INSTITUT FÜRNACHRICHTENVERMITTLUNG
UND DATENVERARBEITUNGProf. Dr.-Ing. Dr. h. c. mult. P. J. Kühn
Universität StuttgartINSTITUT FÜR
KOMMUNIKATIONSNETZEUND RECHNERSYSTEME
Prof. Dr.-Ing. Dr. h. c. mult. P. J. Kühn
Universität Stuttgart
Trends in Optical Burst Switching
A Survey of OBS Research
Christoph [email protected]
• Motivation and Introduction of OBS• Key Building Blocks and Selected Results• Trends and Viability
E1 Workshop on OBS/OPS, Stuttgart, June 2004
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
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
optical circuit switching
optical burst switching
granularity
required overprovisioning for given IP traffic pattern
switching complexity
optical packet switching
Institute of Communication Networks and Computer Engineering University of Stuttgart
OBS Scenario
...
OBS network
core node
control-channel
data-channelsOBS link
edge node
......
• Burst assembly in edge node, mostly variable length
• WDM-based transmission
• Fast optical switch
• Separation of control and data
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 schedulingcontentionresolution
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
optical bursts 10 kByte…10 MByte8 µs…8 ms at 10 Gbps
control unit
data
control
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 setupone-pass reservation
Data ACK
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 only conflicting part of burst to be dropped
➔ Optimized combination of these schemes
Contention Resolution
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
10-3
10-2
10-1
100
bu
rst
loss
pro
ba
bili
ty
0 1 2 3 4normalized basic buffer delay
10-7
10-6
10-5
10-4
10-3
10-2
10-1
100
burs
t/pac
ket l
oss
prob
abili
ty
OBS, 4 FDLsOBS, 6 FDLsOBS, 8 FDLs
0 1 2 3 4normalized basic buffer delay
10-7
10-6
10-5
10-4
10-3
10-2
10-1
100
burs
t/pac
ket l
oss
prob
abili
tyOPS, RNFOPS, MINLOPS, MING
8 FDLs
void filling, variable offset
no void filling, variable offset
Institute of Communication Networks and Computer Engineering University of Stuttgart
Quality of Service
QoS DifferentiationMechanisms
Additional QoS Offset
Preemption(Segmentation)
Intentional Dropping
QoS Scheduling of Ctrl. Packets
Resource Reservation
Requirements beyond differentiation capability
• Robustness wrt/ network scenario and traffic characteristics
• Low management complexity
• Minimal processing and signalling effort
Institute of Communication Networks and Computer Engineering University of Stuttgart
Node Design
End-to-end Signaling
SwitchingTechnology
1 100 1 10 100 1 10 100nano sec micro sec milli sec
10
SOAsMEMS
TWCs
second1 10 100
Granularity burstpacket
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
Institute of Communication Networks and Computer Engineering University of Stuttgart
…more like OPS
short: 10…100 µs, some aggregation
one-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
one-pass or end-to-end
λ conv., defl./alternative routing
➔ Viable if solutions are consistent: architecture + technology + control
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
INSTITUT FÜRNACHRICHTENVERMITTLUNG
UND DATENVERARBEITUNGProf. Dr.-Ing. Dr. h. c. mult. P. J. Kühn
Universität StuttgartINSTITUT FÜR
KOMMUNIKATIONSNETZEUND RECHNERSYSTEME
Prof. Dr.-Ing. Dr. h. c. mult. P. J. Kühn
Universität Stuttgart
Trends in Optical Burst Switching
A Survey of OBS Research
Christoph [email protected]
E1 Workshop on OBS/OPS, Stuttgart, June 2004
Institute of Communication Networks and Computer Engineering University of Stuttgart
Provisioning Scenarios
t setup t service=
BurstPacket t service
t setup
week
hour
second
millisec.
week monthsecond hourmillisec. nanosec. microsec.
Flow
t setup t service«t setup t service≤min. t setup
λ-channel
year
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
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
ControlData
one-pass reservation
source dest
t
Request
Data
Pretrans-mission delay Tp
∆2
∆1
∆3
end-to-end setup
ACK
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 Durationvoid 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 bursts
➔ complexity of JET is not prohibitive
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
100
bu
rst
loss
pro
ba
bili
ty
0 1 2 3 4normalized basic buffer delay
10-7
10-6
10-5
10-4
10-3
10-2
10-1
100
burs
t/pac
ket l
oss
prob
abili
ty
OBS, 4 FDLsOBS, 6 FDLsOBS, 8 FDLs
0 1 2 3 4normalized basic buffer delay
10-7
10-6
10-5
10-4
10-3
10-2
10-1
100
burs
t/pac
ket l
oss
prob
abili
ty
OPS, RNFOPS, MINLOPS, 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
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 0.25 0.5 0.75 1conversion ratio
10-8
10-7
10-6
10-5
10-4
10-3
10-2
10-1
100
burs
t los
s pr
obab
ility
prefer FDL
0 0.25 0.5 0.75 1conversion ratio
10-8
10-7
10-6
10-5
10-4
10-3
10-2
10-1
100
burs
t los
s pr
obab
ility
prefer conversion
4 FDL’s
no FDL
2 FDL’s
0 1 2 3 4basic buffer delay / mean burst transmission time
10-5
10-4
10-3
10-2
10-1
100
burs
t los
s pr
obab
ility
1 FDL2 FDLs3 FDLs4 FDLs
0 1 2 3 4basic buffer delay / mean burst transmission time
10-5
10-4
10-3
10-2
10-1
100
burs
t los
s pr
obab
ility
8 λs, 8 output fibers, load 0.416 λs, 4 output fibers, load 0.8
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
Institute of Communication Networks and Computer Engineering University of Stuttgart
Offset-based QoS
HP burst
LP burst higher loss
lower loss
t
0 2 4 6 8 10
QoS offset / mean transmission time
10-4
10-3
10-2
10-1
burs
t los
s pr
obab
ility
of h
igh
prio
rity
clas
s
simulationanalysis
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
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
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
• offset reduction due to processing leads to unintended differentiation
Offset-based QoS
0 2 4 6 8 10
QoS offset / mean transmission time
10-4
10-3
10-2
10-1
burs
t los
s pr
obab
ility
of h
igh
prio
rity
clas
s
simulationanalysis
neg.-exp.
lower boundary
hyperexp. CoV 2
hyperexp. CoV 4
no differentiation
uniform [0, 2]
Pareto CoV 2
0 1 2 3 4
QoS offset / mean transmission time
10-6
10-5
10-4
10-3
10-2
10-1
100
burs
t los
s pr
obab
ility
base ratio 0.01base ratio 0.1base ratio 0.5
hp last hophp first
lp last hop
lp first of 2 hops
of 2 hops
8 wavelengths, load 0.6
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