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Supporting Application Network
Flows with Multiple QoS Constraints
Amit Mondal, Northwestern University
Puneet Sharma, HP Labs
Sujata Banerjee, HP Labs
Aleksandar Kuzmanovic, Northwestern University
IP multiplay
Same IP/access network is used for different
applications with diverse QoS requirements
A. Mondal
Supporting Application Network Flows with Multiple QoS Constraints
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QoS and the Internet
 Real-time and interactive
multimedia applications have
multiple QoS requirements
IP routing
Best effort service
Destination-based routing
“high throughput”
 Packets for different applications
with varied QoS requirements
traverse the same Internet path to a
destination
 No widespread deployment of
Quality-of-Service
A. Mondal
“low delay”
Supporting Application Network Flows with Multiple QoS Constraints
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QoS provisioning using overlay networks
Build Overlay Backbone
Deploy overlay nodes at strategic locations in the
Internet.
Provide support for per-flow forwarding
e.g. Anagran Flow Aware Routers
Flow route management architecture
Discover and setup end-to-end paths for individual
flows with diverse flow QoS requirements
Monitor end-to-end flow performance to trigger
path adaptation
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Supporting Application Network Flows with Multiple QoS Constraints
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Overlay flow QoS management architecture
Configure intermediate
overlay nodes for per-flow
forwarding
Find a path to X
with b/w > b, delay
< d and loss < l%
Sensing local link
Adapt to different
characteristics
path dynamically as
current path fails to
meet QoS parameters
AS1
AS3
AS4
AS2
End user
Overlay node
Physical link
Logical link
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Our contribution
 Design a scalable QoS routing protocol which
finds path under multiple constraints
 Propose a distributed algorithm for dynamic
path adaptation
 Evaluate accuracy, efficiency and scalability
of the protocol using large-scale simulation
and compare with other existing approaches
 Built a functional prototype using Click
modular router
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Design challenges
Multiple QoS metrics
Finding a feasible path using Dijkstra’s algorithm is
NP-Complete
Randomized and approximation algorithms
Single composite metric derived from multiple
metrics
 Paths might not meet individual QoS constraints
Dynamic overlay-link properties
Increases control message overhead
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Supporting Application Network Flows with Multiple QoS Constraints
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Overview of our multi-constraint QoS
routing protocol
Path vector protocol to disseminate path
information
Tag with QoS parameters
How to aggregate path information when
multiple QoS metrics are considered?
Distribute the best paths for each metrics
What about QoS requests which could be
served by paths which are not in the best
path set?
On-demand route discovery
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Supporting Application Network Flows with Multiple QoS Constraints
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MCQoS: Multi-constraints QoS routing
protocol
Tag QoS
Advertise best path
for each metric
characteristics
X AS1 (10ms, 0.01%, 1Mbps) AS3 (5ms, 0.01%, 768Kbps) AS5
B/w
X AS1 (2ms, 0.0%, 128Kbps) AS3 (3ms, 0.005%, 378Kbps) AS5 Loss
X AS1 (2ms, 0.01%, 128Kbps) AS3 (3ms, 0.02%, 378Kbps) AS5 Delay
Local link
info
B
A
QoS Path
Table
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Supporting Application Network Flows with Multiple QoS Constraints
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Aggregating path information
best b/w
infeasible
undecideable
best delay
BW
feasible
Delay
There will feasible requests that can be supported
The
source
nodeexist
already
knows apath
pathinifthe
the
There
cannot
a feasible
but the
source
node
might
not
know
about
those
»
What
about
QoS
requests
in
the
QoS
request
fallsQoS
in the
feasible
region
network
if
the
request
falls
in the
paths, thus cannot
admit
flows
based
on
local
undeciable region?
infeasible region
information
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Supporting Application Network Flows with Multiple QoS Constraints
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On-demand route discovery
infeasible
undecideable
 Admit or deny flow based
on local QoS table if in
feasible or infeasible region
BW
feasible
Delay
C
A
B
E
 Otherwise, On-demand
route discovery for requests
in undecideable region
 Exploit advertisement
received from neighbors to
reduce search space while
route discovery
D
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Supporting Application Network Flows with Multiple QoS Constraints
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An example
best b/w
best delay
10ms 12Mbps
100ms 50Mbps
4ms 5Mbps
C
(5ms, 100Mbps)
105ms 50Mbps
A
B
(1ms, 100Mbps)
5ms 5Mbps
106ms 50Mbps
(2ms, 20Mbps)
2ms
8ms
E
5MbpsD
20Mbps
Requests:
10ms,
3Mbps
10ms, 3Mbps
120ms, 15Mbps
10ms, 100Mbps
15ms,
15ms, 15Mbps
15Mbps
A. Mondal
OK
OK ABD…E
OK ABC…E
X
---OK
??? ABD…E
Supporting Application Network Flows with Multiple QoS Constraints
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Route maintenance
 Path restoration through path
patching
 Each intermediate node knows
the QoS requirements from
the node to the destination
 Upstream node periodically A
pushes QoS requirements to
downstream nodes
 As a node detects QoS
violation, it triggers alternate
path search at local node
Notify upstream node if no
alternative path
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D
F
H
C
E
G
B
Supporting Application Network Flows with Multiple QoS Constraints
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Evaluation
Event-driven simulator
GT-ITM to generate random flat topology
Outdegree min(10, size/2)
Assign link metrics from actual planetlab link
measurement data
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Convergence time of MCQoS
 Convergence time: how long does it take to
stabilize for a given network snapshot?
