1. University of Kansas Department of Electrical Engineering and Computer Science March 8, 20001 Finding Your Shade of Grey on the Network Spectrum Towela P.R. Nyirenda-Jere Victor S. Frost (sponsored by Sprint)

2. University of Kansas Department of Electrical Engineering and Computer Science March 8, 20002 The Network Spectrum Simple traffic handling + Huge Capacity Moderate traffic handling + Moderate Capacity Complex traffic handling + Minimal Capacity

3. University of Kansas Department of Electrical Engineering and Computer Science March 8, 20003 The Problem Determining the equivalence of traffic handling mechanisms Understanding the trade-off between the complexity of traffic handling mechanisms and the network capacity required to support service guarantees

4. University of Kansas Department of Electrical Engineering and Computer Science March 8, 20004 What we know Service guarantees depend on both traffic handling and network capacity Total aggregation schemes require more capacity than per flow schemes Partial aggregation schemes scale better than per-flow schemes when number of flows is large

5. University of Kansas Department of Electrical Engineering and Computer Science March 8, 20005 What we don’t know How much more capacity do we need with total aggregation versus per-flow? How does the complexity of per-flow management measure up against the cost of additional capacity with aggregate traffic handling? What about partial aggregation schemes?

6. University of Kansas Department of Electrical Engineering and Computer Science March 8, 20006 What we need Quantification of the gain obtained by using complex traffic handling with smaller network capacity versus using simple traffic handling with abundant network capacity Quantification of the sensitivity of traffic handling to changes in network load both in terms of the total load and in terms of the relative mix of different classes

7. University of Kansas Department of Electrical Engineering and Computer Science March 8, 20007 What should be done Quantify trade-off between complexity of traffic handling and network capacity Determine scalability of the analytic methods with network size and capacity Provide analytic framework for capacity provisioning and traffic handling strategy Study the sensitivity of traffic handling schemes to changes in network load

8. University of Kansas Department of Electrical Engineering and Computer Science March 8, 20008 Traffic Handling Mechanisms

9. University of Kansas Department of Electrical Engineering and Computer Science March 8, 20009 Traffic Modeling Deterministic burstiness constraint model of Cruz et. al. Traffic described by two parameters: average rate and burstiness No assumptions on traffic type Aligns well with IETF and ATM Forum traffic description

10. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200010 Analytic Method Use Network Calculus approach of Cruz, Parekh & Gallagher WFQ is used as the reference mechanism Find number of voice, video, e-mail and WWW sources using WFQ taking into account delay requirements Find capacity required to support these sources using CBQ, PQ and FIFO

11. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200011 Applications and Service Requirements

12. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200012 Scenario Single-node Network OC-3 Link for WFQ Video load = 10% of OC-3 Voice load varied from 10-90% of OC-3 E-mail and WWW share remaining capacity using pre-defined ratios

13. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200013 CBQ Capacity Requirements Capacity requirements of CBQ same order of magnitude as WFQ

14. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200014 PQ Capacity Requirements Capacity requirements of PQ same order of magnitude as WFQ Non-monotonic

15. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200015 PQ and CBQ Capacity Requirements PQ capacity does not exceed CBQ capacity

16. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200016 FIFO Capacity Requirements FIFO requires two orders of magnitude more capacity than WFQ

17. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200017 Sensitivity to Design Point Goal is to explore the ability of the three schemes to provide acceptable delay guarantees when the traffic submitted exceeds the traffic for which the network was designed Two broad cases »voice as dominant class »WWW as dominant class

18. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200018 Sensitivity: Network designed for Voice WFQ1, CBQ sensitive to increase in voice PQ, FIFO not as sensitive

19. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200019 Sensitivity: Network designed for WWW FIFO most sensitive to increase in WWW traffic PQ least sensitive

20. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200020 Projections on Network Traffic and Capacity Assume 5% growth in Voice and 100% growth in WWW per year Initially OC-3 link with total utilization 45% »5% voice, 15% e-mail and 25% WWW

21. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200021 Projections on Network Traffic and Capacity FIFO capacity at year 5 is 2000x capacity at year 1

22. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200022 Projections on Network Traffic and Capacity CBQ and PQ capacity at year 5 is 8x capacity at year 1 WFQ capacity at year 5 is 4x capacity at year 1

23. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200023 Shades of Grey

24. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200024 What Have We Learned It is possible to quantify the trade-off between network capacity and traffic management Sensitivity of the traffic handling schemes depend on the assumptions made in designing the network as well as the traffic class contributing to the growth in traffic

25. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200025 What’s Next Review methodology and define performance metrics/indices Extend analysis to carrier-size networks Incorporate stochastic bounds on performance

26. University of Kansas Department of Electrical Engineering and Computer Science March 8, 200026 Significance Identifying the tradeoffs associated with the use of traffic handling mechanisms with respect to network capacity Sensitivity analysis will provide a tool for long- term planning Development of a methodology which can be used to compare traffic handling schemes in general