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Quantifying Overprovisioning vs. Class-of-Service: Informing the Net Neutrality Debate

Quantifying Overprovisioning vs. Class-of-Service: Informing the Net Neutrality Debate. Murat Yuksel (University of Nevada – Reno) yuksem@cse.unr.edu K. K. Ramakrishnan (AT&T Labs Research) kkrama@research.att.com Shiv Kalyanaraman (IBM Research India) shivkumar-k@in.ibm.com

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Quantifying Overprovisioning vs. Class-of-Service: Informing the Net Neutrality Debate

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  1. Quantifying Overprovisioning vs. Class-of-Service: Informing the Net Neutrality Debate Murat Yuksel (University of Nevada – Reno) yuksem@cse.unr.edu K. K. Ramakrishnan (AT&T Labs Research) kkrama@research.att.com Shiv Kalyanaraman (IBM Research India) shivkumar-k@in.ibm.com Joseph D. Houle (AT&T) jdhoule@att.com Rita Sadhvani (Verizon Wireless) rita.sadhvani@verizonwireless.com

  2. rate 10000 0 0 40000 80000 time rate 10000 0 0 40000 80000 time Motivation: Thick (Over-provisioned) or Thin (Engineered) Pipes? • Media-rich applications require performance guarantees: • e.g.: VoIP requires <300ms round-trip delay, <1% loss • How to respond to these application needs? • CoS approach: provide priority (i.e. higher class) to premium traffic • Classless (best-effort) service approach: over-provision the capacity • Question:How much extra capacity does the classless service require to match the performance of the higher class (premium) service in the CoS approach? [Jim Roberts et al.] Thin: How to deal with bursts/overload? And meet premium SLAs… ! Thick: Cost of overprovisioning? Can this commodity model break even?

  3. Classless Link (neutral) BE N=? Two Approaches: CoS vs. Classless CoS Link (differentiated) Prem= gD Premium Scheduling (e.g. priority) D D BE=(1-g)D BE • GIVEN: D, D and a performance target (i.e. ttarget or ptarget) • FIND:The minimum N that gives the same performance as in the premium class of the CoS case? D

  4. REC: Required Extra Capacity REC = <required neutral link capacity> - <CoS link capacity> = N - D (rate) = 100(N/D – 1) (%) How to quantify REC?

  5. Analytical Link Model: Poisson traffic • Assume: • Poisson traffic, Exponential packet lengths for traffic in each class i.e. • Premium class traffic is Poisson with g D • Best-effort class traffic is Poisson with (1-g) D • The aggregate traffic for the neutral link is also Poisson with rate D Both the performance target and the REC can be expressed in terms of two key parameters: (i) ρ – utilization, (ii) g – proportion of premium traffic.

  6. More Bursty Traffic: MMPP • MMPP = Markov-Modulated Poisson Process • Easy to do the math… • Simplest MMPP is of two states. • MMPP traffic with mean D • Traffic w/ equivalent rate to the neutral case, but w/ more burstiness. 1 2 Higher r means more bursty traffic.

  7. Simulated Link Model: DelayMMPP/M/1 model If packet size is 1KB and the CoS link is D = 1Gb/s: 5,000packets of delay = 40.1ms a=0.5, r=4 Surface color shows the performance target. REC can be quite high even for very small g and medium utilizations.

  8. Simulated Link Model: DelayMMPP/M/1 model a=0.5, r=4 Can be drawn in multiple 2-d graphs REC increases as link utilization increases REC is large even for small proportion of premium traffic

  9. Simulated Link Model: LossMMPP/M/1/K model REC for the same performance target decreases as buffer size increases The graphs are generic for various buffer sizes. An example: For a 10Mb/s link carrying 1KB packets: K = ~15pkts  25ms buffer time K = ~60pkts  100ms buffer time

  10. Simulated Link Model: LRD Traffic LOSS – LRD/D/1/K DELAY – LRD/D/1 • Internet traffic : known to be LRD with Hurst parameter value between 0.7 and 0.9. • REC for Hurst=0.75 is significantly higher than our 2-state MMPP model results. • We also observed that REC increases as Hurst value increases towards 0.9. • Also looked at closed-loop traffic - many TCP flows - and observed similar trends. • We further looked at the case when Premium traffic is CBR and BE is TCP, and this increased REC further.

  11. Network Model • Steps to calculate network REC (NREC): • Step 1: Construct the routing matrix RFxL based on shortest path • Run Dijkstra’s algorithm on the topology matrices ANxN and WNxN • Step 2: Form the traffic vectorFx1 from TNxN • Step 3: Calculate the traffic load on each link: RT = Q • Step 4: Check the feasibility of the traffic load and routing • For any link • If link capacity is less than the traffic load (e.g. C < Q) then update T accordingly and go to Step 2. • Step 5: Calculate the required per-link REC (i.e. N - D) by using QI as the traffic rate D for Ith link, and the performance goal ptarget or ttarget. Used Rocketfuel topologies for ANxN and WNxN. Used gravity model for TNxN. Made a look-up to the simulated link model results.

  12. NREC: Two ways to calculate • Steps to calculate network REC (NREC) (cont’d): • Step 6: Calculate the NREC by averaging the per-link RECs from Step 5. • We calculated NRECs for the Rocketfuel topologies: • Used the MMPP link model (a=0.5 and r=4) or the LRD link model (H=0.75) – Much more conservative than real or TCP traffic • Assumed K=100ms buffer time • Only report Sprintlink, as the other topologies gave higher REC values total extra capacity needed on the whole network average extra capacity needed on each link

  13. NREC for Sprintlink: G2G Delay NREC can be much higher than 100% for a network operating with 60% utilization. 10ms queueing delay target for VoIP may require large REC values. Solid lines are NRECI and dashed lines are NRECA

  14. NREC for Sprintlink: G2G Loss NREC can be much higher than 1000% even for a network operating with 40% utilization. 0.1% loss target may require large REC values. Solid lines are NRECI and dashed lines are NRECA

  15. Summary • A framework to study REC for delay or loss being the performance target. • Link model • REC grows when: • traffic becomes more bursty • the utilization of the CoS link becomes higher • the performance target becomes tighter • the fraction g of the Premium class traffic becomes smaller • Closed-loop (e.g., TCP) or LRD traffic further increases REC • Network model: • For legacy g2g performance targets, REC ranges from 50% to over 100% as g reduces below 0.5 and the utilization goes up to 60%. • Future trends/work: • The performance targets will keep becoming tighter. REC is high perpetually – not just today, but in future also.. • The value of g is a crucial factor. Small g does not necessary favor a classless network. • Further research should estimate the actual costs of CoS and classless designs, as scheduling & management complexity need to be considered.

  16. THE END Thank you!

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