TARDIS: Stable ISP Traffic Balancing in Space and Time Richard G. - - PowerPoint PPT Presentation

ISP internal network User 1 User 2 User N Transit ISP 1 Transit ISP 2 Transit ISP M

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TARDIS: Stable ISP Traffic Balancing in Space and Time

Richard G. Clegg, Raul Landa, Jo˜ ao Taveira Ara´ ujo, Eleni Mykoniati,David Griffin, Miguel Rio

• Dept. of Electronic and Electrical Engineering, University College London, London, UK

July 2012

Introduction

Problem statement (vague)

Given an eyeball ISP with a number of possible exits to its network (with different billing schemes) and applications which have some flexibility to move their traffic, how should the ISP direct the application to reduce its bill? The system should allow:

• Traffic to be shifted in space – moved to different transit or

local links to reduce costs.

• Traffic to be shifted in time – delayed by several hours

(under the user/application’s control) to reduce cost.

• The costs must be set at correct values and the resulting

traffic shifts must take place in a stable manner.

Simplified system diagram

ISP internal network User 1 User 2 User N Transit ISP 1 Transit ISP 2 Transit ISP M

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N users send traffic to M transit links (note that link may be a proxy for different billing schemes on same link or it may represent a local link with zero or fixed monthly cost).

Shifting traffic in time and space

Time shifting of traffic

Allow users to delay traffic (for example to overnight) in order to smooth ISP traffic patterns and reduce the 95th percentile bill. Existing research includes “Good things come to those who (can) wait”, “Time-Dependent Internet pricing” and others.

Space shifting of traffic

Allows traffic to be diverted to other destinations to reduce ISP transit costs. Existing research includes ALTO/P4P , ONO “Taming the torrent: a practical approach to reducing cross-ISP traffic in peer-to-peer systems”, “Content aware traffic engineering” and many others.

The solution: TARDIS

TARDIS: Traffic Assignment and Retiming Dynamics with Inherrent Stability (moves traffic in time and space).

95th percentile billing – an opportunity

Time of day Traffic level

• Split total traffic in billing period T into smaller periods t

with traffic f1, f2, . . ..

• Discard the 5% highest fi and choose the next one – this is

f (95). The total charge is $pf (95) where p is the stated rate.

• Clearly there is a gain to be made by moving traffic in time

(and space) but how should this be done?

Aside – what traffic can swap and how?

• Space swap: Traffic to CDNs and large providers is at

multiple locations. (“Content Aware traffic engineering” estimates this is 40% of all traffic). ISPs could transparently change the used CDN node.

• Space swap: P2P systems contribute a respectable

proportion of traffic. If users were willing to install the software interfaces like CINA and ALTO could inform them which peers to choose.

• Space swap: Click hosts have a large traffic share hosted

in several networks. ISPs could transparently reroute or users could install software which selected the best.

• Time swap: ISPs could retime transfers between CDN

nodes and data centers (they probably do this already).

• Time swap: Users could be incentivised to delay long

downloads to overnight (Internet Post Office).

Problem statement (more specific)

Pricing problem

Given several links and existing traffic profiles for them, how can an effective price for each slot be created which reflects the pricing scheme and traffic profile.

Assingment problem

Given prices for each slot, reassign traffic (in a way compatible with user ability to choose between slots) to reduce prices.

The pricing solution: Shapley gradient

• The Shapley value is a commonly used notion in game

theory.

• It formalises the notion of assessing a single user’s

contribution to a non-additive score.

• Here we adapt this to the Shapley gradient.
• This assesses the change to price a user makes by adding

traffic in a single slot.

• It allows a comparison between different pricing schemes

at different times.

• Works for schemes other than “linear” and “95th

percentile”.

Solving the assignment problem

• It is often thought that setting a price “solves” the problem.
• Here though, prices alone are not sufficiently informative.
• It may be that two “slots” are only equally priced with an

unequal traffic split.

• The road traffic concept of Wardrop equilibrium is used as

a target for assigment.

• We use the concept of traffic splits within “choice sets”.
• A dynamical system based upon adjusting splits is created.
• This is shown to be Lyapunov stable under modest

assumptions.

Data used

Japanese data set

Data is from MAWI data set. It is derived from full non anonymous IP packets. 10,000 users (inside network). Only a three continuous days of data available. Start and end IP addresses known. No prices for links known.

European data set

Data is from a European ISP . 40,000 users and seven full days

• f traffic. No mapping to the outgoing transit links (or knowledge
• f the nature of these links). No prices for links known.

Test framework (diagram)

Modelling assumptions

• Need to assume how traffic originally maps to egresses.
• Need to assume a proportion of users who can-and-will

time swap and who can-and-will space swap – for modelling simplicity willingness and ability are rolled into a single variable.

• Need to assume how users will respond to “split

percentages” (all or nothing vs proportional)

• Need to define prices on links – try HIGH variation (25:5:1)

LOW variation (4:2:1) EQUAL prices (1:1:1).

• Need to define maximum delay for time swap (12 hours, 18

hours, 24 hours).

