tomography-based overlay network monitoring and its applications
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Tomography-based Overlay Network Monitoring and its Applications. Yan Chen. Joint work with David Bindel, Brian Chavez, Hanhee Song, and Randy H. Katz UC Berkeley. Motivation. Applications of end-to-end distance monitoring Overlay routing/location Peer-to-peer systems - PowerPoint PPT PresentationTRANSCRIPT
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Tomography-based Overlay Network Monitoring and its
Applications
Joint work with David Bindel, Brian Chavez, Hanhee Song, and Randy H. Katz
UC Berkeley
Yan Chen
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Motivation• Applications of end-to-end distance
monitoring– Overlay routing/location – Peer-to-peer systems – VPN management/provisioning– Service redirection/placement – Cache-infrastructure configuration
• Requirements for E2E monitoring system– Scalable & efficient: small amount of probing traffic– Accurate: capture congestion/failures– Incrementally deployable– Easy to use
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Existing Work• Static estimation:
– Global Network Positioning (GNP)• Dynamic monitoring
– Loss rates: RON (n2 measurement)– Latency: IDMaps, Dynamic Distance Maps, Isobar
• Latency similarity under normal conditions doesn’t imply similar losses !
• Network tomography– Focusing on inferring the characteristics of
physical links rather than E2E paths– Limited measurements -> under-constrained
system, unidentifiable links
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Problem FormulationGiven n end hosts on an overlay network and
O(n2) paths, how to select a minimal subset of paths to monitor so that the loss rates/latency of all other paths can be inferred.
• Key idea: select a basis set of k paths that completely describe all O(n2) paths (k «O(n2)) – Select and monitor k linearly independent paths to compute
the loss rates of basis set– Infer the loss rates of all other paths– Applicable for any additive metrics, like latency
End hosts
Overlay Network Operation Center
topology
measurements
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Path Matrix and Path Space
• Path loss rate p, link loss rate l
• Totally s links, path vector v
1..
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xvxvlvp Ts
j
jj
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)1log()1log(
1,}1|0{, rsr bGbGx• Path matrix G
– Put all r = O(n2) paths together
– Path loss rate vector b
A
p
B
lj
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Sample Path Matrix
• x1 - x2 unknown => cannot compute x1, x2
• Set of vectorsform null space
• To separate identifiable vs. unidentifiable components: x = xG + xN
• All E2E paths are in path space, i.e., GxN = 0
01
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xxx
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B
1
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3b1
b2
b3
(1,-1,0)
link 2
link 1
link 3
(1,1,0)
row space(measured)
null space(unmeasured)
T]011[
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1 2
1
2 3
1’
Real links (solid) and overlaypaths (dotted) going through them
Virtualization
Virtual links
1’ 2’
1
23
1’2’
4
Rank(G)=1
Rank(G)=2
Rank(G)=3
3’
4’
Intuition through Topology Virtualization
1
1 2
12
3
• Virtual links: minimal path segments whose loss rates uniquely identified
• Can fully describe all paths
• xG : similar forms as virtual links 5
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Algorithms• Select k = rank(G) linearly independent paths
to monitor– Use rank revealing decomposition, e.g., QR with
column pivoting– Leverage sparse matrix: time O(rk2) and memory
O(k2)• E.g., 10 minutes for n = 350 (r = 61075) and k = 2958
• Compute the loss rates of other paths
– Time O(k2) and memory O(k2)
GGG GxbbxGx then,with Solve
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How much measurement saved ?
