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Distributed-Dynamic Capacity Contracting:
A congestion pricing framework for Diff-Serv
Murat Yuksel and Shivkumar KalyanaramanRensselaer Polytechnic Institute, Troy, NY.
IEEE MMNS 20022
Overview Motivation/Context Framework: Dynamic Capacity
Contracting (DCC) Scheme: Edge-to-Edge Pricing (EEP) Distributed-DCC Simulation Experiments Summary
IEEE MMNS 20023
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 20024
DCC Framework
Stations of the providerimplement pricing strategiesfor the short-term contracts
Customers
Network Core(Accessed only by contracts)
EdgeRouter
EdgeRouter
EdgeRouter
EdgeRouter
EdgeRouter
EdgeRouter
EdgeStrategy
IEEE MMNS 20025
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:
ijijij pxb ˆ
IEEE MMNS 20026
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
),,( max TVpfContract vvp
TmaxV
vp
maxV
IEEE MMNS 20027
DCC Framework (cont’d)
User'straffic
p iji
3
j
User
c ij
kl
12
uv
4 m
DCC
User'straffic
User
kl
uv
4 m
p i
p j
p 3
p 2p 1
i
3
j
12
Low's
IEEE MMNS 20028
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 20029
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.
ijijij cbp /ˆijbijc
ijc
ijb
ijc
ijp
ijp
IEEE MMNS 200210
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 200211
Distributed-DCC (cont’d)Distributed-DCC Framework
.
.
.
.
.
.
Egress1
Ingress1
Egressn
Egress2
Ingressn
Ingress2
LPS
Customers
IEEE MMNS 200212
Distributed-DCC (cont’d)
CurrentRates of
CustomerFlows
measuredhere at
Ingress i
ContractParameters
(price,maximum
volume,length) toCustomers
in
i
i
b
b
b
ˆ
.
.
ˆ
ˆ
2
1
BudgetEstimationsof Flows to
Egresses
TotalEstimatedNetwork
Capacity andAllowed Flow
Capacitiesfrom LPS
Ingress i
T
V
pij
max
in
i
i
c
c
c
C
.
.2
1
PricingScheme
(e.g. EEP)
BudgetEstimator
in
i
i
x
x
x
.
.2
1
IEEE MMNS 200213
Distributed-DCC (cont’d)
CongestionIndications
EstimatedFlow
Capacities to LPS
nj
j
j
b
b
b
ˆ
.
.
ˆ
ˆ
2
1
BudgetEstimations of Flows
fromIngresses
CurrentOutput
Rates ofFlows
measuredhere at
Egress j
Egress j
Congestion-Based
CapacityEstimator
FairnessTuner
nj
j
j
c
c
c
ˆ
ˆ
ˆ
.
.1
1
nj
j
j
b
b
b
.
.
2
1UpdatedBudget
Estimation of
Flows toLPS
CongestionDetector
nj
j
j
.
.
2
1
ArrivingTraffic
atEgress j
Flow CostAnalyzer
(e.g. ARBE)ijr
Congestion
Indicationsabout
flows toLPS
IEEE MMNS 200214
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.:
ijc
ijc
ij
ijc
indication congestion no ,ˆ)1(ˆ
indication congestion ,)(ˆ
ctctc
ij
ij
ij
IEEE MMNS 200215
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
),,ˆ( parametersbfb ijij ijr
)(
ˆ),,,ˆ(
minminmin rrr
brrbfb
ij
ijijijij
01
IEEE MMNS 200216
Distributed-DCC (cont’d)
+
nnnn
n
n
ccc
ccc
ccc
ˆ..ˆˆ
....
ˆ..ˆˆ
ˆ..ˆˆ
21
22221
11211EstimatedFlow
Capacitiesreceived
fromEgresses
UpdatedBudget
Estimations of Flows
receivedfrom
Egresses
TotalEstimatedNetworkCapacity
andAllowed
FlowCapacitiesbeing sent to
Ingresses
CongestionIndications
about flowsreceived from
Egresses
Logical Pricing Server (LPS)
nnnn
n
n
bbb
bbb
bbb
..
....
..
..
21
22221
11211
nnnn
n
n
ccc
ccc
ccc
C
..
....
..
..
21
22221
11211
CapacityAllocator
(e.g. ETICA)
IEEE MMNS 200217
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
ijc ijb
ijc
IEEE MMNS 200218
Distributed-DCC (cont’d) An example Capacity Allocator:
Edge-to-edge Topology-Independent Capacity Allocation (ETICA).
Define for flow :
Define as congested, if .
ijK ijf
)1(in congestion no ,1)1(
)1(in congestion ,)(
ttK
tktK
ijij
ijf 0ijK
. . .1-p
p
p
p
p
1-p p1-p1-p1-p
kk-120 1
IEEE MMNS 200219
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.
ijf
otherwise ),(ˆ
0)( ,)(
tc
tKB
Cb
tc
ij
ijc
cij
ij
IEEE MMNS 200220
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 200221
Simulation Experiments (cont’d)
15Mb
15Mb
10Mb
0
1
15Mb
15Mb 1
2
flow 2
15Mb
0
15Mb
2
A B
flow 1flow 0
Single-Bottleneck
15Mb
15Mb 10Mb
15Mb
A
3
0
1
15Mb 010Mb D10Mb
15Mb
15Mb
15Mb
15Mb
1 2
CB
32
flow 3
flow 0
flow 1 flow 2
Multi-Bottleneck
IEEE MMNS 200222
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 200223
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 200224
Simulation Experiments (cont’d)
IEEE MMNS 200225
Simulation Experiments (cont’d)
IEEE MMNS 200226
Simulation Experiments (cont’d)
IEEE MMNS 200227
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 200228
Simulation Experiments (cont’d)
IEEE MMNS 200229
Simulation Experiments (cont’d)
IEEE MMNS 200230
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 200231
Simulation Experiments (cont’d)
IEEE MMNS 200232
Simulation Experiments (cont’d)
IEEE MMNS 200233
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.
