Available Bandwidth Estimation

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

2. Outline • Introduction and definitions • Estimation methodologies • Train of Packet Pairs(TOPP) • Self Loading Periodic Streams (SLoPS) • Packet Train Gap Model • Open Issues 8803 Class Presentation

3. Definition • Available Bandwidth: unutilized capacity • Varies with time • ui : utilization of link i in time interval t ( 0 <= ui <= 1 ) • Available bandwidth in link i: • Available bandwidth in path (Avail-bw): • Tight link: minimum avail-bw link 8803 Class Presentation

4. Available Bandwidth:time varying metric t A(t) T t • t defines sampling/averaging timescale • Average avail-bw in t • Does not tell how avail-bw varies • Variation range gives more information 8803 Class Presentation

5. Why do we care ? • ssthresh in TCP • Streaming applications • SLA verification • Overlay routing • End-to-end admission control 8803 Class Presentation

6. 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 8803 Class Presentation

7. 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 8803 Class Presentation

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

9. Introduction • In FCFS queue, output rate is function of input rate and cross-traffic rate Oj Oj-1 Oj+1 Cj+1 Cj Cj+1-Mj > Cj-Mj-1 Mj Mj-1 • In one hop: • In two hop: 8803 Class Presentation

10. Key Idea:TOPP • o :sending rate • f: receiving rate • where i is number links with different available bandwidth • For i=1 • b1=1/Ctight • a1=1-Atight/Ctight Break points 8803 Class Presentation

11. Algorithm • Algorithm: • Send n probe pairs with a minimum rate • Record receive rate at receiver • Increment rate by fixed d 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 8803 Class Presentation

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. Key idea: SLoPS R R R S send • Examine One-Way Delay (OWD) variations of a fixed rate stream • Relate rate to avail-bw • OWD: Di = Tarrive-T= Tarrive - Tsend + Clock_Offset(S,R) • SLoPS uses relative OWDs, DDi = Di+1 – Di-1 (independent of clock offset) • With a stationary & fluid model for the cross traffic, and FIFO queues: • If R > min Ai, then DDi > 0 for I = 1…N • Else DDi = 0 for for I = 1…N 8803 Class Presentation

14. 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 8803 Class Presentation

15. Trend in real data • For some rate R • Increasing trend in OWDs  R > Avail-bw • No trend in OWDs  R < Avail-bw 8803 Class Presentation

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

17. 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 8803 Class Presentation

18. Big Picture • Increasing trend  R > variation range of Avail-bw • No trend  R < variation range of Avail-bw • Grey trend  R inside variation range 8803 Class Presentation

19. Rmax > A Gmax Gmin Rmin < A Rate adjustment algorithm Increasing trend : Rmax = R(n) R(n+1) = (Gmax + Rmax)/2 Non-increasing trend: Rmin = R(n) R(n+1) = (Gmax +Rmin)/2 Grey region & R(n) > Gmax: Gmax = R(n) R(n+1) = (Gmax + Rmax )/2 Grey region & R(n) < Gmin: Gmin = R(n) R(n+1) = (Gmin + Rmin )/2 Grey region Variation Range Terminate if: (Rmax – Gmax) && (Rmin– Gmin) <  8803 Class Presentation

20. How do we detect an increasing trend? Infer increasing trend when PCT or PDT trend  1.0 8803 Class Presentation

21. 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 qi secs 5-min average avail-bw R reported by pathload: 8803 Class Presentation

22. Verification: Simulation • Effect of tight link load • Pathload range versus avail-bw during simulation (average of 50 runs) • 5 Hop, Ctight=10Mbps, utilnon-tight=.6 % • Center of pathload range: good estimate of average of avail-bw 8803 Class Presentation

23. Verification: Experiment • Tight link: U-Ioannina to AUTH (C=8.2Mbps), =1Mbps 8803 Class Presentation

24. Avail-bw Variability versus stream length • Relative variation index: • Longer probing stream observe lower variability • However, longer streams can be more intrusive 8803 Class Presentation

25. Avail-bw variability versus traffic load • Heavier link utilization leads to higher avail-bw variability 8803 Class Presentation

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

27. Packet Pair Model: Single Hop • In single hop path • Competing traffic may be inserted between packet pair • Packet pair gap at receiver is function of cross traffic Gi Input q Go t Case1: Go = Gi – q/C < Gi • Assumption: Fluid cross traffic • In practice, CT is bursty • Packet train will capture average Go t m/C Case2: Go=m/C+Gb t 8803 Class Presentation

28. Packet Train Model: Single Hop Gi Gb Gi+ t t Where Total numer of probing packets = M+K+N • Assumption: • Only increased gap sees CT • Packet dispersion not affected by CT at post-tight link 8803 Class Presentation

29. IGI and PTR Algorithm • Start by sending out packet train with minimum gap ( gB) • If gap@receiver != gap@sender • Send another train with increased gap • Else calculate available bandwidth • IGI: Use equation • PTR: Available Bandwidth = Rate of last train measured at receiver 8803 Class Presentation

30. 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 8803 Class Presentation

31. 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 8803 Class Presentation

32. Pathchirp • Uses exponentially spaced packet train • Main idea: • Avail-bw > Rk , if qk >= qk+1 • Avail-bw < Rk , otherwise • Can be used when probe packets are close enough • Identify excursions: consecutive packets show increased queuing delays • Per-packet avail-bw Ek • Final estimate: Expected value of Rk 8803 Class Presentation