• k « O(n2) ?• For power-law Internet topology, M nodes, N end
hosts– There are O(M) links and N > M/2 (with proof)– If n = O(N), overlay network has O(n) IP links, k = O(n)
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When a Small Portion of End Hosts on Overlay
• Internet has moderate hierarchical structure [TGJ+02]– If a pure hierarchical structure (tree): k = O(n)– If no hierarchy at all (worst case, clique): k = O(n2)– Internet should fall in between …
For reasonably large n, (e.g., 100), k = O(nlogn)BRITE 20K-node hierarchical topology Mercator 284K-node real router topology
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Practical Issues• Topology measurement errors tolerance
– Care about path loss rates than any interior links– Poor router alias resolution present show multiple
links for one => assign similar loss rates to all the links
– Unidentifiable routers => ignore them as virtualization
• Topology changes– Add/remove/change one path incurs O(k2) time– Topology relatively stable in order of a day =>
incremental detection
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Evaluation• Simulation
– Topology• BRITE: Barabasi-Albert, Waxman, hierarchical: 1K – 20K nodes• Real router topology from Mercator: 284K nodes
– Fraction of end hosts on the overlay: 1 - 10%– Loss rate distribution (90% links are good)
• Good link: 0-1% loss rate; bad link: 5-10% loss rates• Good link: 0-1% loss rate; bad link: 1-100% loss rates
– Loss model: • Bernouli: independent drop of packet• Gilbert: busty drop of packet
– Path loss rate simulated via transmission of 10K pkts• Metric: path loss rate estimation accuracy
– Absolute/relative errors– Lossy path inference
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Areas and Domains # of hosts
US (40)
.edu 33
.org 3
.net 2
.gov 1
.us 1
Interna-tional (11)
Europe (6)
France 1
Sweden 1
Denmark 1
Germany 1
UK 2
Asia (2)Taiwan 1
Hong Kong 1
Canada 2
Australia 1
Experiments on Planet Lab• 51 hosts, each from
different organizations– 51 × 50 = 2,550 paths
• Simultaneous loss rate measurement– 300 trials, 300 msec each– In each trial, send a 40-byte
UDP pkt to every other host• Simultaneous topology
measurement– Traceroute
• Experiments: 6/24 – 6/27– 100 experiments in peak
hours
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• Loss rate distribution
• Accuracy– On average k = 872 out of 2550– Absolute error |p – p’|:
• Average 0.0027 for all paths, 0.0058 for lossy paths– Relative error
lossrate [0, 0.05)
lossy path [0.05, 1.0] (4.1%)
[0.05, 0.1) [0.1, 0.3) [0.3, 0.5) [0.5, 1.0) 1.0
% 95.9% 15.2% 31.0% 23.9% 4.3% 25.6%
Tomography-based Overlay Monitoring Results
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Absolute and Relative Errors
• For each experiment, get its 95 percentile absolute and relative errors for estimation of 2,550 paths
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Lossy Path Inference Accuracy
• 90 out of 100 runs have coverage over 85% and false positive less than 10%
• Many caused by the 5% threshold boundary effects
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Topology Measurement Error Tolerance
• Out of 13 sets of pair-wise traceroute …• On average 248 out of 2550 paths have no
or incomplete routing information• No router aliases resolved
Conclusion: robust against topology measurement errors
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Performance Improvement with Overlay
• With single-node relay• Loss rate improvement
– Among 10,980 lossy paths:– 5,705 paths (52.0%) have loss rate reduced by 0.05 or more– 3,084 paths (28.1%) change from lossy to non-lossy
• Throughput improvement– Estimated with
– 60,320 paths (24%) with non-zero loss rate, throughput computable
– Among them, 32,939 (54.6%) paths have throughput improved, 13,734 (22.8%) paths have throughput doubled or more
• Implications: use overlay path to bypass congestion or failures
lossraterttthroughput 5.1
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SERVER
OVERLAY RELAYNODE
OVERLAY NETWORKOPERATION CENTER
CLIENT
3. Network congestion /failure
4. Detect congestion /failure
2. Register trigger
7. Skip-free streamingmedia recovery
6. Setup New Path
1. Setupconnection
5. Alert +New Overlay Path
XUC Berkeley
UC San Diego
Stanford
HP Labs
Adaptive Overlay Streaming Media
• Implemented with Winamp client and SHOUTcast server• Congestion introduced with a Packet Shaper• Skip-free playback: server buffering and rewinding• Total adaptation time < 4 seconds
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Adaptive Streaming Media Architecture
Client 1
MEDIASOURCE
SERVERSHOUTcast
Server
Buffering Layer
Clie
nt 1
Clie
nt 2
Clie
nt 3
Clie
nt 4
FromSHOUTcast
server
Calculatedconcatenation
point
BU
FFE
R
ByteCounter
Client 2
Client 3
Client 4
INTERNET
Triggering /alert + new path
OVERLAY RELAY NODE
RELAYOverlay Layer
Path Management
TCP/IP Layer
RELAY
CLIENTWinamp client
TCP/IP Layer
Overlay Layer
Internet
Path Management
Winamp Video/Audio Filter
Byte Counter
TCP/IP Layer
OVERLAY NETWORKOPERATION CENTER
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Conclusions• A tomography-based overlay network
monitoring system– Given n end hosts, characterize O(n2) paths with
a basis set of O(nlogn) paths– Selectively monitor O(nlogn) paths to compute
the loss rates of the basis set, then infer the loss rates of all other paths
• Both simulation and real Internet experiments promising
• Built adaptive overlay streaming media system on top of monitoring services– Bypass congestion/failures for smooth playback
within seconds
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