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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

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

IEEE MMNS 2002

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:

IEEE MMNS 2002

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

IEEE MMNS 2002

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)

IEEE MMNS 2002

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

IEEE MMNS 2002

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

IEEE MMNS 2002

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.

IEEE MMNS 2002

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.

IEEE MMNS 2002

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.

IEEE MMNS 2002

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.

IEEE MMNS 2002

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