LIDS

MIT

Outline

• Motivation

• Simulation Study

• Scheduled OFS

• Experimental Results

• Discussion

LIDS

MIT

Optical Flow Switching Motivation

IP router IP router IP router

WDM WDM WDM

IP router IP router IP router

WDM WDM WDM

IP router IP router IP router

WDM WDM WDM

Without flow switching

Router initiated flows

End-end flows

• OFS reduces the amount of electronic processing by switching long sessions at the WDM layer

– Lower costs, reduced delays, increased switch capacity– Provide specific QoS for advanced services

LIDS

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OFS Motivation (cont)

Flow Size1KB 1MB 100MB10MB

Nu

mb

er

of F

low

s

To

t al B

y te s

Flow Size1MB 100MB10MB1KB

Optical DomainElect. Domain Optical DomainElect. Domain

-Internet displays a “heavy-tail” distribution of connections-More efficient optics => more transactions in optical domain (red line moves left)

LIDS

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Optical Flow Switching Study

• Short-duration optical connections – Access area– Wide area

• Network architecture issues– Connection setup– Route/wavelength assignment– Goal: efficient use of network resources I.e. high throughput

• Previous work: “probabilistic” approaches– Difficulty: high-arrival rate leads to high blocking probability– Problem: lack of timely network state information

• Our proposed solution: Use of timing information in network– Schedule connections– Gather timely network state information

• This demonstration– Demonstrate flow switching– Demonstrate viability of timing and scheduling connections– Investigate key sources of overhead– High efficiency

LIDS

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Connection Setup Investigation

• Key issue:– How to learn optical resource availability?– Distribution problem– “Wavelength continuity” problem makes it worse

• Previous work– Addresses issues one at a time– Assumes perfect network state information– Will these results be useful for ONRAMP, WAN implementation?

• This work– Assesses effects of distributed network state information– Models some current proposals

MP-lambda-S ASON

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Methodology

• Design distributed approaches – Combined routing, wavelength assignment– Connection setup

• Baseline flow switching architecture– Requested flows from user to user– Durations on order of seconds– All-optical

• Simulate approaches on WAN topology– End-to-end latency (“time of flight” only)– Approaches: Ideal, Tell-and-Go, Reverse Reservation

• Assess performance versus idealized approach– Blocking probability

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MIT

Ideal Approach Illustration

A C B

D

-Changers-Changers-Changers

-Changers

A C B

DBidirectionalMulti-fiber Link

Network Infrastructure

“Tell”cntl packet

LLR Routing, Connection Setup

Optical Flow

Assume: Flow Requested from A->B

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Tell-and-Go Approach Illustration

A C B

D

Link-stateUpdates Available : 1,2,3Available : 1,2

Available : 2,3 Available : 2,3,4

Link-State Protocol

A C B

D

Optical Flow

Connection Setup

“Tell” Packet - Single wavelength

Assume: Flow Requested from A->B

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Reverse Reservation Approach Illustration

A C B

D

Information Packets

A C B

D

Route Discovery

Route Chosen by B

ReservationPacket

Assume: Flow Requested from A->B

Route, Wavelength Reservation

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Simulation Description

• Results shown as Blocking Probability vs. Traffic Intensity– Uniform, Poisson flow traffic per node

• Fixed WAN topology

• Parameters:– F = Number of fibers/link– L = Number of channels/link– K = Number of routes considered for routing decisions– U = Update interval (seconds) = Average service rate for flows (flows/second) = Average arrival rate of flows (flows/second) = Traffic intensity. Equal to /

not utilization factor

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Simulation Topology

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Latency-free Control Network Results (1sec flows)

RR: F=1, L=16, K=10 TG: F=1, L=16, K=10

Title:

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Control Network With Latency Results (1sec flows)

Title:

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TG, RR: U=0.1, F=1, L=16, K=10

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MIT

Interesting Phenomenon

• Why is TG performance better than RR?– 1 sec flows and large rho => small inter-arrival times

