Building Blocks for Engineering QoS Expectations over Best-Effort Networks
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Transcript of Building Blocks for Engineering QoS Expectations over Best-Effort Networks
Shivkumar KalyanaramanRensselaer Polytechnic Institute
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Building Blocks for Engineering QoS Expectations over Best-Effort Networks
David Harrison, Yong Xia, Omesh Tickoo, Hema T. Kaur, Shiv Kalyanaraman
I E
I
EI
E
Logical FIFO
B
A
ED
CB
F2
21
3
1
1
2
53
5
2
: “shiv rpi”
Shivkumar KalyanaramanRensselaer Polytechnic Institute
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Context: Rest-in-Peace QoS ? (SIGCOMM RIPQoS 2003)
QOSQOS
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Our Goals…
HISTORY: TCP offers reliability over unreliable networks
Our focus: Building blocks for …
… performance expectations …
over (I.e. as an overlay)
…multi-domain inter-networks …
...that do not guarantee performance… (I.e. best-effort networks)
Shivkumar KalyanaramanRensselaer Polytechnic Institute
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Talk Overview
Part 3: E2E Expected QoSPipe Abstraction:
H2H Multimedia Apps
Part 1:I E
I
EI
E
Logical FIFO
B
Expected QoSUsing Closed-loopTechniques
Part 2:A
ED
CB
F2
21
3
1
1
2
53
5
2
BANANAS Multi-path & TE Framework: incrementallydeployable
[Eg: MSN/Yahoo + Akamai]
Shivkumar KalyanaramanRensselaer Polytechnic Institute
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Part 1: Overlay Closed-loop QoS
FIFO
B
Loops: differentiate service on an RTT-by-RTT basis using edge-based policy configuration.
Differentiation/Isolation meaningful in steady state only…
B
Priority/WFQ
Scheduler: differentiates service on a packet-by-packet basis
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Architectural Advantages of Closed Loops Traffic management consolidated at edges
(placement of functions in line with E2E principle)
End system
Bottleneckqueue
Edge system
Architectural Potential: Edge-based (distributed) QoS services, Edge plays in application-level QoS
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Expected Min Rate (EMR) Service: Sample Steady State Behavior
Flow 1 with 4 Mbps assured + 3 Mbps best effort
Flow 2 with 3 Mbps best effort
Issues: Transients, steady state oscillations/tracking errors
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Overlay QoS w/ Closed Loops
and therefore,
QoS can be posed in Kelly’s optimization framework
Challenges:
1. What about the non-concavity of QoS utility functions?
2. Can we avoid admission control? Graceful degradation instead.
Ans:
1. Define & Solve an Auxiliary Optimization Problem
2. Alternative Convex Constraints in Lagrange Domain can avoid need for admission control, allowing graceful service degradation
QoS can be viewed as a congestion control problem: extension of the idea of fairness
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Kelly’s Optimization Formulation
)log( ss xwMaximize
Network N optimizes
Each user s optimizes
subject to capacity constraints
ss wxU )(Maximize
Resulting System optimizes
)( sxUMaximize
weightUser s’s send rate
User s’s utility function
subject to capacity constraints
ws can be interpreted
as price per unit time or as congestion in the network.
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Two User Utility Functions
optimum
)log()log()()( yxyUxU Maximize
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Issue: QoS => Non-concave User Utility Functions A single user with a minimum rate QoS expectation (gracefully degrading into a
weighted service) can be modeled with a non-concave utility function. But this kind of U-function cannot be plugged directly into Kelly’s non-linear
optimization formulation!
log(xs)
expected minimum =0.410log(xs)
log(xs-0.4)
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Luckily, the Sum of Non-Concave U-fns is not what we want to Optimize!
U(z) = log(z-0.6) if z > 0.6 (expected minimum rate)
10logz if z <= 0.6 (graceful degradation to weighted svc)
Two maxima
Desired Allocation(oversubscribed)not anyof the 2 maxima!
