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A Fair and Dynamic Load Balancing Mechanism F. Larroca and J.L. Rougier International Workshop on Traffic Management and Traffic Engineering for the Future.

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Presentation on theme: "A Fair and Dynamic Load Balancing Mechanism F. Larroca and J.L. Rougier International Workshop on Traffic Management and Traffic Engineering for the Future."— Presentation transcript:

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

2 page 1 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 page 2F. 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 page 3F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

5 Introduction Previous proposals: Define a link-cost function  l  l  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 page 4F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008 Example:

6 page 5 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 Maximization Load-Balancing Define a single performance indicator per OD pair u s (d) : performance perceived by OD pair s when traffic distribution is d “Distribute” u s (d) among OD pairs to maximize total Utility (à la Congestion Control) d s = total demand of OD pair s (given) d si = traffic sent through path i of OD pair s ( ∑d si = d s ) d = [ d 11 d 12.. d S1.. d SnS ] T How to define u s (d) ? page 6F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

8 Utility Maximization Load-Balancing Our choice for u s (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 page 7F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

9 Utility Maximization Load-Balancing Final version of the problem: If ABW si 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 page 8F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

10 page 9 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) page 10F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

12 page 11 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 page 12F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

14 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 ( u s ) (weighted mean, 10% quantile and minimum) Link Utilization (mean, 90% quantile and maximum) Fluid-Level Simulations page 13F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008 In two real topologies and TMs:

15 Mean ABW ( u s ) Link Utilization Fluid-Level Simulations – Abilene page 14F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

16 Fluid-Level Simulations – Géant Mean ABW ( u s ) Link Utilization page 15F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

17 page 16 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 page 17F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

19 page 18 Thank you Questions? F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008

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