Rade Stanojevic

Using Tuángòu to Reduce IP Transit Costs

Rade Stanojevic(Joint work with Ignacio Castro and Sergey Gorinsky)

ACM CoNext 2011

Tokyo, Japan

团购 (tuángòu) = group-buying

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Part 1: Tuángòu for IP transit

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IP transit as a service

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IP transit• Lower tier ISPs customers of

higher tier ISPs• Critical for global connectivity• Fee depends on traffic

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IP Transit costs

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Affects almost every online entity IP transit cost-reduction technologies

ISP peeringCDNMulticastP2P localizationTraffic shaping (P2P throttling)

The less traffic on transit links the lower the cost

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Simple idea:

Exploit the IP transit market properties:Price elasticity 95th-percentile pricing

CIPT: Cooperative IP transit

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purchase in group and share the costs

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IP transit billing: two key properties

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• Price elasticity

• Peak based traffic metering: 95th-percentile pricing

Submodularity of the peak: peak(f) + peak(g) ≥ peak(f+g)

Voxel pricingSource= https://www.voxel.net/ip-services(as accessed on June 2011)

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CIPT as a cooperative game

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• How to share the benefits of cooperation?• Shapley value

• ‘Fair’, exists and unique for any cooperative game• Individually rational (everyone better off in the coalition)• Hard to calculate exactly for large number of players• Monte-Carlo method for accurate estimation

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Part 2: Quantifying CIPT benefits

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From peering to transit traffic

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• ISP Transit traffic data: not available• ISP Peering traffic data: available at some IXPs • Peering traffic as a proxy for estimating transit traffic

Ttransit(t) = cTpeering(t)

• Validation using 4 medium-sized (academic) ISPs

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Obtaining peering traffic data

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2. Transform images into numeric data on peering traffic

1. Peering traffic data of 264 ISPs from 6 IXPs

Optical function/character recognition

Collection of mrtg images

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Expected cost savings in the range of 8%-56%ISP traffic volume distribution, peak-hour profile, nmb of transit providers, etc.

Per ISP savings from $1K to few $100K per yearSmallest ISPs save >90%, Largest ISPs save <10%

BreakdownAround 1/3 of the savings come from the 95th-pctThe rest comes from the elastic pricing

Check the paper for details…

Country-wide coalitions

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Small (random) coalitions provide most of total attainable gains

CIPT coalition size: low of diminishing returns

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Beyond gains sharing

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Organizational embodimentPhysical infrastructurePerformance/SLAsTraffic confidentialityTransit providers and strategic issues

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Open problems

CIPT coalition formation process? Shapley value too implicit. Simpler metrics?Tuángòu in other (price-elastic) domains?

IP transportWired/wireless accessMobile voice/txt/data

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Team-buying has potential for cutting IP transit billShapley value as a mechanism for cost sharingUnique dataset of traffic data from 264 ISPs with heterogeneous sizes and temporal properties

Conclusions

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Backup slides

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Cooperative IP Transit (CIPT)

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Tuángòu = team-buyingcoalitional arrangement for bulk buying of IP transit

CIPT gains

Per-Mbps price reduction thanks to elastic pricing and peak-based (95th-pct) metering

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Price decay vs. traffic growth

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Source= https://www.telegeography

Per-Mbps transit price decline vs. interdomain traffic growth

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Shapley value definition

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Shapley value(i) ISP i's expected marginal contribution if the players join the coalition one at a time, in a uniformly random order

N = number of playersc(S)= cost of coalition SS(π, i) = set of players arrived in the system not later than iπ = permutations of the set of players N

i’s marginal contribution

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• Monte Carlo method*• We estimate the Shapley value as the average cost contribution

over set πk of K randomly sampled arrival orders.

• Estimation accuracy• K is the knob controlling the accuracy

• We use K = 1000 to keep the error under 1%

(*) D. Liben-Nowell, A. Sharp, T. Wexler, K. Woods, “Computing Shapley Value in Cooperative Supermodular Games”, Preprint, 2010. R. Stanojevic, N. Laoutaris, P. Rodriguez, “On Economic Heavy Hitters: Shapley Value Analysis of the 95th-Percentile Pricing”, Proc. of ACM IMC 2010.

Shapley value estimation

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Peering = 35-40% of the total traffic.

Peering and transit follow very similar patterns.

Peering-transit traffic similarity

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Absolute aggregate CIPT gains grow with IXP size (in terms of billed traffic)

Relative aggregate CIPT gains decrease with IXP size

Aggregate CIPT gains

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Per-partner CIPT gains

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Absolute individual CIPT gains grow with ISP size

Relative individual CIPT gains decrease with ISP size

There are large gains for all CIPT members