• Can use strictly concave functions and define multiple optimization problems for the same QoS problem &
• Dynamically choose a different optimization problem when oversubscribed
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No Over-Subscription Case: Auxiliary Problem
Let i.e. flow i: two virtual sub-flows on same path
Think of a modified network with modified link capacities
Provide proportional fairness on residual
network capacity iell xCC
~
Capacity, C1
xie
Capacity,
ieiip xxx
lC~
N~
N~
N
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Handling both under- and over-subscription…
• For ai, xi: (primary problem)
Exp. Min Rate:Effective when:
ai < Ai
lQq ll ,
lcx lIi
ie
l
,
Weighted fairnessEffective when: ai = Ai
lQq ll ,
• For aip, xip: (auxiliary problem)
If under-subscribed, solve the aux-problem; and the primary problem is automatically solved (note: aip = constant)
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Accumulation: Per-flow Backlog in a Series of Routers
J
j
J
jkkiji dtqta
1
1
)()(
11,)()( 1, Jjitdt jijij Assuming: Define accumulation:
which is a time-shifted, distributed sum of buffered bits of flow i at all routers 1 through J
Note: accumulation is an abstract dynamical concept. Vegas and Monaco attempt to provide estimators for accumulation.
1 j j+1 J
μij Λi,j+1
dj
fi
Λiμi
ingress egress
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Accumulation: Definition & Physical Meaning
1 j j+1 J
μi
j
Λi,j+1
djfi
Λ
i
μi
… …
16time
)(1f
ii dtq )(
1
J
jkkij dtq
)(tqiJ
1 j j+1 J
jd 1Jd
),( tdtI fii
)(tai
)( ttai
),( ttOi
fid
t
J
j
J
jkkiji dtqta
1
1
)()(
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Accumulation-based Control Policy1 j j+1 J
μij Λi,j+1
dj
fi
Λiμi
0)( * i
atai control objective : keep
if goal , no way to probe increase of available b/w;0)( tai
ttttdtttarecall
thenataif
thenataif
if
iii
ii
ii
i
i
)],(),([),(:
)(
)(*
*
control algorithm :
0,0)0(,
))(()( *
kfonlyfwhere
atafktw iii
Example control algorithm :
Shivkumar KalyanaramanRensselaer Polytechnic Institute
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Monaco: Robust Accumulation Estimation
1 j j+1 J
μij Λi,j+1
dj
fi
Λiμi
… …
time
)(1f
ii dtq )(
1
J
jkkij dtq
)(tqiJ
1 j j+1 J
jd 1Jd
)(taq im out-of-band
in-band ctrl pkt
),,(1
J
jkkq dtjit
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Monaco Accumulation Estimator
1 jf Jf
μij Λi,j+1
djf
fi
Λiμi
Jb jb+1 jb 1djb ctrl
data
jf+1
ctrl
out-of-bd ctrl
in-band ctrl,data pkt
classifier fifo
Interior routers provide two priority fifo queues :
1) high priority queue for out-of-band control packet
2) low priority queue for in-band control packet and data packet
Can be done w/ IP precedence
on existing routers in Internet!!