Being
path vector based
protocol
MCQoS
 Restabilization
time:
how long
does it take
takestolonger
time once
to converge,
but does
not
stabilize
a link metric
changes?
involve any NP-hard computation, thus scale
with network size
 QRON: Link state based multi-QoS routing
protocol using composite metric approach
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Supporting Application Network Flows with Multiple QoS Constraints
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Message overhead of path
dissemination
Message overhead of MCQoS is comparable
to Link-State based (QRON) protocol
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Depletion area
K-hop path: paths in the undecidabe region discovered
within k-hops of on-demand route discovery process
Global feasible region: feasible region at the
source
Depletion area
node if the source node knew all alternative paths like
link-state protocol
Depletion area: part of global feasible QoS region not
known at the source node because many alternate paths
are suppressed
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Overhead of on-demand path
discovery
How many hops does it take to discover the entire
depletion area?
We measure the fraction of depletion area
discovered within k hops from the source node
More than 90% of the depletion area is
discovered within 3 hops
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Comparison with other approaches
 Composite Metric = K1*delay + k2/bw
where k1=1, k2 = 10^7, delay in sec, bw in
bps
 False positive: flow is admitted but the
path does not meet the QoS
 False negative: there exists a feasible path
but the flow is not admitted
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Comparison with other approaches
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Comparison with other approaches
 Lui et al. proposed line segment based
approach to for topology aggregation in
delay-bw plane. Tam et al. designed a
distance vector based QoS protocol using
the line-segment approach
 False positive: Fraction of undecidable
region that is actually infeasible, but the
approach labels as feasible.
 False negative: Fraction of undecidable
region that is feasible, but the approach
labels as infeasible.
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Comparison with other approaches
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Comparison with other approaches
 A feasible path with a composite metric
might not satisfy individual QoS metrics.
 The line-segment based approach often
suffers from loss/distortion.
 Our hybrid approach has no false positive
and false negative percentage can be
reduced to less than one 1% by 3-hop ondemand route discovery.
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Supporting Application Network Flows with Multiple QoS Constraints
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QoS violation ratio in dynamic
environment
 100 node topology
 Generate QoS requests with certain arrival
rate with bw [5Mbps, 55Mbps] and delay
[100ms,400ms]
 Each flows last between 5 to 10 minute
 We simulate the network behavior for 10 min
 New flows arrive before network stabilizes
 Expect to observe QoS violation
Arrival
60
120
The
rateQoS violation ratio
(conn/sec)
rate
of 600 conn/sec.
Violation
ratio(%)
A. Mondal
0.32
0.33
240
300
600
0.78
0.4
1.12
is negligible even with arrival
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Prototype
QoS path setup (Y:p -> X:q, Dms, L%, BKbps)
Peers (path ads)
Local link
Characteristics
MCQoSd
Rt. discovery req,
Rt. discovery reply
Flow setup req
Control
Plane
S3
QoS Path
table
Flow setup
Flow id nexthop
Y:p->X:q
Click Router
DataIn
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DataOut
Supporting Application Network Flows with Multiple QoS Constraints
Data
Plane
25
Conclusion
 Designed a scalable multiple constraints QoS
flow route management protocol
 hybrid approach of path vector routing and ondemand route discovery
 Keep balance between flow setup time and
control message overhead
 No complex NP-hard computation
 Performed large-scale simulations to
demonstrate the efficiency and scalability of
the approach
 Built a prototype using Click modular router
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Thank You!
&
Questions ?
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Overlay flow QoS management architecture
Configure intermediate
overlay nodes for per-flow
Sensing nor
local link
We are not addressing forwarding
the problem of overlay node placement
Find abuilding
path to
X network! It is in fact orthogonal tocharacteristics
a overlay
the problem
Adapt to different
we are
with b/w
> b,addressing
delay here.
path dynamically as
< d and loss < l%
current path fails to
meet QoS parameters
AS1
AS3
AS4
AS2
End user
Overlay node
Physical link
Logical link
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Path vector protocol
 Advertise local reachability
 Receive reachability information from neighbors
 Collect local link information
 Aggregate received advertisements
 Disseminate the aggregated path information
X
dest
Local link
info
Path Table
B
32
cost
AS1
AS3
AS5
Path
vector
A
dest nexthop
X A
Forwarding table
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MCQoS: Multi-constraints QoS routing
protocol
Tag QoS
characteristics
Advertise best path
for each metric
X AS1 (10ms, 0.01%, 1Mbps) AS3 (5ms, 0.01%, 768Kbps) AS5
B/w
X AS1 (2ms, 0.0%, 128Kbps) AS3 (3ms, 0.005%, 378Kbps) AS5 Loss
X AS1 (2ms, 0.01%, 128Kbps) AS3 (3ms, 0.02%, 378Kbps) AS5 Delay
Local link
info
QoS Path
Table
B
A
Flow id nexthop
Y:p->X:q
C
Forwarding table
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Supporting Application Network Flows with Multiple QoS Constraints
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No of hops required to discover
entire depletion area?