Smaller than round trip time

– Thus, with high probability, successive flows will see same state (at least locally)

– Increases chance of collision Effect of distribution (latency)

• Why is Rand better than FF?– This is exactly opposite of analytical papers’ claim– Combination of reasons

Nodes have imperfect information FF makes them compete for same wavelengths (false advertisement)

– Not seen in analysis because distribution was ignored

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MIT

Scheduled OFS in ONRAMP

• Inaccurate information hurts performance– In this case: Simple speed of light– Biggest problem: Core network resources wasted

• Our proposal: Use of timing information to schedule flows– Deliver network information on time to make decisions– Exchange flow-based information– Maximize utilization of core network– Possible small delay for user

• Issues– Can timing be implemented cheaply, scaled?– Can schedules be implemented?– Must make use of current/future optical devices– Low cost

• ONRAMP OFS– Demonstration of scheduled OFS in access-area network– One example of an implementation

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MIT

Fixed Xponder

Tunable Xponder

Access Node #2

OXC

Router

GE GE

IPFLOW

IP

Control

Xmitter (X)

Fixed Xponder

Tunable Xponder

Access Node #1

Router

OXC

GE GE

IP FLOW

IP

Control

Intermediate Node

OXCOXC

RouterRouter

•

Receiver (R )

Fixed Xponder

Tunable Xponder

Access Node #2

OXC

GE GE

IPFLOW

IP

Control

)

Fixed Xponder

Tunable Xponder

Access Node #1

Router

OXC

GE GE

IP FLOW

IP

Control

Intermediate Node

OXCOXC

RouterRouter

X- R-

OXC Sched OXC Sched

OXC Sched

Scheduling in ONRAMP

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• Uses timeslotting and schedules for lightpaths

• X => i busy on output of node i at corresponding slot

OXC Schedule

Slot 1 …..Slot 2 Slot 3

X X

X

X

ONRAMP Connection Setup

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-Overheads includes all timing uncertainty

-Efficiency of any scheduled algorithm related to timing uncertainty, and switching/electronic overheads

-Rough efficiency = Flow duration / Flow duration + Overhead

Slot 1

Overhead - Dependent on timing uncertainty

TIMEScheduling OH

Cannot go in next timeslot

Scheduling OH

Can go in next timeslot

Slot 3Slot 2

Algorithm Timeline

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Utilizing Link Capacity

• Sending GigE over transparent optical channel– Clock rate 1.244 Ghz– Rate 8/10 coding results in raw bit rate of 995.2 Mb/s

• Payload capacity for UDP– Send MTU-sized packets

9000 bytes Avoid fragmentation

– Headers Ethernet (26 bytes) + IP (20 bytes) + UDP (8 bytes) = 54 bytes Result: 8946 bytes of payload/packet

– Link payload limit 989.2288 Mb/s

• Rate-limited UDP– Input: desired rate– Timed sends of UDP packets achieve desired rates– Demonstrates transparency of OFS channel

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MIT

Experimental Setup

• OFS implemented in lab

• One second timeslots– Timing overhead negligible

• Routing/wavelength selection– All available wavelengths (currently 14)– Both directions around ring

• Gigabit Ethernet link layer– Flows achieve theoretical maximum link rate ~989 Mb/s

• Rate limited UDP– Unidirectional flows– No packet loss (100s of flows)– Variable rate– Demonstrates transparent use of optical connection

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OFS Performance

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MIT

Current Performance Limitations

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• Current overhead is 0.10 seconds– Efficiency for one-second flows is therefore 90%– Analysis of overhead reveals possible overhead of Gigabit Ethernet

frame sync Still under investigation

– Switching overhead and timing uncertainty negligible– I.e. scheduling viable, efficient

Current Performance Limitations(cont.)F

low

Req

uest

time

Beg

in S

lot

Scheduling

Command

GBE Sync?

Receiver Laser

Switching

Algorithm Overhead Timeline

Flow begins…………

10ms 150ms100ms