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ACC: Fairness & Linux Implementation Results
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“Accumulation”: Steering Parameter Accumulation-based congestion control (ACC) is a nonlinear optimization, where
user i maximizes: Ui(xi) = ai lnxi
Accumulation (ai) is the weight (wi) of the weighted prop. fair allocation Accumulation is hence a “steering” parameter:
Equilibrium accumulation allocation => Equilibrium rate allocation! Dynamics of CC scheme decoupled from equilibrium spec (unlike AIMD)
Accumulation has a physical meaning: sum of buffered bits of the flow in the path Accumulation is related to the lagrange multiplier, I.e., ai = pl
For two flows i,k sharing the same path, ai / ak = xi / xk FIFO queues => arrival order decides departure order => buffer occupancy decides rate allocation
q1
q2 x1x2
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Recall: Handling both under- and over-subscription…
• For ai, xi: (primary problem)
Exp. Min Rate:Effective when:
ai < Ai
lQq ll ,
lcx lIi
ie
l
,
Weighted fairnessEffective when: ai = Ai
lQq ll ,
• For aip, xip: (auxiliary problem)
If under-subscribed, solve the aux-problem; and the primary problem is automatically solved (note: aip = constant)
Shivkumar KalyanaramanRensselaer Polytechnic Institute
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Accumulation
Control Law
Expected Min Rate (EMR) Service Building Block
iieiip dxxa )(
iii dxa
iieie dxa ix
ipx
iexdi
) - A, a - a(a - w iiipipi*max
Accumulation limit
Target
Estimated accumulationin network
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Range of Expected Minimum Rates
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Mean Queue Length for EMR
A=30KB A=3000KBRIO
No AQM
AVQ+VD
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Service Multiplexing Topology
100M100M
0,20,2
0,0
0,3
0,1
0,0
0,1
0,3
…
100M
…
3,0 ~ 3,3
…
…
2,0 ~ 2,3
…
500 webA users
500 webB users
…
1,0 ~ 1,3
1,0 ~ 1,3
…
…web B
3,0 ~ 3,3
…
…web A
2,0 ~ 2,3
1-100ms 1-100ms
- Bandwidth for all unlabelled links are 1Gbps; Delay 1ms; - AQM+VD at router R1, no AQM at other routers
1ms
34ms
67ms
100ms
R0 R1 R2 R3
30M60K
35M75K
50M60K
10M15K
2,0 has weight 3
1-100ms
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No oversubscription<m00=30,A00=60KB><m03=10,A03=15KB>
Service Multiplexing: Expected Min Rate
Moderate oversubscription<m01=35,A01=75KB>
Gross oversubscription<m02=50,A02=60KB>
Loop 02,03stop sending
Web
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Queue Lengths
Non-AQM: Q-length Q-length w/ virtual accumulation support
More details: Y.Xia et al, “Accumulation-based Congestion Control,” to appear in IEEE/ACM Transactions of Networking, early 2005.
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Talk Overview
Part 3: E2E Expected QoSPipe Abstraction:
H2H Multimedia Apps
Part 1:I E
I
EI
E
Logical FIFO
B
Expected QoSUsing Closed-loop
Techniques [Eg: MSN/Yahoo + Akamai]
Part 2:A
ED
CB
F2
21
3
1
1
2
53
5
2
BANANAS Multi-path & TE Framework: incrementallydeployable
Shivkumar KalyanaramanRensselaer Polytechnic Institute
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Part 2/3: Best-Effort Path Multiplicity
EthernetWiFi (802.11b)802.11a
USB/802.11a/b
Firewire/802.11a/b
Phone modem
InternetAS1
ISP-1
ISP-n
.
.
.
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Part 2: Multiplicity: Flows, Paths, Exits/Interfaces
Multi-paths withina routing domain
Multi-AS-paths across domainsW/o specifying intra-domain paths Multiple exits from an AS
w/o specifying intra-domain paths
Multiple E2E Flows
Multi-homed interfaces & ISP/AS peers
BANANAS: A Single Abstract Framework To Exploit These Forms of Multiplicity In the Internet and Future Networks
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Isn’t multi-path routing an old subject?
Lots of old work: Multi-path algorithms/protocols [5, 6, 7, 8]*, Internet signaling architectures [9, 10, 11, 12, 13]* Novel overlay routing methods [14, 15]* Transport-level approaches for multi-homed hosts [16,
17]*
But newer goals: Traffic engineering (reliability, availability, survivability, re-route):
Separation of traffic trunking from route selection Packet level or aggregate (micro-flow or macro-flow level)
Best-effort e2e service composition… Why hasn’t it happened after 2
decades of work?
