A Fair and Dynamic Load Balancing Mechanism F. Larroca and J.L. Rougier

1. A Fair and Dynamic Load Balancing MechanismF. Larroca and J.L. Rougier International Workshop on Traffic Management and Traffic Engineering for the Future Internet Porto, Portugal, 11-12 December, 2008

2. Agenda • Introduction • Utility Maximization Load-Balancing • Distributed Algorithm • Simulations • Packet-Level Simulations • Fluid-Level Comparison • Conclusions F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

3. Introduction • Network Convergence: • Traffic increasingly unpredictable and dynamic • Classic TE techniques (i.e. over-provisioning) inadequate: • Ever-increasing access rates • New emerging architectures with low link capacities • Possible answer: Dynamic Load-Balancing • Origin-Destination (OD) pairs with several paths: how to distribute its traffic? • Paths configured a priori and distribution dependent on current TM and network condition F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

4. Introduction • Network operator interested OD pairs obtained performance • Why not state the problem in their terms? • Analogy with Congestion Control (TCP): • End-hosts = OD pairs • Rate = OD performance indicator • Differences: • Decision variable: portion of traffic sent through each path (total traffic is given) • Much larger time-scale F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

5. Introduction • Previous proposals: • Define a link-cost function Fl(rl) for each link l=1..L • Minimize the total network’s cost • Limitations: • Indirect way of proceeding • Cannot prioritize an OD pair or enforce fairness • Example: F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

6. Agenda • Introduction • Utility Maximization Load-Balancing • Distributed Algorithm • Simulations • Packet-Level Simulations • Fluid-Level Comparison • Conclusions F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

7. Utility MaximizationLoad-Balancing • Define a single performance indicator per OD pair • us(d): performance perceived by OD pair s when traffic distribution is d • “Distribute” us(d) among OD pairs to maximize total Utility (à la Congestion Control) • ds = total demand of OD pair s (given) • dsi = traffic sent through path i of OD pair s (∑dsi= ds) • d = [ d11 d12 .. dS1 .. dSnS]T • How to define us(d)? F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

8. Utility MaximizationLoad-Balancing • Our choice for us(d): mean path’s Available Bandwidth (ABW) • Assumptions: • Majority of traffic is elastic (i.e. TCP) • Path choice considered propagation delay • Advantages: • Mean ABW rough approximation of rate obtained by TCP flows (ABW is the most important indicator) • Sudden increases in demand may be accommodated F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

9. Utility MaximizationLoad-Balancing • Final version of the problem: • If ABWsi is the flow obtained rate, the problem is very similar to Multi-Path TCP • By only changing ingress routers, users may be regarded as if they used MP-TCP: improved performance and more supported demands F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

10. Agenda • Introduction • Utility Maximization Load-Balancing • Distributed Algorithm • Simulations • Packet-Level Simulations • Fluid-Level Comparison • Conclusions F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

11. Distributed Algorithm • The optimization problem is not convex • However, not too “unconvex” • The distributed algorithm solves the dual problem and results in a good approximation • Based on the Harrow-Hurwitz method: greedy on path utility (PU) minus path cost (PC) F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

12. Agenda • Introduction • Utility Maximization Load-Balancing • Distributed Algorithm • Simulations • Packet-Level Simulations • Fluid-Level Comparison • Conclusions F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

13. Packet-Level Simulations • A simple example: all links have the same capacity and probabilities are updated every 50 seconds F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

14. Fluid-Level Simulations • In two real topologies and TMs: • Comparison with two previous proposals: • MATE: minimize total M/M/1 delay • TeXCP: greedy on the path’s maximum utilization • Two performance indicators: • Mean ABW (us) (weighted mean, 10% quantile and minimum) • Link Utilization (mean, 90% quantile and maximum) F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

15. Fluid-Level Simulations – Abilene • Mean ABW (us) • Link Utilization UM/MATE UM/TeXCP TeXCP - UM TeXCP - MATE F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

16. Fluid-Level Simulations – Géant • Mean ABW (us) • Link Utilization UM/MATE UM/TeXCP TeXCP - MATE TeXCP - UM F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

17. Agenda • Introduction • Utility Maximization Load-Balancing • Distributed Algorithm • Simulations • Packet-Level Simulations • Fluid-Level Comparison • Conclusions F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

18. Conclusions • Performance as perceived by OD pairs is always better in UM than in MATE or TeXCP • MATE: relatively small differences in mean, but significant in the worst case • TeXCP: more significant differences • Link utilization results for TeXCP and UM are very similar • MATE: although similar in mean and quantile, the maximum link utilization may increase significantly • Future Work: • Stability • Other simpler methods or objective function that obtains similar results F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

19. Thank you Questions? F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008