[PPT] - Trends in Optical Burst Switching A Survey of OBS Research PowerPoint Presentation

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INSTITUT FÜR NACHRICHTENVERMITTLUNG UND DATENVERARBEITUNG

Prof. Dr.-Ing. Dr. h. c. mult. P. J. Kühn

Universität Stuttgart

INSTITUT FÜR KOMMUNIKATIONSNETZE UND RECHNERSYSTEME

Prof. Dr.-Ing. Dr. h. c. mult. P. J. Kühn

Universität Stuttgart

Trends in Optical Burst Switching

A Survey of OBS Research

Christoph Gauger gauger@ikr.uni-stuttgart.de

Motivation and Introduction of OBS
Key Building Blocks and Selected Results
Trends and Viability

E1 Workshop on OBS/OPS, Stuttgart, June 2004

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Institute of Communication Networks and Computer Engineering University of Stuttgart

Internet emerged as the global platform for communication

Exploding traffic demand
Highly dynamic and asymmetric traffic profiles

➔ flexible and efficient transport network

QoS demanding applications

➔ transport network should offer QoS WDM transport introduced as cost-efficient transport layer

Increasing discrepancy between optical transmission and

electronic switching speed ➔ keep data in optical layer

Flexible optical buffers difficult to realize

➔ avoid store-and-forward switching

No complex optical processing

➔ process control information electronically

OBS Design Rationale

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Institute of Communication Networks and Computer Engineering University of Stuttgart

OBS between packet and circuit switching
support IP traffic dynamics better than OCS
less complex optical layer than OPS

OBS Design Rationale

ptical circuit

switching

ptical burst

switching granularity required overprovisioning for given IP traffic pattern switching complexity

ptical packet

switching

SLIDE 4

Institute of Communication Networks and Computer Engineering University of Stuttgart

OBS Scenario

. . .

OBS network core node

control-channel data-channels

OBS link edge node

. . . . . .

Burst assembly in edge node,

mostly variable length

WDM-based transmission
Fast optical switch
Separation of control and data

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Institute of Communication Networks and Computer Engineering University of Stuttgart

OBS Building Blocks

Definitions Burst assembly assembly of client layer data into bursts Burst reservation end-to-end burst transmission scheme Burst scheduling assignment of resources in individual nodes Contention resolution reaction in case of burst scheduling conflict

burst reservation QoS / service differentiation

. . . . . .

burst assembly

burst scheduling contention resolution

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Institute of Communication Networks and Computer Engineering University of Stuttgart

Burst assembly triggered by time or size or both
burst arrival process
burst length distribution

➔ Strong impact on performance

Burst Assembly

. . . . . .

client layer data, e.g., IP packets today 40…1500 Byte 32 ns…1.2 µs at 10 Gbps

ptical bursts

10 kByte…10 MByte 8 µs…8 ms at 10 Gbps control unit

data control

SLIDE 7

Institute of Communication Networks and Computer Engineering University of Stuttgart

Burst reservation

small vs. large pretransmission delay
blocking in core vs. at edge

➔ Determined by application scenario: network size and burst length Burst scheduling

Huge amount of proposals for optimized resource utilization
Void-filling
Offsets produce voids ➔ void-filling algorithms

➔ complexity of void-filling is not prohibitive (2 implementations reported) ➔ performance benefit sometimes overestimated

Resource Allocation

Pretrans- mission delay Data Control

∆2 ∆1 ∆3

Offset Control

end-to-end setup

ne-pass reservation

Data ACK

SLIDE 8

Institute of Communication Networks and Computer Engineering University of Stuttgart

Burst loss possible due to bufferless statistical multiplexing
Application of OBS in high-speed metro/core networks

➔ lost data has to be retransmitted on end-to-end basis ➔ very low burst loss probability required (e.g., 10-6) ➔ Need for highly effective contention resolution

Wavelength domain

wavelength conversion

very effective as all WDM channels shared among all bursts
but: low burst loss probabilities only for

100 ≥ λs ➔ additional schemes necessary

Time domain

fiber delay lines (FDLs)

Space domain

deflection/alternative routing

Segmentation
nly conflicting part of burst to be dropped

➔ Optimized combination of these schemes

Contention Resolution

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Institute of Communication Networks and Computer Engineering University of Stuttgart

Contention Resolution

FDL buffer reservation in OBS and OPS
Different reservation strategies, early reservation with OBS
Joint work with Walter Cerroni in COST 266
FDLs like offsets lead to reservations spread over time ➔ voids
void filling can reduce this negative effects
No improvement by void filling for offset == 0 or constant