*[5-17] In SIGCOMM Future Directions of Network Architectures (FDNA) 2003 paper
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Missing Architectural Concepts An evolutionary partial deployment strategy
Allows partial deployment, incremental upgrades Incentives: increasing value with increasing deployment Fits the connectionless nature of dominant routing protocols,
Must not require signaling (unlike ATM, MPLS etc)
Abstract enough to be applicable to other scenarios: Overlay networks, last-mile multi-hop (mesh or community)
wireless networks, ad-hoc/sensor networks…
Flexible enough to have other realizations/semantics: Different placement of functions (edge vs core) Tunneling/Label Stacking Geographic/Trajectory routing
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Connectionless TE: Questions Can we do multi-path & explicit routing ?
without signaling (I.e. in a connectionless context) without variable (and large) per-packet overhead being backward compatible with OSPF & BGP allowing incremental network upgrades
Non-Goals: Monitoring, Traffic trunking/mapping
Shortest Path MPLS…
BANANAS-TE
Signaled TE
Traffic Engineering (TE) Spectrum
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What can we learn from ATM and MPLS ? MPLS label = Path identifier at each hop
Labels is a local identifier… Signaling maps global identifiers (addresses, path
specifications) to local identifiers
Miami
Seattle
SanFrancisco(Ingress)
New York(Egress)
1321
5
120
IP 1321
IP 120
IP 0
IPLabe
l
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Global Path Identifiers? Instead of using local path identifiers (labels in MPLS),
consider the use of “global” path identifiers Constructed out of global variables a node already knows! Eg: Link/Router IP addrs, Link weights, ASNs, Area Ids, GPS location
Avoid the need for signaling to establish a mapping!
10
Miami
Seattle
9
27
SanFrancisco(Ingress)
New York(Egress)
18
1
5
4
3
5
IP
IP PathId
4
IP 36
IP 27
IP 0
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Global Path Identifier (continued)
Path = {i, w1, 1, w2, 2, …, wk, k, wk+1, … , wm, j} Sequence of globally known node IDs & Link weights Global PathID: hash of this sequence: computable w/o signaling!
Canonical method: MD5 hashing of the subsequence of nodeIDs followed by a CRC-32 to get a 32-bit hash value (MD5+CRC)
Low collision (i.e. non-uniqueness) probability
Note: Different PathID encodings have different architectural implications
i
k
j
m-11
2w1
w2
wm
Path su
ffix
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Abstract Forwarding Paradigm Forwarding table (Eg; at Node k):
[Destination Prefix, ] [Next-Hop, ] [j, ] [k+1, ]
i
k
j
m-1
1
2w1
w2
wm
Path su
ffix
Packet Header:
[j, H{k, k+1, … , m-1} ]
PathID SuffixPathID
H{k, k+1, … , m-1} H{k+1, … , m-1}
[j, H{k+1, … , m-1} ]
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BANANAS TE: Partial Deployment Only red nodes are upgraded
“Virtual hop” between upgraded nodes Black nodes compute single-shortest-path
10
Miami
Seattle
9SanFrancisco(Ingress)
New York(Egress)
28
1
5
4
30
1
IP
4
IP 27
IP 0
27
1
3
2
IP 27
IP 27
X
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Outline
Motivation: Best-effort path multiplicity
BANANAS: Data-plane
BANANAS: Control-plane
BANANAS: Mapping to BGP
Not covered: details in SIGCOMM FDNA paper
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Putting It Together: Integrated OSPF/BGP Simulation
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Multiplicity: Take-Home Message…
Multi-paths withina routing domain
Multi-AS-paths across domainsW/o specifying intra-domain paths Multiple exits from an AS
w/o specifying intra-domain paths
Multiple E2E Flows
Multi-homed interfaces & ISP/AS peers
BANANAS: A Single Abstract Framework To Exploit These Forms of Multiplicity In the Internet and Future Networks
Shivkumar KalyanaramanRensselaer Polytechnic Institute
43
Talk Overview
Part 3: E2E Expected QoSPipe Abstraction:
H2H Multimedia Apps
Part 1:I E
I
EI
E
Logical FIFO
B
Expected QoSUsing Closed-loop
Techniques [Eg: MSN/Yahoo + Akamai]
Part 2:A
ED
CB
F2
21
3
1
1
2
53
5
2
BANANAS Multi-path & TE Framework: incrementallydeployable
Shivkumar KalyanaramanRensselaer Polytechnic Institute
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Part 3: Multimedia Apps: Leverage E2E Performance Diversity
Shivkumar KalyanaramanRensselaer Polytechnic Institute
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Part 3: Introduction Motivation: Video over best-effort networks (wired/wireless)
Broadband => more access bandwidth End-to-end (E2E) => constraints due to path congestion Virtual extension of broadband access pipe E2E using
multi-paths => HOME-to-HOME (H2H) multimedia
Path Diversity: dimensions Aggregate Capacity Delay diversity Loss diversity Correlations in path performance characteristics
Key: Match inherent content diversity to path diversity
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Motivation: Internet Path Congestion limits E2E bandwidth
Internet
Server Access Link
Client Access Link
Per
form
ance
Access Link Speed
Performance Saturation (even w/ many flows/path)
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Multi-paths using Peers
Overlays or peers can provide path diversity even if multi-paths not available natively
Issue: diversity of performance (b/w, delay, loss), possible correlations…
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Path (Flow) Aggregator/ Multiplexer
Path (Flow) Aggregator/ De-multiplexer
Internet
E2E Broadband Virtual Pipe Abstraction!!
