Software-Defined Networking: Hype vs. Hope · Software-Defined Networking (SDN): Hype vs. Hope...

Software-Defined Networking (SDN):

Hype vs. Hope

Inder Monga

Chief Technologist and Area Lead

HEAnet Conference 2013

Athlone, Ireland

Networking for Science +

Lawrence Berkeley National Laboratory U.S. Department of Energy | Office of Science

Outline

Energy Sciences Network

Networking for Science

Software-Defined Networking


ESnet was formed 26 years ago - 3 years after

HEAnet


26 Years as a Mission Network

• >100 Nobel Prizes

Mission of Energy Sciences Network: Accelerate research and discovery for DOE

Office of Science.

Mission of DOE Office of Science: Deliver knowledge and tools for transforming

our understanding of the universe.

$5B/year for the US National Lab

Complex, which includes:

• world's largest collection of scientific

user facilities (32) • supercomputers, accelerators, xray

/ neutron sources, electron

microscopes, sequencers, fusion

facilities, Energy Sciences Network


Embedded in a US National Laboratory

(Berkeley Lab)

11/14/2013 6

• one of 3 CS Divisions

at Berkeley Lab

• surrounded by

scientific

collaborations, large-

scale tools, Petabytes

of data, 4000

researchers / staff

• advantages of

proximity: cafeteria and

hallway conversations


Coupled with a Major Research University

11/14/2013 7

UC Berkeley is

just downhill.

• 36,000 students,

1500 faculty

• hundreds with joint

appointments at

Berkeley Lab


Our Advisory Board

Larry Smarr Jagdeep Singh David Foster Vint Cerf

Cees De Laat

Kristin Raushenbach

David Clark

Lo

g s

ca

le

From HEAnet Strategic Plan 2008-2013

Global Transfer Activities (LHC/ATLAS)


SNLL

PNNL

SNLA

BNL

AMES

LLNL

JGI

GFDL

PU Physics SUNN

10

10

100 10

10

10

1

100

100

100

10

10

10

10

10

100 10

10

10

100 100

100

100

Geographical

representation is

only approximate

1

The 100G Energy Sciences Network (Fall 2013)

Int’l

PPPL

100

100

Cle

v.

10

SUNN STAR AofA

100G testbed

SF Bay Area Chicago New York Amsterdam

AMST

U.S. R&E

peerings (many)

100

U.S. commercial

peerings 100

100

ESnet routers

site routers

100G

10-40G

1G

Metro area circuits

Site provided circuits

10

10

100

Optical only

1

100

Int’l

Int’l

100 100

Int’l

100

100 100

100 100

100

100

Capability to scale

to 13.2 Tbps


ESnet Research Testbeds

100G Testbed

• High-speed protocol research

• Available since Jan 2012

• Dedicated 100G wave from Oakland to Chicago to NYC

• Connects to 100G across Atlantic to Amsterdam (ANA-100G)

OpenFlow Testbed

• 10G Nationwide Footprint

Dark Fiber Testbed

• Continental-scale fiber footprint for disruptive research

Selma

Jackson

Houston

Dallas

Tulsa

Kansas CitySt. Louis

Peoria

Nashville

Louisville

Indianapolis

Chicago

South Bend

Cleveland

Buffalo

Albany

Cambridge

New York

Pittsburgh

Washington DCDenverGoodland

Albuquerque

El Paso

Los Angeles

Phoenix

Echo Springs

Salt Lake CityReno

Sacramento

Sunnyvale

Eugene

Seattle

Boise

Raleigh

Philadelphia

Atlanta

Charlotte

Chattanooga

317 miles20

5 m

iles

130 mile

s

152 miles 259 miles

264 miles

295

mile

s

179 miles

137

mile

s

228 miles

255 miles

212 m

iles

150 miles

228 m

iles27

5 m

iles

212

mile

s2

76

mile

s

116 miles

95 miles

56

0 m

iles

284 miles

618 miles

317 miles

422 miles

551 miles325 miles

500 m

iles

249 miles

863 miles

25

7 m

iles

278

mi le

s

248 m

iles

172 miles

306 miles

275 mile

s

147 miles

246 mile

s

198 miles 460 miles

336 miles

204 miles

532 miles

138 miles

LBNL Long Haul Dark Fiber Routes

12,924 miles

BayExpres Metro Fibers: 432 miles

ChiExpress Metro Fibers: 167 miles

NYExpress Metro Fibers: 6 miles

74 miles

Chepachet

Stamford61 miles

119 m

il es

Silver City

119 miles

Seminary


2/25/13 20

ESnet 10G OpenFlow Testbed

HOUS

NERSC SUNN

LBNL

StarLight

ANL

BNL

NYC


2/25/13 3

Test%Hosts%

NERSC Test%Hosts%

StarLight MAN

LAN

ESnet 100G Testbed


Outline





Network engineered for the Elephants

11/14/2013


Sensitive Elephants, Robust Mice

> 80x reduction in data

transfer rate at DOE-

relevant distances (ANL to

NERSC) and speeds

(10Gpbs).