0.0 1.0 2.0 3.0 4.0 5.0

mean basic offset / mean burst transmission time

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burst loss probability 1 2 3 4 normalized basic buffer delay 10

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10 burst/packet loss probability OBS, 4 FDLs OBS, 6 FDLs OBS, 8 FDLs 1 2 3 4 normalized basic buffer delay 10

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10 burst/packet loss probability OPS, RNF OPS, MINL OPS, MING 8 FDLs

void filling, variable offset no void filling, variable offset

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Institute of Communication Networks and Computer Engineering University of Stuttgart

Quality of Service

QoS Differentiation Mechanisms Additional QoS Offset Preemption (Segmentation) Intentional Dropping QoS Scheduling

f Ctrl. Packets

Resource Reservation

Requirements beyond differentiation capability

Robustness wrt/ network scenario and traffic characteristics
Low management complexity
Minimal processing and signalling effort

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Institute of Communication Networks and Computer Engineering University of Stuttgart

Node Design

End-to-end Signaling Switching Technology

1 100 1 10 100 1 10 100

nano sec micro sec milli sec

10 SOAs MEMS TWCs

second

1 10 100

Granularity burst packet

nation metro campus world

dynamic circuit Burst Assembly edge delay joint work with HHI assumption: core rate approx. 10*access rate

Granularity determines switching technology and vice versa
switching time << mean burst duration
Delay of 80km fiber
End-to-end delay constraint: few 100ms

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Institute of Communication Networks and Computer Engineering University of Stuttgart

…more like OPS

short: 10…100 µs, some aggregation

ne-pass only

λ conv., FDL, deflection routing

Trends in OBS

Future direction of OBS

typical burst length burst reservation contention resolution …

… more like OCS

long: > 1 ms, extensive aggregation

ne-pass or end-to-end

λ conv., defl./alternative routing ➔ Viable if solutions are consistent: architecture + technology + control

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Institute of Communication Networks and Computer Engineering University of Stuttgart

OBS networks
benefit from aggregation and assembly
several architectural options available
can offer service differentiation to client layers
Technology
cost and availability of switching components still unsuitable
burst mode transmission requires changes in deployed infrastructure
Beneficial application scenario still open
core/transport vs. metro networks
intensive traffic grooming towards core ➔ benefit of dynamic network?
OBS has to fit into carriers’ world

➔ Interworking with circuit-switched photonic layer ➔ More effort towards efficiency, robustness, reduced complexity ➔ Application scenario and business model

Viability – Realization

SLIDE 14

INSTITUT FÜR NACHRICHTENVERMITTLUNG UND DATENVERARBEITUNG

Prof. Dr.-Ing. Dr. h. c. mult. P. J. Kühn

Universität Stuttgart

INSTITUT FÜR KOMMUNIKATIONSNETZE UND RECHNERSYSTEME

Prof. Dr.-Ing. Dr. h. c. mult. P. J. Kühn

Universität Stuttgart

Trends in Optical Burst Switching

A Survey of OBS Research

Christoph Gauger gauger@ikr.uni-stuttgart.de E1 Workshop on OBS/OPS, Stuttgart, June 2004

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Institute of Communication Networks and Computer Engineering University of Stuttgart

Provisioning Scenarios

t setup t service =

Burst Packet

t service t setup

week hour second millisec. week month second hour millisec. nanosec. microsec. Flow

t setup t service « t setup t service ≤

min. t setup

λ-channel

year

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Institute of Communication Networks and Computer Engineering University of Stuttgart

Burst Assembly

Can it reduce the detrimental impact of IP traffic characteristics?

Self-similarity on large time scales
early work suggested YES
recent publications prove NO for data plane
Smoothing on smaller time scales
consistent results show YES

➔ assembly really yields better performance Impact on TCP performance

In general positive due to smoothing
Assembly timer should be adapted to TCP congestion control

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Institute of Communication Networks and Computer Engineering University of Stuttgart

Burst Reservation

Burst loss at edge
Dominated by propagation delay
long-haul networks (tens of ms)

➔ Only acceptable for large bursts

Burst loss in network
Offset compensates processing
Alternative: FDL in each node

➔ Mostly independent of network size and burst length ∆2 ∆1 ∆3

Offset source dest t Control Data

ne-pass reservation

source dest t Request Data Pretrans- mission delay Tp

∆2 ∆1 ∆3

end-to-end setup

ACK

SLIDE 18

Institute of Communication Networks and Computer Engineering University of Stuttgart