Server Access Link
Client Access Link
Per
form
ance
Access Link Speed
Smart Multi-path Capacity Aggregation (SMCA): Motivation
Performance Scaling
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Time
Lossy
Low Capacity
High Delay/Jitter
Network paths usually have:• low e2e capacity, • high latencies and • high/variable loss rates.
Single path issues: capacity, delay, loss…
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SMCA: Leverage Diversity!
Low Perceived Delay/Jitter
Low Perceived Loss
High Perceived Capacity
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Delay Diversity Unit
Loss Diversity Unit
Network
Receive Buffer
Content
SMCA: Framework
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Paths Ranked by LatencyApplication Data
Low DelayRANK
High DelayRANK
SMCA: Delay Diversity Unit
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Paths Ranked by LatencyApplication Data
Low DelayRANK
High DelayRANK
Early deadline packets mapped to low-delay paths
SMCA: Delay Diversity Unit
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Paths Ranked by LatencyTransmit Queue
Low DelayRANK
High DelayRANK
Early deadline packets (in order of rank) mapped to low-delay paths (in order of rank)
SMCA: Delay Diversity Unit
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Paths Ranked by LatencyTransmit Queue
Low DelayRANK
High DelayRANK
Late deadline packets mapped to high-delay paths…
Note: these packets leave the sender roughly at the same time as the early-deadline packets
SMCA: Delay Diversity Unit
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Paths Ranked by LatencyTransmit Queue
Low Delay
High Delay
Consider a delay-based group of paths and the associatedpackets…
SMCA: Delay Diversity Loss Diversity
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Paths Ranked by LatencyTransmit Queue
Low Delay
High Delay
Consider a delay-based group of paths and the associatedpackets…
SMCA: Delay Diversity Loss Diversity
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Paths Ranked by Loss Raten GOPs
Low LossRANK
High LossRANK
Re-rank Paths within this group based upon packet loss rates
SMCA: Loss Diversity Unit
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I
Paths Ranked by Loss Raten GOPs
Low LossRANK
High LossRANK
Enlarged View of Packets (with content labels) and Paths
P
BBPBB
SMCA: Loss Diversity Unit
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I
Paths Ranked by Loss Raten GOPs
Low LossRANK
High LossRANK
P
BBPBB
Map high priority packets (eg: I-frame packets) to low loss rate rank paths
SMCA: Loss Diversity Unit
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I
Paths Ranked by Loss Raten GOPs
Low LossRANK
High LossRANK
P
BBPBB
Continue map packets to low loss rank paths based upon priority(Eg: P-frames get the next set of loss-ranked paths)
SMCA: Loss Diversity Unit
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I
Paths Ranked by Loss Raten GOPs
Low LossRANK
High LossRANK
P
BBPBB
Lowest priority packets get high loss rate paths(within the delay-based group of paths)
SMCA: Loss Diversity Unit
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I
Paths Ranked by Loss Raten GOPs
Low LossRANK
High LossRANK
P
BBPBB
FEC (unequal FEC) for a GOP mapped within the same delay-group, but mapped to the higher loss paths
SMCA: Loss Diversity Unit + FEC
I-FEC
P-FEC
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SMCA: Performance with increasing number of Paths
Num. Of
Paths
1
2
3
4
5
PSNR (dB)
20.98
22.48
25.42
26.02
28.04
Table 1. Average PSNR Variation with Number of Paths
Background traffic
generatorBackground traffic sink
Content Source Content Sink
Performance with Path Diversity + Matching
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SMCA gains with delay diversity
Avg. Delay(ms)
SMCAPSNR(dB)
PTPSNR(dB)
OPMSPSNR(dB)
300 21.78 18.73 11.03
100 25.12 24.21 19.19
50 28.32 29.46 24.33
30 30.12 31.63 27.96
Table 3. Gains with Delay Variation
Better comparative perf. when average delay and jitter is high
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SMCA gains with loss diversity
Avg. Loss Prob.