More than 90% of the times the entire
depletion area is discovered within 5-hops
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Supporting Application Network Flows with Multiple QoS Constraints
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System Implementation
QoS path setup (Y:p -> X:q, Dms, L%, BKbps)
Peers (path ads)
Local link
Characteristics
OQoSd
Rt. discovery req,
Rt. discovery reply
Flow setup req
Control
Plane
S3
QoS Path
table
Flow setup
Flow id nexthop
Y:p->X:q
Click Router
DataIn
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DataOut
Supporting Application Network Flows with Multiple QoS Constraints
Data
Plane
32
Classification of services
• Best effort
– File transfer, E-mail, Web-surfing
• New Services require Quality-of-Experience
– VOIP, IPTV, VoD, Video conferencing
• Quality-of-Experience includes
– Speed (bandwidth)
– Interactivity (low delay, jitter, loss)
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Design challenges
 Multiple QoS metrics
 Finding a feasible path using Dijkstra’s algorithm is NPComplete
 Another approach is to use single composite metric derived
from multiple metrics
 some paths might not meet each QoS metric requirement
individually
 Dynamic overlay-link properties
 Increases control message overhead
 limits scalability
 Adaptive Routing
 Monitor to detect violation and adopt alternate path in such
cases.
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Future work
• Scalability analysis of the protocol
• Formal correctness proof of the routing
protocol
• Implementing dynamic path adaptation/Route
maintenance algorithm
• Planetlab experiment with large and complex
topologies
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Our goal
• Design scalable flow route management
architecture to
– Efficiently discover paths to meet requests
with multiple QoS constraints
– Flow establishment
• Configure overlay nodes in the path for per-flow
forwarding
– Route Maintenance
• Adapt to alternate path dynamically to continually
guarantee flow QoS requirements
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Classification of QoS metrics
•
Additive metric e.g. delay
•
Multiplicative metric e.g. loss
•
Concave metric e.g. bandwidth
•
Jitter, Cost are also additive
•
Wang and Crowcroft proved that finding path subject to
n additive and m multiplicative constraints is NPComplete if n+m >= 2
delay(A-B-C-D) = delay(AB) + delay(BC) + delay(CD)
loss(A-B-C-D) = 1 – (1-loss(AB))(1-loss(BC))(1-loss(CD))
bw(A-B-C-D) = min { bw(AB), bw(BC), bw(CD)}
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Preliminary evaluation
• Evaluated in controlled
environment
– Emulab
A
B
• Planetlab expt
• Live demo with VLC
media player in Lab
setup
G
E
H
F
nrg048
378Kbps
VLC client
sauvignon
HP Labs LAN
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D
C
nrg002
256Kbps
Default path
nrg049
nrg090
VLC server
Supporting Application Network Flows with Multiple QoS Constraints
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Demo Topology
nrg048
378Kbps
VLC client
256Kbps
sauvignon
HP Labs LAN
nrg002
Default path
nrg049
nrg090
VLC server
Requests:
- 300 Kbps
- 128 Kbps
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Evaluation Metrics
• Control overhead/scalability
– Path vector + On-demand route discovery
• Flow admission delay
– On-demand route discovery + Flow setup time
• Amortized value depends on % of QoS flow requests that fall in
the undecideable region
– Planetlab measurements for path characteristics distribution
– MEDISYN for synthetic QoS requests generation
• Convergence time
– Worst case path vector
– Improved using frequent updates with interest-based path
information dissemination
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Design Choice
Link State
• Finding a feasible path
under multiple
constraints - hard
problem 
• Optimal path with single
derived metric often
does not meet individual
requirements 
• High cost of flooding
link state information in
large network 
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Path Vector
• Distributed path
computation makes path
finding easy even under
multiple constraints. No
complex graph search 
• No flooding of individual
link metrics or updates
instead distribute
aggregated path metrics
• Advertising all alternative
paths may not be feasible,
how to choose the best
path/a representative
path under multiple
metrics
? QoS
 Constraints
Supporting Application Network Flows
with Multiple
42
Emulab Experiment
C
A
D
B
G
E
H
F
Overlay topology
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Route Maintenance
• Path restoration through
path patching
• Trigger alternate path
search at local node
– Notify upstream node
if no alternative path
• End-to-end session delay
monitoring to figure out
delay violation
A. Mondal
D
F
D
A
C
E
G
B
C
A
E
B
D
Supporting Application Network Flows with Multiple QoS Constraints
F
44