bandwidth estimation in computer networks: measurement techniques & applications constantine...
TRANSCRIPT
![Page 1: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/1.jpg)
Bandwidth estimation in computer networks:
measurement techniques &applications
Constantine Dovrolis
College of ComputingGeorgia Institute of Technology
![Page 2: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/2.jpg)
The Internet as a “black box”
Several network properties are important for applications and transport protocols: Delay, loss rate, capacity, congestion, load, etc
But routers and switches do not provide direct feedback to end-hosts (except ICMP, also of limited use) Mostly due to scalability, policy, and simplicity reasons
![Page 3: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/3.jpg)
Can we guess what is in the black box?
End-systems can infer network state through end-to-end (e2e) measurements Without any feedback from routers Objectives: accuracy, speed, non-intrusiveness
probe packets
![Page 4: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/4.jpg)
Bandwidth estimation in packet networks
Bandwidth estimation (bwest) area: Inference of various throughput-related metrics
with end-to-end network measurements Early works:
Keshav’s packet pair method for congestion control (‘89) Bolot’s capacity measurements (’93) Carter-Crovella’s bprobe and cprobe tools (’96) Jacobson’s pathchar - per-hop capacity estimation (‘97) Melander’s TOPP method for avail-bw estimation (’00)
In last 3-5 years: Several fundamentally new estimation techniques Many research papers at prestigious conferences More than a dozen of new measurement tools Several applications of bwest methods Significant commercial interest in bwest technology
![Page 5: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/5.jpg)
Overview
This talk is an overview of the most important developments in bwest area over the last 10 years A personal bias could not be avoided..
Overview Bandwidth-related metrics Capacity estimation
Packet pairs and CapProbe technique Available bandwidth estimation
Iterative probing: Pathload, Pathvar and PathChirp Comparison of tools
Applications SOcket Buffer Auto-Sizing (SOBAS)
![Page 6: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/6.jpg)
Network bandwidth metrics
Ravi S.Prasad, Marg Murray, K.C. Claffy, Constantine Dovrolis,
“Bandwidth Estimation: Metrics, Measurement Techniques, and Tools,”
IEEE Network, November/December 2003.
![Page 7: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/7.jpg)
Capacity definition Maximum possible end-to-end throughput at IP layer
In the absence of any cross traffic Achievable with maximum-sized packets
If Ci is capacity of link i, end-to-end capacity C defined as:
Capacity determined by narrow link
iHiCC
,...,1min
![Page 8: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/8.jpg)
Available bandwidth definition Per-hop average avail-bw:
Ai = Ci (1-ui)
ui: average utilization A.k.a. residual capacity
End-to-end avg avail-bw A:
Determined by tight link ISPs measure per-hop avail-
bw passively (router counters, MRTG graphs)
iHiAA
,...,1min
![Page 9: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/9.jpg)
The avail-bw as a random process
Instantaneous utilization ui(t): either 0 or 1 Link utilization in (t, t+)
Averaging timescale: Available bandwidth in (t, t+)
End-to-end available bandwidth in (t, t+)
t
t
ii dttuttu )(1
),(
)],(1[),( ttuCttA iii
),(min),(,...,1
ttAttA iHi
![Page 10: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/10.jpg)
Available bandwidth distribution Avail-bw has significant variability
Need to estimate second-order moments, or even better, the avail-bw distribution
Variability depends on averaging timescale Larger timescale, lower variance
Distribution is Gaussian-like, if >100-200 msec and with sufficient flow multiplexing
![Page 11: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/11.jpg)
E2E Capacity estimation (a’):
Packet pair technique
Constantine Dovrolis, Parmesh Ramanathan, David Moore,
“Packet Dispersion Techniques and Capacity Estimation,”
In IEEE/ACM Transactions on Networking, Dec 2004.