EU space but no time swapping

0.6 0.7 0.8 0.9 1 1.1 1.2 10 20 30 40 50 60 70 80 90 100 Day cost as proportion of first day Time (days) 20% link swappers 40% link swappers 60% link swappers 80% link swappers 100% link swappers

MAWI data, space but no time swapping

0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 10 20 30 40 50 60 70 80 90 100 Day cost as proportion of first iteration cost Time (days) 20% link swappers 40% link swappers 60% link swappers 80% link swappers 100% link swappers

EU data, time but no space swapping

0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 10 20 30 40 50 60 70 80 90 100 Day cost as proportion of first day Time (days) 20% time swappers (smoothed) 40% time swappers (smoothed) 60% time swappers (smoothed) 80% time swappers (smoothed)

MAWI data, time but no space swapping

0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 10 20 30 40 50 60 70 80 90 100 Day cost as proportion of first iteration cost Time (days) 20% time swappers (smoothed) 40% time swappers (smoothed) 60% time swappers (smoothed) 80% time swappers (smoothed)

EU data, space and time swapping

0.4 0.5 0.6 0.7 0.8 0.9 1 20 40 60 80 100 Final cost (as proportion of no swap cost) Link swap percentage Time swappers 0% Time swappers 20% Time swappers 40% Time swappers 60%

Conclusions

• TARDIS: Traffic Assignment and Retiming Dynamics with

Inherrent Stability – mathematically sound and works well in simulation under a variety of assumptions.

• The system is designed for 95th percentile pricing and

linear pricing. Could be extended to flat fee and bandwidth cap.

• In tests the system produces a stable reassignment of

traffic which reduces prices in a wide variety of circumstances.

• Link and time swapping can produce large reductions in

ISP transit bills.

• The degree of reduction depends on the exact nature of

the scheme but for many situations a good proportion of the maximum possible benefit can be extracted.

Aside – pricing in the wild

• 95th percentile still appears to be dominant for transit.
• Increasingly ISPs route traffic through IXPs which have

various pricing models (flat rate with bandwidth cap is common). This can be incorporated in our model.

• Transit ISPs sometimes unbundle traffic by destination and

charge ISPs different rates according to the destination of the traffic (e.g. national less than international).

• IXPs often have “stepped” pricing where managers choose

a connection size (e.g. 5x1GB links each charged a flat price per month). Our model could not handle this but could handle keeping traffic below a given utilisation for the link capacity chosen.

MAWI data geographical split (incoming)

5 10 15 20 25 30 10 20 30 40 50 60 70 80 Traffic GBytes/hour Time (hours) Japan/China inbound US inbound Other destinations inbound

EU data peak split 2 hours (incoming)

10 20 30 40 50 60 20 40 60 80 100 120 140 160 180 Traffic GBytes/hour Time (hours) Inbound link 1 Inbound link 2 Inbound link 3

MAWI data, space and time swapping

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 20 40 60 80 100 Final cost (as proportion of no swap cost) Link swap percentage Time swappers 0% Time swappers 20% Time swappers 40% Time swappers 60%

EU data: Changing the price variance (1:1:1) (1:2:4) (1:5:25)

0.2 0.4 0.6 0.8 1 1.2 1.4 10 20 30 40 50 60 70 80 90 100 Day cost as proportion of first iteration cost Time (days) 40% link swappers (high cost variance) 40% link swappers (low cost variance) 40% link swappers (equal costs) 80% link swappers (high variance) 80% link swappers (low variance) 80% link swappers (equal costs)

Does all-or-nothing assignment make a difference? (EU data)

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 10 20 30 40 50 60 70 80 90 100 Day cost as proportion of first day Time (days) 40% link swappers (proportional) 40% link swappers (All or nothing) 100% link swappers (proportional) 100% link swappers (All or nothing)

MAWI data: Changing the price variance (1:1:1) (1:2:4) (1:5:25)

0.2 0.4 0.6 0.8 1 1.2 10 20 30 40 50 60 70 80 90 100 Day cost as proportion of first iteration cost Time (days) 40% link swappers (high cost variance) 40% link swappers (low cost variance) 40% link swappers (equal costs) 80% link swappers (high variance) 80% link swappers (low variance) 80% link swappers (equal costs)

Does it make a difference if “everyone swaps” (EU data)

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 10 20 30 40 50 60 70 80 90 100 Day cost as proportion of first day Time (days) 100% of users swap 40% of data 40% of users swap 100% of data 100% of users swap 80% of data 80% of users swap 100% of data

EU data (40% space swap no time swap)

300 400 500 600 700 800 900 1000 10 20 30 40 50 60 70 80 90 100 Traffic GBytes/day Time (days) TPG 1 TPG 2 TPG 3

EU data costs (40% and 80% space swap)

160 180 200 220 240 260 280 300 320 340 360 10 20 30 40 50 60 70 80 90 100 Day cost Time (days) 40% link swappers 40% link swappers (smoothed) 80% link swappers 80% link swappers (smoothed)

MAWI data costs (40% and 80% space swap)

5 10 15 20 25 30 35 40 45 50 55 10 20 30 40 50 60 70 80 90 100 Day cost Time (days) 40% link swappers 40% link swappers (smoothed) 80% link swappers 80% link swappers (smoothed)