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Distributed-Dynamic Capacity Contracting: A congestion pricing framework for Diff-Serv

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Distributed-Dynamic Capacity Contracting: A congestion pricing framework for Diff-Serv

Murat Yuksel and Shivkumar Kalyanaraman

Rensselaer Polytechnic Institute, Troy, NY.

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

IEEE MMNS 2002

- 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 2002

IEEE MMNS 2002

- 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 2002

- The provider offers short-term contracts:
- is price per unit volume
- Vmax is maximum volume user can contract for
- T is contract length

- Pv is formulated by “pricing scheme” at the ingress, e.g. EEP, Price Discovery
- Vmax is a parameter to be set by soft admission control

IEEE MMNS 2002

IEEE MMNS 2002

- 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 2002

- 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 2002

- 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 2002

IEEE MMNS 2002

IEEE MMNS 2002

IEEE MMNS 2002

- Congestion-Based Capacity Estimator:
- Estimates available capacity for each flow fij exiting at Egress j
- To calculate it uses:
- Congestion indications from Congestion Detector
- Actual output rates of flows

- Increase when fij generates congestion indications, decrease when it does not, e.g.:

IEEE MMNS 2002

- 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 2002

IEEE MMNS 2002

- 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 2002

- An example Capacity Allocator:
- Edge-to-edge Topology-Independent Capacity Allocation (ETICA).
- Define for flow :
- Define as congested, if .

- Edge-to-edge Topology-Independent Capacity Allocation (ETICA).

IEEE MMNS 2002

- 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 2002

- 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 2002

IEEE MMNS 2002

- 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 2002

- 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 2002

IEEE MMNS 2002

IEEE MMNS 2002

IEEE MMNS 2002

- 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 2002

IEEE MMNS 2002

IEEE MMNS 2002

- 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 2002

IEEE MMNS 2002

IEEE MMNS 2002

- 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.

IEEE MMNS 2002