![Page 12: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/12.jpg)
Packet pair dispersion Packet Pair (P-P) technique
Originally, due to Jacobson & Keshav
Send two equal-sized packets back-to-back Packet size: L Packet trx time at link i:
L/Ci
P-P dispersion: time interval between last bit of two packets
Without any cross traffic, the dispersion at receiver is determined by narrow link:
iinout C
L,max
C
L
C
L
iHi
R
,...,1max
![Page 13: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/13.jpg)
Cross traffic interference Cross traffic packets can affect P-P dispersion
P-P expansion: capacity underestimation P-P compression: capacity overestimation
Noise in P-P distribution depends on cross traffic load Example: Internet path with 1Mbps capacity
![Page 14: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/14.jpg)
Multimodal packet pair distribution Typically, P-P distribution includes several local modes
One of these modes (not always the strongest) is located at L/C Sub-Capacity Dispersion Range (SCDR) modes:
P-P expansion due to common cross traffic packet sizes (e.g., 40B, 1500B)
Post-Narrow Capacity Modes (PNCMs): P-P compression at links that follow narrow link
![Page 15: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/15.jpg)
E2E Capacity estimation (b’):
CapProbe
Rohit Kapoor, Ling-Jyh Chen, Li Lao, Mario Gerla, M. Y. Sanadidi,
"CapProbe: A Simple and Accurate Capacity Estimation Technique,"
ACM SIGCOMM 2004
![Page 16: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/16.jpg)
Compression of p-p dispersion First packet queueing => compressed dispersion
Capacity overestimation
![Page 17: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/17.jpg)
Expansion of p-p dispersion Second packet queueing => expansion of dispersion
Capacity underestimation
![Page 18: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/18.jpg)
CapProbe Both expansion and compression are result queuing delays Key insight: packet pair that sees zero queueing delay would yield exact estimate
measure RTT for each probing packet of packet pair estimate capacity from pair with minimum RTT-sum
CapacityCapacity
![Page 19: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/19.jpg)
Measurements - Internet, Internet2
To
UCLA-2 UCLA-3 UA NTNU
Time Capacity Time Capacity Time Capacity Time Capacity
CapProbe
0’03 5.5 0’01 96 0’02 98 0’07 97
0’03 5.6 0’01 97 0’04 79 0’07 97
0’03 5.5 0’02 97 0’17 83 0’22 97
Pathrate
6’10 5.6 0’16 98 5’19 86 0’29 97
6’14 5.4 0’16 98 5’20 88 0’25 97
6’5 5.7 0’16 98 5’18 133 0’25 97
Pathchar
21’12 4.0 22’49 18 3 hr 34 3 hr 34
20’43 3.9 27’41 18 3 hr 34 3 hr 35
21.18 4.0 29’47 18 3 hr 30 3 hr 35
• CapProbe implemented using PING packets, sent in pairs
![Page 20: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/20.jpg)
Avail-bw estimation (a’):
Pathload
Manish Jain, Constantine Dovrolis,“End-to-End Available Bandwidth:
Measurement Methodology, Dynamics, and Relation with TCP Throughput,”
IEEE/ACM Transactions on Networking, Aug 2003.
![Page 21: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/21.jpg)
Probing methodology
Sender transmits periodic packet stream of rate R K packets, packet size L, interarrival T = L/R
Receiver measures One-Way Delay (OWD) for each packet D(k) = tarv(k) - tsnd(k) OWD variations: Δ(k) = D(k+1) – D(k)
Independent of clock offset between sender/receiver
With stationary & fluid-modeled cross traffic: If R > A, then Δ(k) > 0 for all k Else, Δ(k) = 0 for all k
![Page 22: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/22.jpg)
Self-loading periodic streams
Increasing OWDs means R>A Almost constant OWDs means R<A
![Page 23: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/23.jpg)
Example of OWD variations 12-hop path from U-Delaware to U-Oregon
K=100 packets, A=74Mbps, T=100μsec Rleft = 97Mbps, Rright=34Mbps
Ri
![Page 24: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/24.jpg)
Iterative probing and Pathload
Iterative probing in Pathload: Send multiple probing streams (duration τ) at various rates Each stream samples path at different time interval Outcome of each stream is either Ri > A or Ri < A Estimate upper and lower bound for avail-bw variation range
Avail-bw time series from NLANR trace
![Page 25: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/25.jpg)
Avail-bw estimation (b’): percentile estimation
Manish Jain, Constantine Dovrolis,“End-to-End Estimation of the Available
Bandwidth Variation Range,”ACM SIGMETRICS 2005
![Page 26: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/26.jpg)
Avail-bw distribution and percentiles
Avail-bw random process, in timescale A(t) Assume stationarity
Marginal distribution of A:
F(R) = Prob [A ≤ R]
Ap :pth percentile of A, such that p = F(Ap)
Objective: Estimate variation range [AL, AH] for given averaging timescale ALand AH are pL and pH percentiles of A
Typically, pL =0.10 and pH =0.90
![Page 27: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/27.jpg)
Percentile sampling
Question : Which percentile of A corresponds to rate R?
