models for quality of service (qos) routing and...

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Venkatesh Sarangan Computer Science Department Oklahoma State University [email protected] Donna Ghosh, Raj Acharya Dept. of Comp. Sci. & Eng. Pennsylvania State University dghosh,[email protected] Models for Quality of Service (QoS) routing and Performance analysis in a multi-class Internet

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Page 1: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

Venkatesh SaranganComputer Science Department

Oklahoma State [email protected]

Donna Ghosh, Raj AcharyaDept. of Comp. Sci. & Eng.

Pennsylvania State Universitydghosh,[email protected]

Models for Quality of Service (QoS) routing and Performance analysis in a multi-class

Internet

Page 2: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

Outline

n Introduction to QoS routingn Work done on inter-domain QoS routingn Work done on analyzing multi-class networksn Other on-going projects

Page 3: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

QoS Routing

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n Current IP routing follows the shortest path in terms of hops

Voice over IP from Source to Receiver

n 7 bw units required

n Current IP routing n Path: Src � C �Rcvn Poor quality !

n Better path exists: Src �A �B �Rcv

Voice over IP from Source to Receiver

n 7 bw units required

n Current IP routing n Path: Src � C �Rcvn Poor quality !

n Better path exists: Src �A �B �Rcv

n Need mechanisms that route a connection based on its resource requirements, rather than just the hop count

n Need QoS routing

Page 4: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

QoS Routing (contd.)

n Basic goal is to find a feasible path for a given connectionn Additionally, can optimize network resource utilization

n Routing involves two basic tasks:n Collecting network state information and keeping it up-to-

daten Using this information to compute a feasible path

n Route computation depends on how much state information is collected and where it is storedn QoS routing strategies - Source, Distributed, and

Hierarchical

Page 5: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

Hierarchical QoS Routing

n Source and distributed schemes can’t scale for large networks n Hence ‘hierarchical’ schemes

n Nodes are clustered into groups recursively creating a multi-level hierarchyn A node maintains an aggregated state information about

other groups and detailed information about its own group

n Advantages: n Can scale to large networks when compared with the

other two schemes

n Disadvantages:n How to aggregate resources concisely and accurately ?

Page 6: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

Aggregation for Hierarchical QoS routing

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n Aggregation involvesn Summarizing the domain connectivityn Summarizing the resource availability

A B

C

AB

C

Page 7: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

Resource (Bandwidth) Aggregation

n Resource aggregate is the bandwidth in the best path between a border router pair

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Existing aggregates �� ��

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More accurate aggregate

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n Does not consider a domain’s finite capacity to route traffic

Page 8: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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How to estimate the routing capacity?

n Maximum volume of traffic a domain can handle from a neighbor

n Possible estimate: the installed capacityn Indeed, the maximum that a domain can handlen May over-estimate bandwidth, if more than one neighbor

n Need a parameter thatn Does not over-estimate/under-estimate bandwidthn Varies with network traffic conditions

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Page 9: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

*

An observation

n ISP 2’s routing capacity w. r. to ISP 1 is the sum of:n ISP 1’s traffic flowing thru 2n Additional traffic that ISP 1 can

send to 2n depends on free BW available in

ISP 2

ISP 1 ISP 2

ISP 3

ISP 4

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n C12 = T12 + �C12

n T12 is maintained by ISP 1’s router connecting to 2

n �C12 is advertised by ISP 2 to 1

Page 10: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Estimating �C12

ISP 1 ISP 2

ISP 3

ISP 4

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ISP 2(flow graph)

C2i: capacity of ISP i w. r. to ISP 2

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Page 11: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

##

How to use the capacity as an aggregate ?

n ISP 1 forwards a request to ISP 2, only ifn T12 + b <= C12

n b < widest path BW

n Routing capacity is not used alonen It is not clear if there is any single path in ISP 2 with a width of

at least b

Page 12: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Routing capacity in a probabilistic setting

n Bandwidth available in a link varies with timen Can do better by modeling these fluctuations

n Model link bandwidths as random variablesn Ping the links periodically and use the time histories as the

pmfs, orn Construct the pmfs through some knowledge of the link state

updates

n Goal:n To estimate the distribution of a domain’s routing capacity

n Solve for max-flow in a deterministic graph with probabilistic edge weights

Page 13: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Max-flow with probabilistic edge weights

We approximate the min of a joint distribution by t he random variable with the lowest mean .

n pmf of max-flow = pmf of min cut (Max-flow, Min-cut theorem)n Difficult problem, exponential time in # of edges in graph

n Existing approximation algorithms have restrictive assumptions n i.i.d. edge weights

n Proposed heuristic:n Create graph G’ with deterministic edge weights – replace the

random weights with their meann Find the min cut in G’ n Find the distribution of the above cut in Gn Distribution of max-flow in G � distribution of min cut in G’