SMCAPSNR(dB)
PTPSNR(dB)
OPMSPSNR(dB)
0.4 22.78 20.31 11.64
0.35 26.32 26.86 18.21
0.1 29.03 29.02 24.43
0.05 29.32 31.82 26.06
Table 2. Gains with Loss Variation
Better comparative perf. when avg loss and loss variation is high
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Video Results: Original Video
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Video Results: SMCA vs Simple Mapping
SMCA Multiplexing Simplistic Mapping
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Part 3: Summary Multi-path performance diversity can be leveraged E2E
Key: must be mapped to content diversity (Similar to lessons learnt from content-driven unequal FEC protection
vs uniform FEC protection)
Ideas: Map late deadline packets to high latency paths Map higher priority packets to lower loss rate paths (within a delay-
based group of paths) FEC packets sent on paths different from that of associated content
(FEC: lower priority)
Our scheme can scale to handle lots of paths Possible with p2p networks (eg: 10-100 kbps from single path, but 10s
of paths) Does not require MD coding, or high complexity optimization
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Perspective: Traditional QoS: Control/Data Planes
Internetw ork or W ANW orkstationRouter
Router
RouterW orkstation
Control Plane: Signaling + Admission Control orSLA (Contracting) + Provisioning/Traffic Engineering
Data Plane: Traffic conditioning (shaping, policing, markingetc) + Traffic Classification + Scheduling, Buffer management
Our Overlay QoS BBlocks: primarily in the data-plane.
Note: Skype, DHTs are control-plane ideas that can be combined w/ such overlay QoS
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Perspective: Overlay QoS: > best-effort
Shortest Path MPLS…
BANANAS-TE
Signaled TE
Traffic Engineering
Best Effort Leased Line…
OverQoS, Overlay QoS w/ Closed Loop Control
DPS/CSFQ, Diff-Serv
Int-Serv
QoS spectrum
Overlay QoS: * Performance Expectations (not guarantees), * Edge/end-systems take charge of QoS just like reliability/availability* Perceived QoS by diversity matching
Shivkumar KalyanaramanRensselaer Polytechnic Institute
72
Thanks !
: “shiv rpi”
Papers, PPTs, Audio talks:
Shivkumar KalyanaramanRensselaer Polytechnic Institute
73
Overall Summary
Part 3: E2E Expected QoSPipe Abstraction:
H2H Multimedia Apps
Part 1:I E
I
EI
E
Logical FIFO
B
Expected QoSUsing Closed-loopTechniques
Part 2:A
ED
CB
F2
21
3
1
1
2
53
5
2
BANANAS Multi-path & TE Framework: incrementallydeployable
Shivkumar KalyanaramanRensselaer Polytechnic Institute
74
Global Path Identifier: Key Ideas
ik j
m-11
2w1
w2
wm
IP PathId(i,j)
IP PathId(1,j)
Key ideas (take-home!): 1. Global pathids (computed from global variables) instead of local labels!2. Avoid inefficient path encoding (IP) AND 3. Avoid signaling (MPLS)4. Incrementally deployable: w/ control-plane modifications