Given R and estimate F(R)
Assume that F(R) is inversible Sender transmits periodic packet stream of rate R
Length of stream = measurement timescale Receiver classifies stream as
Type-G if A ≤ R: I(R)= 1 with probability F(R)
Type-L otherwise: I(R)= 0 with probability 1-F(R) Collect N samples of I(R) by sending N streams of rate R
Number of type-G streams: I(R,N)= Σi Ii(R)
E[ I(R,N) ] = F(R) * N Percentile rank of probing rate R: F(R) = E[I(R,N)] / N
Use I(R,N)/N as estimator of F(R)
![Page 28: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/28.jpg)
Non-parametric estimation
Does not assume specific avail-bw distribution Iterative algorithm
Stationarity requirement across iterations N-th iteration: probing rate Rn
Use percentile sampling to estimate percentile rank of Rn
To estimate the upper percentile AH with pH = F(AH): fn = I(Rn,N)/N If fn is between pH±report AH = Rn
Otherwise, If fn > pH + , set Rn+1 < Rn
If fn < pH - , set Rn+1 > Rn
Similarly, estimate the lower percentile AL
![Page 29: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/29.jpg)
Validation example Verification using real Internet traffic traces
b=0.05 b=0.15
Non-parametric estimator tracks variation range within 10-20% Selection of b depends on traffic
Traffic spikes/dips may not be detected if b is too small But larger b causes larger MSRE
![Page 30: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/30.jpg)
Parametric estimation Assume Gaussian avail-bw distribution
Justified assumption for large degree of traffic multiplexing And/or for long averaging timescale (>200msec)
Gaussian distribution completely specified by Mean and standard deviation
pth percentile of Gaussian distribution Ap = + -1(p)
Sender transmits N probing streams of rates R1 and R2 Receiver determines percentiles ranks corresponding to R1 and R2 and can be then estimated by solving
R1 = + -1(p1) R2 = + -1(p2)
Variation range is then calculated from: AH = + -1(pH) AL = + -1(pL)
![Page 31: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/31.jpg)
Validation example
Gaussian traffic non-Gaussian traffic
Verification using real Internet traffic traces Evaluated with both Gaussian and non-Gaussian traffic
Parametric algorithm is more accurate than non-parametric algorithm, when
traffic is good match to Gaussian model in non-stationary conditions
![Page 32: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/32.jpg)
Avail-bw estimation (c’):
PathChirp
Vinay Ribeiro, Rolf Riedi, Jiri Navratil, Rich Baraniuk, Les Cottrell,
“PathChirp: Efficient Available Bandwidth Estimation,”PAM 2003
![Page 33: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/33.jpg)
Chirp Packet Trains
Exponentially decrease packet spacing within packet train
Wide range of probing rates
Efficient: few packets 100Mbps-1 packets, 134.1
![Page 34: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/34.jpg)
Chirps vs. CBR Trains
Multiple rates in each chirping train Allows one estimate per-chirp Potentially more efficient estimation
![Page 35: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/35.jpg)
CBR Cross-Traffic Scenario
Point of onset of increase in queuing delay gives available bandwidth
![Page 36: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/36.jpg)
Comparison with Pathload
• 100Mbps links• pathChirp uses 10
times fewer bytes for comparable accuracy
Available bandwidth
Efficiency Accuracy
pathchirp pathload pathChirp10-90%
pathloadAvg.min-max
30Mbps 0.35MB 3.9MB 19-29Mbps 16-31Mbps
50Mbps 0.75MB 5.6MB 39-48Mbps 39-52Mbps
70Mbps 0.6MB 8.6MB 54-63Mbps 63-74Mbps
![Page 37: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/37.jpg)
Experimental comparison of avail-bw estimation tools
Alok Shriram, Marg Murray, Young Hyun, Nevil Brownlee, Andre Broido, k claffy,
”Comparison of Public End-to-End Bandwidth Estimation Tools on High-Speed
Links,”PAM 2005
![Page 38: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/38.jpg)
Testbed topology
![Page 39: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/39.jpg)
Accuracy comparison - SmartBitsDirection 1, Measured AB
Direction 2, Measured AB
Actual AB
![Page 40: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/40.jpg)
Accuracy comparison - TCPreplayActual Available Bandwidth
Measured Available Bandwidth
![Page 41: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/41.jpg)
What’s wrong with Spruce? Spruce was proposed as fast & accurate estimation
method Strauss et al., IMC’03: example of direct probing
With a single-link model: Sender sends periodic stream with input rate Ri Receiver measures output rate Ro Avail-bw A given by:
Assumptions: Tight link capacity Ct is known Input rate Ri can be controlled by source But what would happen in a multi-hop path?
Ri can be less than source rate (due to previous link with lower capacity) and, even worse, it can be unknown!