How to build a lossless network service?

• Infrastructure: ample network capacity

• Equipment: deep packet buffers

• ScienceDMZ: optimized end-site architecture

• perfSONAR: automatic and continual verification of

network health

• OSCARS: ‘fast lanes’

• 60 Mbps out / 5 Gbps in

• 88 ms RTT

• 122 Mbps out / 7 Gbps in

• 51 ms RTT

• 1 Gbps out / 9.5 Gbps in

• 11 ms RTT

• 7.3 Gbps out / 9.8 Gbps in

• 1 ms RTT

http://www.es.net/assets/pubs_presos/sc13sciDMZ-final.pdf










“Science DMZ” Design Pattern for Data

Transfer

11/14/2013 16

Dedicated

Systems for

Data Transfer

Network

Architecture

Performance

Testing &

Measurement

Data Transfer Node • high performance

• tuned for data transfer

• proper tools

Science DMZ • dedicated and clean

location for DTN

• easy to deploy - no

need to redesign the

whole network

• additional info:

http://fasterdata.es.net/

perfSONAR • enables fault isolation

• verifies correct operation

• widely deployed in ESnet

and other networks, as well

as sites and facilities

source:

Eli Dart

ESnet



Prototype Science DMZ

10GE

10GE

10GE

10GE

10G

Border Router

WAN

Science DMZ

Switch/Router

Enterprise Border

Router/Firewall

Site / Campus

LAN

High performance

Data Transfer Node

with high-speed storage

Per-service

security policy

control points

Clean,

High-bandwidth

WAN path

Site / Campus

access to Science

DMZ resources

perfSONAR

perfSONAR

11/14/2013 17

source:

Eli Dart

ESnet


Prototype Science DMZ Data Path

10GE

10GE

10GE

10GE

10G

Border Router

WAN

Science DMZ

Switch/Router

Enterprise Border

Router/Firewall

Site / Campus

LAN

High performance

Data Transfer Node

with high-speed storage

Per-service

security policy

control points

Clean,

High-bandwidth

WAN path

Site / Campus

access to Science

DMZ resources

perfSONAR

perfSONAR

High Latency WAN Path

Low Latency LAN Path

11/14/2013 18

source:

Eli Dart

ESnet


Science DMZ is critical.

Knowledgebase:


Science DMZ:

http://fasterdata.es.net/science-dmz/

Security:http://www.internet2.edu/presentations/tip2013/20130115-dart-science-dmz.pdf

CC-NIE:

http://www.nsf.gov/pubs/2013/nsf13530/nsf13530.htm

11/14/2013 19








http://www.internet2.edu/presentations/tip2013/20130115-dart-science-dmz.pdf













Outline





http://www.tomsitpro.com/articles/sdx-software-defined-kitchen-sink,1-1085.html

SDN is everywhere!

SDN

2013


What is SDN?

Control

Software

Network

ASICs

Firmware

Network Element

Network

Monitoring

Network

Provisioning

Protocols (SNMP, TL1)

Provisioning

Topology

Statistics

Network Virtualization

[Science]

Applications Network Apps

[NaaS]

Protocol(s) (OpenFlow, ?)

Loose definition: separation of

data-plane from control plane

In essence: enables

programmability

Network

ASICs

Firmware

control

Network

ASICs

Firmware

control

Network

ASICs

Firmware

control

Network

ASICs

Firmware

control

programmable

Network Controller(OS)

Network

ASICs

Firmware

control

Network Element

Control

Software

Network

ASICs

Firmware

Cloud/End-user Applications


What is the paradigm change?

Internet today:

- Built-in control in each layer

- Multiple management domains

SDN Approach:

- Network-wide cross-layer view

- OpenFlow enables programmatic

access to network flows

Layer 1

Layer 2

Layer 3

Control

Control

Control

Ma

na

ge

me

nt L

ayer

1

Layer

2

Layer

3

Control

(Network-wide view)

Ma

na

ge

me

nt

OpenFlow OpenFlow

Layer 3 Control ?

