High-Fidelity Switch Models
for SDN Emulation
Danny Y. Huang
Kenneth Yocum
Alex C. Snoeren
University of California, San Diego
Buying OpenFlow Switches
Clients
HP Procurve
Fulcrum Monaco
Which one
to buy?
Quanta LB4G
2
Buying OpenFlow Switches
Clients
30% queries
timed out
3
Buying OpenFlow Switches
Clients
4
Buying OpenFlow Switches
Clients
Same topology.
Same workload.
Different performance!
5
Buying OpenFlow Switches
Performance Variations
Clients
x 1,000 =
How to predict
the performance?
Which one to buy?
6
Emulating OpenFlow Networks
OVS itself
would also
introduce Simulators / Emulators
performance
variations.
Data Traffic
Data
Plane
Figure 2: Query completion times for Redis clients.
Mininet
OpenFlow
Controller
Open vSwitch
Control
Plane
account for individual switch artifactsto accurately predict application performance. Section 3 discusses our methodology for quantifying this effect and reproducing it in an OVS-based emulator.
2.2 Impact of flow table design
7
F
c
Problem
Goal
Hard to predict OpenFlow
network’s performance:
To predict performance
with realism:
• OpenFlow Switches are
different.
• Existing emulation
framework is not good
enough.
• To design an emulator
that captures vendor
variations.
• To measure these
variations in the control
plane.
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Variations across Vendors
Differences in Control Plane
Controller
Flow table size
Flow management policies
OpenFlow
Protocol
Switch CPU
etc
Focus on CPU’s effect on
control-path delays.
Data Traffic
9
Control-Path Delays
Controller (POX)
Packet-in
Events
Ingress
Delay
Flow-mod
Events
CPU
Disproportionately
affects short flows.
Egress
Delay
TCAM
Ingress
Egress
Hardware
OpenFlow
Switch
Data plane traffic
Application Workload
10
Measure Control-Path Delays
Installs rules for
every new flow
Server
Controller Eth 0
Control Plane
HP Procurve
OF Switch
Fulcrum Monaco
Eth 1
Clients Eth 2
Data Plane
Quanta LB4G
Open vSwitch (OVS)
Queries for a 64-byte value every 50 ms.
We measure the query time, ingress & egress delays.
11
Measure Redis Query Times
Query completion times for Redis clients (ms)
12
Measure Ingress Delay
OVS faster than
the others.
13
Measure Egress Delay
OVS: almost
no delays!
Slow down the ingress
and egress delays on OVS
to emulate the hardware
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Implementing the Emulator
Controller (POX)
To slow down
control traffic
Packet-in Events
Flow-mod Events
Open vSwitch (OVS)
Ingress
Egress
Data plane traffic
Application Workload
15
Emulator Proxy
Controller (POX)
Packet-in Events
(Delayed)
Flow-mod Events
Physical OF Switch
Emulator Proxy
Packet-in Events
Flow-mod Events
(Delayed)
≈
Open vSwitch (OVS)
Ingress
Egress
Data plane traffic
Application Workload
16
Evaluation
HP
CDF
OVS
Query completion time for Redis clients (ms)
Hardware
Emulated
17
Evaluation
• We emulate the ingress and
egress delays only.
HP • Reasonble approximation.
CDF
Monaco
Query completion time for Redis clients (ms)
Quanta
Query completion time for Redis clients (ms)
OVS
Hardware
Emulated
Query completion time for Redis clients (ms)
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Summary
Future Work
• Hard to predict
performance due to
vendor variations.
• We designed an
emulator for controlpath delays.
• Simple, but achieves
reasonable
approximation.
• Increase realism.
Capture more artifacts.
• Expand workload
coverage. Automate
switch measurements.
• Capture interactions
among multiple
switches.
Acknowledgements
Marco Canini and Dan Levin (TU Berlin)
George Porter (UC San Diego)
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Thank you!
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Inverse Transform Sampling
• Goal: Emulate switch X, which introduces ingress
delay t with probability p.
• Algorithm:
1. Measure the delay distributions of OVS and X. Make
them into tables.
2. Measure how much OVS has delayed. Call this tOVS.
3. Look up tOVS from the OVS table. This returns
probability p.
4. Look up p from X’s table. This returns delay tX.
5. Introduce delay (tX - tOVS).
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Time on Emulator (ms)
Evaluation (QQ Plots)
HP
Monaco
Quanta
Query completion time on hardware switches (ms)
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Time Dilation
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