)1( o
tit R
CRCA
![Page 42: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/42.jpg)
Measurement latency comparison
• Abing: 1.3 to 1.4 s
• Spruce: 10.9 to 11.2 s
• Pathload: 7.2 to 22.3 s
• Patchchirp: 5.4 s
• Iperf: 10.0 to 10.2 s
![Page 43: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/43.jpg)
Probing traffic comparison
![Page 44: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/44.jpg)
Applications of bandwidth estimation
![Page 45: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/45.jpg)
Applications of bandwidth estimation
Large TCP transfers and congestion control Bandwidth-delay product estimation Socket buffer sizing
Streaming multimedia Adjust encoding rate based on avail-bw
Intelligent routing systems Overlay networks and multihoming Select best path based on capacity or avail-bw
Content Distribution Networks (CDNs) Choose server based on least-loaded path
SLA and QoS verification Monitor path load and allocated capacity
End-to-end admission control Network “spectroscopy” Several more..
![Page 46: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/46.jpg)
Application-1:Improved TCP
Throughput with BwEst
Ravi S. Prasad, Manish Jain, Constantine Dovrolis,
“Socket Buffer Auto-Sizing for High-Performance Data Transfers,”
Journal of Grid Computing, 2003
![Page 47: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/47.jpg)
TCP performs poorly in fat-long paths Basic idea in TCP congestion control:
Increase congestion window until packet loss occurs Then reduce window by a factor of two (multiplicative decrease)
Consequences Increased loss-rate Avg. throughput less than
available bandwidth Large delay-variation
Example: 1Gbps path, 100msec RTT, 1500B pkts Single loss ≈ 4000 pkts
window reduction Recovery from single loss
≈ 400 sec Need very small loss
probability (ρ<2*10-8) to saturate path
![Page 48: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/48.jpg)
TCP background
Socket-layer buffers Send buffer: Bs Receive buffer: Br
Socket BufferSender Receiver
Socket Layer
TCP Layer WrWs
TCP windows Send: Ws <= Bs Congestion: Wc Receiver (advertised): Wr<=
Br Ws = min {Bs, Wc, Wr}
Ideally, TCP send window Ws should be set to connection’s bandwidth-delay product “Bandwidth”: connection’s fair share “Delay”: connection’s RTT Achieve efficiency, fairness, no congestion
![Page 49: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/49.jpg)
Can we avoid congestive losses w/o changing TCP?
Ws = min {Wc, Wr} But Wr<= S
S: rcv socket buffer size We can limit S so that connectionis limited by receive window: Ws = S Non-congested path:
Throughput R(S) = S/T(S) R(S) increases with S until it reaches MFT MFT: Maximum Feasible Throughput (onset of congestion) Objective: set S close to MFT, to avoid any losses and
stabilize TCP send window to a safe value Congested path:
Path with losses before start of target transfer Limiting S can only reduce throughput
![Page 50: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/50.jpg)
Socket buffer auto-sizing (SOBAS) Apply SOBAS only in non-congested paths Receiving application measures rcv-throughput Rrcv
When receive throughput becomes constant: Connection has reached its maximum throughput Rmax
If the send window becomes any larger, losses will occur Receiving application limits rcv socket buffer size S:
S = RTT * Rmax
With congestion unresponsive cross traffic: Rmax = A With congestion responsive cross traffic: Rmax = MFT
SOBAS does not require any changes in TCP But estimation of RTT and congestion-status requires
“ping-like” probing
![Page 51: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/51.jpg)
Measurements at WAN path
Path: GaTech to LBL,CA SOBAS flow get higher average throughput than non-
SOBAS flow because former avoids all congestion losses SOBAS does not significantly increase path’s RTT
Non-SOBAS SOBAS
![Page 52: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/52.jpg)
To conclude..
![Page 53: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/53.jpg)
Things I have not talked about Other estimation tools & techniques
Abing, netest, pipechar, STAB, pathneck, IGI/PTR, … Per-hop capacity estimation (pathchar, clink, pchar)
Other applications Bwest in new TCPs (e.g., TCP Westwood, TCP FAST) Bwest in wireless nets Bwest in overlay routing Bwest in multihoming Bwest in bottleneck detection
Scalable bandwidth measurements in networks with many monitoring nodes
Practical issues Interrupt coalescence Traffic shapers Non-FIFO queues
![Page 54: Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062620/551a83e4550346761a8b51d1/html5/thumbnails/54.jpg)
Conclusions
We know how to do the following: Estimate e2e capacity & avail-bw Apply bwest in several applications
We know that we cannot do the following: Estimate per-hop capacity with VPS techniques Avoid avail-bw estimation bias when traffic is
bursty and/or path has multiple bottlenecks We do not yet know how to do the following:
Scalable bandwidth measurements Integrate probing traffic in application data
Many research questions are still open Help wanted!