Page 14: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Goodness measures

n Bandwidth admission ratio (BAR)n Ratio of BW admitted to BW requested

n Bandwidth Over-estimation Ratio (BOR)n Ratio of BW dropped inside a domain to BW forwarded

n Bandwidth Under-estimation Ratio (BUR)n Ratio of non-forwarded BW that could have been successful to

BW forwarded

Page 15: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Methods Compared

n Det-CAR (Capacity aware routing)n Routing capacity + widest path bandwidth in a deterministic setting

n Det-nCAR (Non capacity aware routing)n Widest path bandwidth alone in a deterministic setting

n Prob-CARn Routing capacity + widest path bandwidth in a probabilistic setting

n Prob-nCARn Widest path bandwidth alone in a probabilistic setting

Page 16: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Sample results: w. r. to routing updates

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2

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0 50 100 150 200 250 300 350 400 450 500 550

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Inter-domain update interval (in sec)

Prob-CARProb-nCAR

Det-CARDet-nCAR

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0 50 100 150 200 250 300 350 400 450 500 550

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Inter-domain update interval (in sec)

Prob-CARProb-nCAR

Det-CARDet-nCAR

Page 17: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Sample results: w. r. to routing updates

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0 50 100 150 200 250 300 350 400 450 500 550

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Inter-domain update interval (in sec)

Prob-CARProb-nCAR

Det-CARDet-nCAR

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Inter-domain update interval (in sec)

Prob-CAR over Prob-nCARDet-CAR over Det-nCAR

Page 18: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Summary

n Using routing capacity as an aggregate can lead to improved routing performancen Reduces over-estimation & increases under-estimation

n Current workn Developing schemes for estimating routing capacity when

cross-traffic pattern is known

n Aggregation schemes for multicast requests

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Page 19: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Models for Performance analysis

n Connection oriented scenario such as multi-class MPLS networks

n To answer questions such as “How many LSPs can be supported in a network with capacity ‘C’ BW units ?”

n Obtain parameters that describe the network’s steady state behavior

n Utilization, LSP blocking probability, Occupancy distribution…

n Useful in design/planning, network optimization etc.

Page 20: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Multi-class MPLS Networks

n “call”: request for a bandwidth guaranteed LSPn C ≡ Network capacity assigned for calls between I and II

n Calls of various classes share this Cn Chosen model –

n Stochastic Knapsack

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Page 21: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Stochastic Knapsack

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n Class i arrivals are independent of other classes

n Class i parameters –n λi : arrival raten 1/µi : mean residence timen bi : resource requirement

of class i arrivals

n Admission Policy – Complete Sharing (FCFS)

n Multi-class loss system –blocked arrivals do not wait

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Page 22: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Stochastic Knapsack with non-Poisson arrivals

n Internet trace studies show bursty (LRD) call arrivalsn Hence, the need to solve a stochastic knapsack with bursty

arrivalsn As the first step, we obtain an heuristic approximation for

a knapsack with non-Poisson arrivals

Page 23: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Our Approach

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Page 24: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Our Approach

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Page 25: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Concepts

n Reversible Processn Reverse time � Statistical properties remain SAME

n Joint process of independent reversible processes is also reversible

Y(t): Reversible Markov process, state space S, equilibrium distribution ππππ(j), j∈∈∈∈S

X(t): Truncated Y(t), state space A ⊂⊂⊂⊂ S

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Page 26: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Proposed solution

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Page 27: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Schemes compared

n Actual system behaviorn Studied through simulations

n Poisson modeln Assume bursty arrival process ∼ Poisson with same mean

n Heuristic approachn Find the solution assuming that the per class queuing

processes with bursty arrivals are reversible

n Proposed modeln Approach discussed so far

Page 28: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Performance measures

n L1 Norm

n Average number of calls

n Average utilization

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Page 29: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Performance under high burstiness

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Page 30: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Performance under high burstiness

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Class-3 Slope parameter

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Page 31: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Summary

n A step towards solving for stochastic knapsack with bursty call arrivalsn Better than the naïve Poisson approximation

n Need to in-corporate characteristics specific for bursty arrivals

n Need to generalize for non-exponential call holding timesn Need to obtain call blocking probabilities

Page 32: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Other on-going projects…

n Ad-hoc networksn Exploring hierarchical QoS routing for MANETs

n Sensor networksn Energy conserving MAC protocols that co-ordinate their on-

off schedules depending on their neighbor’s schedulesn Topology control for energy-efficient, delay-constrained data

transfer

n IP packet trace-backn Solutions for tracing back packets over long paths

Page 33: Models for Quality of Service (QoS) routing and ...lyle.smu.edu/~rajand/EEseminars/Venkatesh_Feb05.pdf · QoS Routing (contd.) n Basic goal is to find a feasible path for a given

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Thank You!

Questions…?