Combining multihoming with overlay routing (or, how to be a better ISP without owning a network)

Combining multihoming with overlay routing

(or, how to be a better ISP without owning a network)

Yong Zhu, Constantine Dovrolis, and Mostafa Ammar

Georgia Institute of TechnologySpeaker: Chen-Hung Yu

Basic form of Internet

Singlehoming

Multihoming & Overlay routing

OSP – Overlay Service Provider

Operates a Multihomed Overlay Network (MON)

MON node – a multihomed routerAn Internet provider that does not own a

network

MON nodes

If a MON node is multihomed with K ISPs each flow has K direct MON paths

With N MON nodes each flow increases to indirect MON paths 12 NK

Outline

IntroductionModel and Problem FormulationMON design heuristicsEvaluation and DiscussionsConclusions

MON design problem

Where to place MON nodes and how to select upstream ISPs for each nodes.

Objectives Profitable Better performance Less expensive

Revenue, cost, customer subscribe

The problem involves …

ISPsPerformance of the native networkThe location and traffic matrix of potential

customersThe OSP routing strategyPricing functionNode deployment costs

ISPs and the native network

POP p = (l, i) – the access point to ISP i at location l, P denotes the set of all POPs LOC(p) = l; ISP(p) = i We denote Il as the set of all ISPs the can be

connected from location l

Native-layer performance – matrix The entry represents the propagation RTT

from POP p to q

PPT

qp,

Estimate the matrix T

Directly measure – e.g., ping If can’t, try to find the model

Mostly depends on the physical distance between two POPs

Intradomain – “highway driving distance” Interdomain – RTT increases with the

number of AS in the route

Intradomain cases

Driving distance from

p to q

Interdomain cases

A constant depends on the # of AS hops h

MON representation

A MON node is present at POP p if the node is located at LOC(p) and connected to ISP(p)

POP selection vector

The locations of all MON nodes

Customers and OSP-preferred flows

The workload of customer u is a set of flows F(u)

A flow f = (sf, df, rf, τf)

OSP-preferred flow and OSP-preferred path

Subscribe – At least a fraction H of a customer’s traffic is in OSP-preferred flows

OSP routing strategy

Direct-Routing-First (DRF)

OSP revenues

Let be the OSP pricing functionThe total OSP revenue

Required upstream capacity at POP p

Total capacity cost

The pricing function used by the ISP at

POP p

OSP costs

Required upstream capacity at POP p

Total capacity cost

Total node deployment cost

The pricing function used by the ISP at

POP p

Cost of deploying a MON node at

location l

Problem statement

Inputs: Native network information OSP information Customer information

Determine the POP selection vector MON to maximize the profit:

Problem statement (cont.)

constraints At most N MON nodes Maximum multihoming degree

NP-hardness Reduction from the set covering problem

MON design heuristics

Two major tasks: Select up to N locations for placing MON nodes Select up to K upstream ISPs for each

deployed MON node

Present four heuristics differ in terms of their inputs

Four Heuristics

RAND CUST –

places N MON nodes at the locations with the maximum number of customers

Each selected l then selects the locally present ISPs with the maximum coverage

TRFC – uses the aggregated traffic rate that originates from all potential customers Places MON nodes at locations where “traffic heavy” customers

are located Select ISPs that receive the maximum traffic rate from

customers PERF

Performance-driven (PERF)

If there are OSP-preferred direct paths, then CUST and TRFC perform quite well.

However, when many customer flows only have indirect OSP-preferred paths, they will fail.

Associate

Evaluation

Compare the MON design heuristicsOSP profitability and performance

Depending on # of MON nodes, degree of multihoming, node deployment cost, OSP/ISP pricing ratio

Examine various OSP routing strategies

Simulation setup

Customer CUST-POPUL, CUST-UNFRM

Flow RATE-GRVTY, RATE-UNFRM

Pricing

)()(

^

rPrPR

pp

Effect of number of MON nodes

Effect of OSP routing strategy

Effect of pricing ratio

Effect of maximum multihoming degree

conclusions

Examine multihoming + overlay routing from the pragmatic perspective of an OSP

Meet the objectives – profitable 、 better performance 、 less expensive Use a performance-aware MON design heuristic Deploy nodes at “key” locations Connect each MON node to ISPs that can directly reach

traffic-heavy destination POPs Direct path > indirect path Charge less than competing native ISPs Determine good trade-off between the # of MON nodes

and multihoming degree based on the d(l)