com/p/base-station-application- optimizer/ http://code.google.com/p/base-station-application- optimizer/ Allot Mobile Trends, Global mobile broadband traffic report, H1/2011. GNU Awk, http://www.gnu.org/s/gawk/manual/gawk.html http://www.gnu.org/s/gawk/manual/gawk.html