10/16/13 Inder Monga 23


Simple programming constructs OpenFlow 1.0 standard

Switch Port

MAC src

MAC dst

Eth type

VLAN ID

IP Src

IP Dst

IP Prot

L4 sport

L4 dport

Rule Action Stats

1. Forward packet to zero or more ports 2. Encapsulate and forward to controller 3. Send to normal processing pipeline 4. Modify Fields 5. Any extensions you add!

+ mask what fields to match

Packet + byte counters

24

VLAN pcp

IP ToS

Slide courtesy Srini Seetharaman 11/14/2013 Inder Monga


Controller

PC

OpenFlow usage Classic model, Simple example

OpenFlow Switch

OpenFlow Switch OpenFlow Switch

Alice’s App

Decision? OpenFlow

Protocol

Alice’s Rule

Alice’s Rule Alice’s Rule

11/14/2013 Inder Monga, WLCG GDB 25

Alice

OpenFlow offloads control intelligence to remote software


Network community is still struggling to meet

application requirements captured in 1986!

Brute force approach (add more bandwidth)

is not going to meet those requirements

First workshop report for ESnet on intersite networking, 1986

Why SDN?

Bridging the application-network divide

Lawrence Berkeley National Laboratory U.S. Department of Energy | Office of Science Slide from Ian Foster

www.ci.anl.gov www.ci.uchicago.edu

28

Advanced Photon Source data rates

1

10

100

1000

10000

1-I

D-1

1-I

D-2

1-I

D-3

1-I

D-4

2-B

M

2-I

D-B

2-I

D-E

2-I

D-D

3-I

D-B

3-I

D-C

7

8-B

M

8-I

D-I

8-I

D-E

9

11

-ID

-B

11

-ID

-C

11

-ID

-D

12

-BM

12

-ID

-B

12

-ID

-C/D

15

-ID

20

21

-ID

21

-ID

-D

21

-ID

-E

21

-ID

-F

21

-ID

-G

22

23

-ID

-D

23

-ID

-B

30

32

-ID

-1

32

-ID

-2

34

-ID

Data Rate (expected in the next 5-10 years) MB/s

Data Rate (current) MB/s

Francesco de Carlo, APS Slide from Ian Foster


Hope #1: Programmability will lead to greater

predictability

Science applications increasingly need

to deal with high performance, any-

any bursts of data

SDN enables

• multi-layer control – packet and

optical layer

• Control over individual flows – ex.

Route science flows around packet

bottlenecks

• Routing non-TCP flows over WAN

Many NRENs have access to fiber,

optical and packet platforms.

Burst movement of data

using PhEDex

Analysis triggered data

movement (PD2P)


Journey towards programmability

Seamless multi-layer for handling elephant flows Layer123 SDN World Congress, Bad Homburg, October 2013

OpenFlow &

REST/JSON

OpenFlow 1.0

WDM/ OTN/

Packet

OTS

Virtualization

Host A Host B

OTS Config

Manager

L0/L1

Topology Multi-Layer

Path Engine

Multi-Layer

Provisioning

Multi-Layer

Topology App

Advanced Reservation System (OSCARS)

SDN Controller

Floodlight

Traffic

Optimization

Engine

Multi-Layer

SDN Control

Layer

Infinera DTN-X

Live Demo Nov 22: http://www.sdncentral.com/events/brocade-infinera-esnet-sdn-demo/

http://www.sdncentral.com/events/brocade-infinera-esnet-sdn-demo/











Abstractions are important (Scott Shenker, October 2011)

“The ability to master complexity is not the same as the

ability to extract simplicity”

“Abstractions key to extracting simplicity”

“SDN is defined precisely by these three abstractions”

• Distribution: centralized vs. distributed

• Forwarding: programming the fabric

• Specification: virtualization

http://opennetsummit.org/archives/oct11/shenker-tue.pdf

11/14/2013 © Inder Monga OFC/NFEC, 2013 31


What is the right abstraction for a

(dynamic) collaboration?

• Set of (dynamic) point to point circuits

• Restricted & static routing policy

• Lots of meetings



Hope #2: Virtualization will simplify how

applications program the network



Network slice

Modeled as a

Virtual WAN

Network Element

NB API

App 1 App ‘n’

simple

complex

Simple, Multipoint, Programmable



Real network is too complex to program for applications SRS, Ciena, SuperComputing 2012, Salt Lake City

Insights

• Virtualization is

the killer-app

for SDN (Scott

Shenker)

• ‘complexity’

pushed to the

‘network

hypervisor’

• Architectural

simplicity –

Flow

programming

only needed at

edges of the

network, core

can be legacy


Thought experiment:

Build an N-port virtual switch for a collaboration

LHC Tier 2 Analysis

Centers

Universities/

physics

groups

Universities/

physics

groups

Universities/

physics

groups

Universities/

physics

groups

Universities/

physics

groups

Universities/

physics

groups Universities/

physics


physics

groups

Universities/

physics

groups

Universities/

physics

groups

Universities/

physics

groups

Universities/

physics


physics

groups

Universities/

physics

groups

Universities/

physics

groups

Universities/

physics

groups

Universities/

physics

groups

The LHC Open

Network

Environment

(LHCONE)

WAN Virtual Switch

CERN →T1 mile

s kms

France 350 565

Italy 570 920

UK 625 1000

Netherlands 625 1000

Germany 700 1185

Spain 850 1400

Nordic 1300 2100

USA – New York 3900 6300

USA - Chicago 4400 7100

Canada – BC 5200 8400

Taiwan 6100 9850

Source: Bill

Johnston



SDN is about system optimization

When the application and network work as a system, network resource

optimization is possible

Without knowledge of flows, networking can only do coarse

characterization

Fine discrimination of flows possible with SDN, meet application needs

Google’s B4 SDN Network Utilization


Hope #3: SDN enables an opportunistic

way to leverage all bandwidth without extra

investment

exploits the fact ‘In general it’s much cheaper to transport

data than to store it’,

for example, vision of a ‘diskless Tier3’ for LHC


Is SDN ready for operations?

The innovator’s dilemma, Clayton Christensen

2013: SDN is here


Challenges = Opportunities?

Provisioning

Topology

Statistics


[Science]

Applications Network Apps

[NaaS]

Protocol(s) (OpenFlow, ?)

Network

ASICs

Firmware

control

Network

ASICs

Firmware

control

Network

ASICs

Firmware

control

Network

ASICs

Firmware

control


Network

ASICs

Firmware

control

Cloud/End-user Applications

1) Communication plane

can be disrupted

2) Single point of

failure or attack?

3) Responsive to

rapid topology changes?

Flapping?

4) Complexity of management

from operations on virtual to

physical reality?

5) Who do you blame?

Who do you call?

Who debugs?

6) Hardware will

never be simple, manage

capability differences

7) How does this

interoperate with the

current IP network?



How to bridge the ‘Internet’ with SDN networks? Treehouse, BGP over SDN infrastructure, ONS 2013 and ongoing

Insights

• SDN networks can now peer with existing Internet

• New techniques need to be developed to scale controller-based networking

• Baby steps

Project led by Josh Bailey, Google


The Bigger Picture: Organizational challenge

to deal with SDN

Network (control and data plane) Layer 0-7

Management, Tools, Measurement Layer 8-9

People (network engineers, sysadmins, operators)

Layer 10

Network (API + data plane)

Network Operating System (control)

+

New tools, service plane and management

People (network engineers**, sysadmins, operators**)

+

(software engineers/devops)

** need to develop new skills


SDN Take-Away

• SDN is a journey R&E networks have been on for a while, but

recently commercially formalized

• Innovator’s dilemma gaps between established and the new ways,

industry and researcher momentum will close those gaps

• Maturity will still take some time

• Focus on the problem being solved aka hope rather than the

vendor hype

• Plan for the SDN future – skillsets, training and hiring


Questions?


Problem: Mice and Elephant flow separation OSCARS, 2006-2013

SDN before it was called ‘SDN’

Insights

• abstractions are

key to success,

regardless of the

protocol

• can only learn by

doing (lots of

naysayers)

• Primary use will

be different than

the original use-

case


Bringing it Together:

A potential SDN R&E architecture

ESnet NERSC

BNL ORNL

Data Plane

Control Plane

Service Plane

R&E Network

NRM

NSI

NS

I

OSCARS OF

OSCARS

SDN Ctrl.

OneWan

Switch

RON

OF

(e2e resource

broker)

(e2e resource

broker)

OF

Transport

SDN

SDN only at edges,

efficient transport in core

Customer

SDN Ctrl. Customer

SDN Ctrl.

FLA

Router

FLA

Router

FLA

Router

Univ.

OF


Three Inflection Points for Data-Intensive

Science

Abundant capacity (88 λ x 100Gbps)

ESnet architecture

(Science DMZ) +

NSF grants.

Campus architectures newly optimized for data mobility (optimizing network architectures end-to-end)

2. Programmability


What is common between modern

networks and analog phone switches?


Labor-intensive, nearly static, error prone

Software-Defined Networking: Hype vs. Hope · Software-Defined Networking (SDN): Hype vs. Hope...

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