Distributed-Dynamic Capacity Contracting: A congestion pricing framework for Diff-Serv Murat Yuksel and Shivkumar Kalyanaraman Rensselaer Polytechnic Institute, Troy, NY.

IEEE MMNS Overview Motivation/Context Framework: Dynamic Capacity Contracting (DCC) Scheme: Edge-to-Edge Pricing (EEP) Distributed-DCC Simulation Experiments Summary

IEEE MMNS Motivation/Context Multimedia (MM) applications introduce extensive traffic loads. Hence, better ways of managing network resources are needed for provision of sufficient QoS for MM applications. For this purpose, congestion pricing is one of the methods among many others. Two major implemetation problems: Timely feedback about price Congestion information about the network

IEEE MMNS DCC Framework

IEEE MMNS DCC Framework (cont’d) Solves implementation issues by: Short-term contracts, i.e. middle-ground between Smart Market and Expected Capacity Edge-to-edge coordination for price calculation Users negotiate with the provider at ingress points The provider estimates user’s incentives by observing user’s traffic at different prices A simple way of representing user’s incentive is his/her budget Budget estimation:

IEEE MMNS DCC Framework (cont’d) The provider offers short-term contracts: is price per unit volume V max is maximum volume user can contract for T is contract length P v is formulated by “pricing scheme” at the ingress, e.g. EEP, Price Discovery V max is a parameter to be set by soft admission control

IEEE MMNS DCC Framework (cont’d)

IEEE MMNS DCC Framework (cont’d) Key benefits: Does not require per-packet accounting Requires updates to edges only enables congestion pricing by edge-to-edge congestion detection techniques deployable on diff-serv architecture of the Internet

IEEE MMNS Edge-to-Edge Pricing (EEP) At Ingress i, given and : Balancing supply (edge-to-edge capacity) and demand (budget for route ij) If is congestion-based (i.e. decreases when congestion, increases when no congestion), then becomes a congestion-sensitive price. formulation above is optimal for maximization of total user utility.

IEEE MMNS Distributed-DCC DCC + distributed contracting, i.e. flexibility of advertising local prices Defines: ways of maintaining stability and fairness of the overall system Operates on a per-edge-to-edge flow basis Major components: Ingresses Egresses Logical Pricing Server (LPS)

IEEE MMNS Distributed-DCC (cont’d)

IEEE MMNS Distributed-DCC (cont’d)

IEEE MMNS Distributed-DCC (cont’d)

IEEE MMNS Distributed-DCC (cont’d) Congestion-Based Capacity Estimator: Estimates available capacity for each flow f ij exiting at Egress j To calculate it uses: Congestion indications from Congestion Detector Actual output rates of flows Increase when f ij generates congestion indications, decrease when it does not, e.g.:

IEEE MMNS Distributed-DCC (cont’d) Fairness Tuner: Punish the flows causing more cost! Punishment function: A particular version by using from Flow Cost Analyzer: Max-min fairness, when Proportional fairness, when

IEEE MMNS Distributed-DCC (cont’d)

IEEE MMNS Distributed-DCC (cont’d) Capacity Allocator Receives congestion indications, and Calculates allowed capacities for each flow Hard to do w/o knowledge of interior topology In general, Flows should share capacity of the same bottleneck in proportion to their budgets Flows traversing multiple bottlenecks should be punished accordingly

IEEE MMNS Distributed-DCC (cont’d) An example Capacity Allocator: Edge-to-edge Topology-Independent Capacity Allocation (ETICA). Define for flow : Define as congested, if.

IEEE MMNS Distributed-DCC (cont’d) An example Capacity Allocator: (cont’d) Allowed capacity for flow : Intuition: If a group of flows are congested, then it is more probable that they are traversing the same bottleneck. Assumes no knowledge about interior topology.

IEEE MMNS Simulation Experiments We want to illustrate: Steady-state properties of Distributed- DCC: queues, rate allocation Distributed-DCC’s fairness properties Performance of the capacity allocation in terms of adaptiveness.

IEEE MMNS Simulation Experiments (cont’d)

IEEE MMNS Simulation Experiments (cont’d) Propagation delay is 5ms on each link Packet size 1000B Users generate UDP traffic Interior nodes mark when their local queue exceeds 30 packets. User with a budget b maximizes its surplus by sending at a rate b/p. For each contracting period, users’ budgets are randomized with truncated-Normal. Contracting 4s, observation 0.8s, LPS 0.16s. k is 25, i.e. a flow stays in congested states for 25 LPS intervals, or one contract period.

IEEE MMNS Simulation Experiments (cont’d) Single-bottleneck experiment: 3 user flows Flow budgets 30, 20, 10 respectively for flows 0, 1, 2. Simulation time 15,000s. Flows get active at every 5,000s.

IEEE MMNS Simulation Experiments (cont’d)

IEEE MMNS Simulation Experiments (cont’d)

IEEE MMNS Simulation Experiments (cont’d)

IEEE MMNS Simulation Experiments (cont’d) Multi-bottleneck experiment 1: 10 user flows with equal budgets of 10 units. Simulation time 10,000s. Flows get active at every 1,000s. All the other parameters are the same as in the PFCC experiment on single- bottleneck topology.  is varied between 0 and 2.5.

IEEE MMNS Simulation Experiments (cont’d)

IEEE MMNS Simulation Experiments (cont’d)

IEEE MMNS Simulation Experiments (cont’d) Multi-bottleneck experiment 2: 4 user flows Simulation time 30,000s. Increase capacity of node D from 10Mb/s to 15Mb/s. All flows get active at the starts of simulation. Initially all flows have equal budget of 10 units. Flow 1 temporarily increases its to 20 units between times 10,000 and 20,000.  is 0.

IEEE MMNS Simulation Experiments (cont’d)

IEEE MMNS Simulation Experiments (cont’d)

IEEE MMNS Summary Deployability of congestion pricing is a problem. A new congestion pricing framework, Distributed-DCC: Middle-ground between Smart Market and Expected Capacity. Deployable on a diff-serv domain. A range of fairness capabilities.