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. Overview. Motivation/Context Framework: Dynamic Capacity Contracting (DCC) Scheme: Edge-to-Edge Pricing (EEP)

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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

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

Murat Yuksel and Shivkumar Kalyanaraman

Rensselaer Polytechnic Institute, Troy, NY.


Overview

Overview

  • Motivation/Context

  • Framework: Dynamic Capacity Contracting (DCC)

  • Scheme: Edge-to-Edge Pricing (EEP)

  • Distributed-DCC

  • Simulation Experiments

  • Summary

IEEE MMNS 2002


Motivation context

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 2002


Dcc framework

DCC Framework

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Dcc framework cont d

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:

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Dcc framework cont d1

DCC Framework (cont’d)

  • 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

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Dcc framework cont d2

DCC Framework (cont’d)

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Dcc framework cont d3

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 2002


Edge to edge pricing eep

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 2002


Distributed dcc

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)

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Distributed dcc cont d

Distributed-DCC (cont’d)

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Distributed dcc cont d1

Distributed-DCC (cont’d)

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Distributed dcc cont d2

Distributed-DCC (cont’d)

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Distributed dcc cont d3

Distributed-DCC (cont’d)

  • 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


Distributed dcc cont d4

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

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Distributed dcc cont d5

Distributed-DCC (cont’d)

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Distributed dcc cont d6

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

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Distributed dcc cont d7

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 2002


Distributed dcc cont d8

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 2002


Simulation experiments

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.

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Simulation experiments cont d

Simulation Experiments (cont’d)

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Simulation experiments cont d1

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 2002


Simulation experiments cont d2

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.

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Simulation experiments cont d3

Simulation Experiments (cont’d)

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Simulation experiments cont d4

Simulation Experiments (cont’d)

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Simulation experiments cont d5

Simulation Experiments (cont’d)

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Simulation experiments cont d6

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.

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Simulation experiments cont d7

Simulation Experiments (cont’d)

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Simulation experiments cont d8

Simulation Experiments (cont’d)

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Simulation experiments cont d9

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.

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Simulation experiments cont d10

Simulation Experiments (cont’d)

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Simulation experiments cont d11

Simulation Experiments (cont’d)

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Summary

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.

IEEE MMNS 2002


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