Burst Scheduling

t reservation horizon t individual reservations

Reserve a Limited Duration no void filling, e.g. LAUC, Horizon Reserve a Fixed Duration void filling, e.g. LAUC-VF , JET

Huge amount of proposals for optimization
rearrangement of bursts, but: additional signalling needed
gap minimization
window-based algorithms for blocking switching matrices
Two implementations reported for ms and s b

ursts ➔ complexity of JET is not prohibitive

SLIDE 19

Institute of Communication Networks and Computer Engineering University of Stuttgart

Burst Scheduling

0.0 1.0 2.0 3.0 4.0 5.0

mean basic offset / mean burst transmission time

10

3

10

2

10

1

10

burst loss probability 1 2 3 4 normalized basic buffer delay 10

7

10

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10

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10 burst/packet loss probability OBS, 4 FDLs OBS, 6 FDLs OBS, 8 FDLs 1 2 3 4 normalized basic buffer delay 10

7

10

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5

10

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10

3

10

2

10

1

10 burst/packet loss probability OPS, RNF OPS, MINL OPS, MING 8 FDLs

void filling, variable offset no void filling, variable offset

joint work with Walter Cerroni, University of Bologna

Offsets lead to reservations spread over time ➔ voids

➔ void filling can reduce this negative effects

No improvement by void filling for offset == 0 or constant
Significant improvement only for large offset scenarios

➔ offset-based QoS scheme ➔ FDL buffer reservation

SLIDE 20

Institute of Communication Networks and Computer Engineering University of Stuttgart

Basic FDL delay in the order of few mean burst durations
Combination of FDL buffers and shared converter pools
Small conversion ratio: prefer FDL better
Large conversion ratio: prefer conversion better

Contention Resolution

0.25 0.5 0.75 1 conversion ratio 10

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10 burst loss probability prefer FDL 0.25 0.5 0.75 1 conversion ratio 10

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10 burst loss probability prefer conversion

4 FDL’s no FDL 2 FDL’s

1 2 3 4 basic buffer delay / mean burst transmission time 10

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10 burst loss probability 1 FDL 2 FDLs 3 FDLs 4 FDLs 1 2 3 4 basic buffer delay / mean burst transmission time 10

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10 burst loss probability

8 λs, 8 output fibers, load 0.4 16 λs, 4 output fibers, load 0.8

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Institute of Communication Networks and Computer Engineering University of Stuttgart

Offset-based QoS

HP burst LP burst additional QoS-offset δQoS basic-offset higher loss lower loss

t

SLIDE 22

Institute of Communication Networks and Computer Engineering University of Stuttgart

Offset-based QoS

HP burst LP burst higher loss lower loss

t

2 4 6 8 10

QoS offset / mean transmission time

10

4

10

3

10

2

10

1

burst loss probability of high priority class

simulation analysis neg.-exp.

lower boundary

hyperexp. CoV 2
hyperexp. CoV 4

no differentiation

uniform [0, 2] Pareto CoV 2

8 wavelengths, load 0.6

additional QoS-offset δQoS basic-offset

High priority class depends on low priority traffic characteristics

➔ severe restrictions on burst assembly strategies

Offset reduction due to processing leads to unintended differentiation

➔ offset-based QoS not robust in network environment

SLIDE 23

Institute of Communication Networks and Computer Engineering University of Stuttgart

Offset-based QoS

high priority burst low priority burst additional QoS-offset δQoS basic-offset more lost bursts fewer lost bursts

t

SLIDE 24

Institute of Communication Networks and Computer Engineering University of Stuttgart

QoS offset in the order of few mean burst durations
high priority class depends on low priority traffic characteristics
ffset reduction due to processing leads to unintended differentiation

Offset-based QoS

2 4 6 8 10

QoS offset / mean transmission time

10

4

10

3

10

2

10

1

burst loss probability of high priority class

simulation analysis neg.-exp.

lower boundary

hyperexp. CoV 2
hyperexp. CoV 4

no differentiation

uniform [0, 2] Pareto CoV 2

1 2 3 4

QoS offset / mean transmission time

10

6

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10

burst loss probability

base ratio 0.01 base ratio 0.1 base ratio 0.5 hp last hop hp first lp last hop lp first of 2 hops

f 2 hops

8 wavelengths, load 0.6