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Available Bandwidth Estimation Manish Jain Networking and Telecom Group CoC, Georgia Tech.

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Presentation on theme: "Available Bandwidth Estimation Manish Jain Networking and Telecom Group CoC, Georgia Tech."— Presentation transcript:

1 Available Bandwidth Estimation Manish Jain Networking and Telecom Group CoC, Georgia Tech

2 8803 Class Presentation2 09/23/2003 Outline Introduction and definitions Estimation methodologies  Train of Packet Pairs(TOPP)  Self Loading Periodic Streams (SLoPS)  Packet Train Gap Model Open Issues

3 8803 Class Presentation3 09/23/2003 Varies with time u i : utilization of link i in time interval  ( 0 <= u i <= 1 ) Available bandwidth in link i: Available bandwidth in path (Avail-bw): Tight link: minimum avail-bw link Definition Available Bandwidth: unutilized capacity

4 8803 Class Presentation4 09/23/2003 Available Bandwidth:time varying metric  defines sampling/averaging timescale Average avail-bw in   Does not tell how avail-bw varies  Variation range gives more information t A(t )  T

5 8803 Class Presentation5 09/23/2003 Why do we care ? ssthresh in TCP Streaming applications SLA verification Overlay routing End-to-end admission control

6 8803 Class Presentation6 09/23/2003 Measuring per-hop available bandwidth Can be measured at each link from interface utilization data using SNMP MRTG graphs: 5-minute averages But users do not normally have access to SNMP data And MRTG graphs give only per-hop avail-bandwidth

7 8803 Class Presentation7 09/23/2003 Measuring path Available Bandwidth Blast path with UDP packets  Intrusive  Carter & Crovella: cprobe (Infocom 1996) Packet train dispersion does not measure available bandwidth (Dovrolis et.al. Infocom’01) Measure throughput of large TCP transfer  TCP throughput depends on network buffer Ribeiro et.al. : Delphi (ITC’00)  Correct estimation when queuing occurs only at single link  Assumes that cross traffic can be modelled by MWM model

8 8 A New End-to-end probing and analysis method for estimating bandwidth bottlenecks B. Melander et al, In Global Internet Symposium, 2000

9 8803 Class Presentation9 09/23/2003 Introduction In one hop: In two hop: CjCj C j+1 O j-1 OjOj M j-1 O j+1 MjMj In FCFS queue, output rate is function of input rate and cross-traffic rate C j+1 -M j > C j -M j-1

10 8803 Class Presentation10 09/23/2003 Key Idea:TOPP  o :sending rate  f: receiving rate where i is number links with different available bandwidth  For i=1   =1/C tight  1 =1-A tight /C tight Break points

11 8803 Class Presentation11 09/23/2003 Algorithm Algorithm: Send n probe pairs with a minimum rate Record receive rate at receiver Increment rate by fixed  and repeat Measure available bandwidth from the relation of o/f vs o Avail-bw and capacity of other links can be measured  if links in ascending order of avail-bw In practice, break points may be hard to identify

12 12 End-to-end Available Bandwidth: Measurement Methodology, Dynamics and Relation with TCP Throughput M. Jain and C. Dovrolis, In IEEE/ACM TON, August 2003

13 8803 Class Presentation13 09/23/2003 Key idea: SLoPS Examine One-Way Delay (OWD) variations of a fixed rate stream  Relate rate to avail-bw OWD : D i = T arrive -T = T arrive - T send + Clock_Offset(S,R) SLoPS uses relative OWDs,  D i = D i+1 – D i-1 (independent of clock offset) With a stationary & fluid model for the cross traffic, and FIFO queues: If R > min Ai, then  D i > 0 for I = 1…N Else  D i = 0 for for I = 1…N send S R R R

14 8803 Class Presentation14 09/23/2003 Illustration of SLoPS Periodic Stream: K packets, size L bytes, rate R = L/T If R>A, OWDs gradually increase due to self-loading of stream

15 8803 Class Presentation15 09/23/2003 Trend in real data For some rate R  Increasing trend in OWDs  R > Avail-bw  No trend in OWDs  R < Avail-bw

16 8803 Class Presentation16 09/23/2003 Iterative algorithm in SLoPS At sender: Send periodic stream n with rate R n At receiver: Measure OWDs D i for i=1…K At receiver: Notify sender of trend in OWDs At sender: If trend is :-  increasing (i.e. R n >A )  repeat with R n+1 < R n  non-increasing (i.e. R n R n Selection of R n+1 : Rate adjustment algorithm Terminate if R n+1 – R n <   : resolution of final estimate

17 8803 Class Presentation17 09/23/2003 If things were black and white… Grey region: Rate R not clearly greater or smaller than Avail-bw during the duration of stream  Rate R is within variation range of avail-bw

18 8803 Class Presentation18 09/23/2003 Big Picture Increasing trend  R > variation range of Avail-bw No trend  R < variation range of Avail-bw Grey trend  R inside variation range

19 8803 Class Presentation19 09/23/2003 Grey region Rate adjustment algorithm Increasing trend : R max = R(n) R(n+1) = (G max + R max) /2 Non-increasing trend: R min = R(n) R(n+1) = (G max +R min )/2 Grey region & R(n) > G max: G max = R(n) R(n+1) = (G max + R max )/2 Grey region & R(n) < G min: G min = R(n) R(n+1) = (G min + R min )/2 Terminate if: (R max – G max ) && (R min – G min ) <  R max > A R min < A G max G min Variation Range

20 8803 Class Presentation20 09/23/2003 How do we detect an increasing trend? Infer increasing trend when PCT or PDT trend  1.0

21 8803 Class Presentation21 09/23/2003 Verification approach Simulation  Multi-hop topology  Cross traffic: Exponential and Pareto interarrivals  Varying load conditions Experiment  Paths from U-Delaware to Greek universities and U-Oregon  MRTG graphs for most heavily used links in path  Compare pathload measurements with avail-bw from MRTG graph of tight link  In 5-min interval, pathload runs W times, each for q i secs 5- min average avail-bw R reported by pathload:

22 8803 Class Presentation22 09/23/2003 Verification: Simulation Effect of tight link load  Pathload range versus avail-bw during simulation (average of 50 runs)  5 Hop, C tight =10Mbps, util non-tight =.6 % Center of pathload range: good estimate of average of avail-bw

23 8803 Class Presentation23 09/23/2003 Verification: Experiment Tight link: U-Ioannina to AUTH (C=8.2Mbps),  =1Mbps

24 8803 Class Presentation24 09/23/2003 Avail-bw Variability versus stream length Relative variation index: Longer probing stream observe lower variability  However, longer streams can be more intrusive

25 8803 Class Presentation25 09/23/2003 Avail-bw variability versus traffic load Heavier link utilization leads to higher avail-bw variability

26 26 Evaluation and Characterization of Available Bandwidth Techniques N. Hu et al, JSAC, August 2003

27 8803 Class Presentation27 09/23/2003 Packet Pair Model: Single Hop Assumption: Fluid cross traffic In practice, CT is bursty  Packet train will capture average Input Case1: G o = G i – q/C < G i Case2: G o =m/C+G b GiGi GoGo GoGo q m/C t t t In single hop path Competing traffic may be inserted between packet pair Packet pair gap at receiver is function of cross traffic

28 8803 Class Presentation28 09/23/2003 Packet Train Model: Single Hop Assumption:  Only increased gap sees CT  Packet dispersion not affected by CT at post-tight link Where Total numer of probing packets = M+K+N GiGi GbGb Gi+Gi+ t t

29 8803 Class Presentation29 09/23/2003 IGI and PTR Algorithm Start by sending out packet train with minimum gap ( g B ) If !=  Send another train with increased gap Else calculate available bandwidth  IGI: Use equation  PTR: Available Bandwidth = Rate of last train measured at receiver

30 8803 Class Presentation30 09/23/2003 Summary: Single Hop Model IGI:  Need to know the capacity of tight link  Assume that tight link is same as narrow link PTR:  Same as TOPP Relation of amount of cross-traffic and dispersion  May not hold in multi-hop path

31 8803 Class Presentation31 09/23/2003 Open Issues Integrate avail-bw estimation methodology with application Use data packets in place of probe packets Implement avail-bw estimation algorithm in network interface card Allow routers to do avail-bw estimation Can we make some short-term predictions of avail- bw? High bandwidth paths Time stamping packets MTU limitations

32 8803 Class Presentation32 09/23/2003 Pathchirp Uses exponentially spaced packet train Main idea: Avail-bw > R k, if q k >= q k+1 Avail-bw < R k, otherwise  Can be used when probe packets are close enough Identify excursions: consecutive packets show increased queuing delays Per-packet avail-bw E k Final estimate: Expected value of R k


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