Bill St. Arnaud

1.
NREN
strategies
for
Data-­‐Intensive
Science
in
a
Carbon
Constrained
World
Bill
St.
Arnaud
Bill.st.arnaud@gmail.com
Unless
otherwise
noted
all
material
in
this
slide
deck
may
be
reproduced,
modified
or
distributed
without
prior
permission
of
the
author

2. Theme
of
this
talk
• We
have
already
lost
the
baIle
to
save
the
planet
from
extreme
climate
change.
Rather
than
focusing
on
reducing
energy
consumpKon,
(MiKgaKon)
we
now
need
to
focus
on
surviving
climate
change
(AdaptaKon)
• Explosion
of
data
and
energy
consumpKon
by
computers
and
networks
is
contribuKng
to
energy
demand
and
CO2
emissions
• But
big
data
and
science
will
be
criKcal
as
move
to
focus
on
adapKng
to
climate
change
• How
can
Internet
and
IT
help
us
build
NRENs
and
support
science
and
educaKon
that
can
adapt
to
global
warming?

3. Changing
NREN
networking
environment
• Global
Virtual
Research
CommuniKes
• Increasing
co-­‐operaKon
between
public
and
private
researchers
• Rapidly
changing
users
demands
• Increasing
potenKal
of
commercial
ICT-­‐service
providers
• EducaKon:
any
Kme,
any
place,
any
device
• CiKzen
Science
and
M2M
communicaKons
and
sensors
• The
disappearing
campus
IT
&
diminishing
experKse
in
ICT
centres
of
connected
insKtuKons

4. Although
there
is
less
news
coverage
global
warming
has
not
disappeared

5. Half
of
US
experienced
record
droughts
or
deluges
in
2011
2010
warmest
year
ever
–
we
are
only
at
the
start
of
the
curve
of
the
hockey
s7ck.
The
worst
is
yet
to
come

6. Blame
it
on
Canada
How
warming
in
the
ArcKc
affects
weather
in
Louisiana
• Warming
ArcKc
slowing
down
jet
stream
•
Basic
Thermodynamics
-­‐
polar
temperatures
drive
the
jet
stream,
– There’s
been
a
20
percent
drop
in
the
zonal
wind
speeds.
• As
get
stream
slows
down,
it
leads
to
those
bigger
kinks
in
the
jet
stream.
– That
amplificaKon
is
associated
with
persistent
weather
paIerns
that
lead
to
“extremes”
like
drought,
flooding
and
heat
waves.
• Those
slow-­‐moving,
persistent
waves
of
weather
energy
may
have
played
a
role
in
the
big
snows
that
hammered
parts
of
the
West
last
winter,
as
well
as
some
of
the
extreme
winter
weather
that
hit
South
West
US
and
Europe
• hIp://summitcountyvoice.com/2012/01/14/
global-­‐warming-­‐revenge-­‐of-­‐the-­‐atmosphere/
6

7. Climate
Forecasts
• MIT
report
predicts
median
temperature
forecast
of
5.2°C
– 11°C
increase
in
Northern
Canada
&
Europe
– hIp://globalchange.mit.edu/pubs/
abstract.php?publicaKon_id=990
MIT
• Last
Ice
age
average
global
temperature
was
5-­‐6°C
cooler
than
today
– Most
of
Canada
&
Europe
was
under
2-­‐3
km
ice
• Nearly
90
per
cent
of
new
scienKfic
findings
reveal
global
climate
disrupKon
to
be
worse,
and
progressing
more
rapidly,
than
expected.
• hIp://www.skepKcalscience.com/pics/
Freudenburg_2010_ASC.pdf

8. Future
Droughts
•
Palmer
Drought
Severity
Index,
or
PDSI.
•
The
most
severe
drought
in
recent
history,
in
the
Sahel
region
of
western
Africa
in
the
1970s,
had
a
PDSI
of
-­‐3
or
-­‐4.
•
By
2030
Western
USA
could
see
-­‐4
to
-­‐6.
Drought
in
Texas
clearly
caused
by
global
warming:
hIp://goo.gl/QjHRS
•
By
2100
some
parts
of
the
U.S.
and
LaKn
America
could
see
-­‐8
to
-­‐10
PDSI,
while
Mediterranean
areas
could
see
drought
in
the
-­‐15
hIp://www.msnbc.msn.com/id/39741525/ns/
or
-­‐20
range.
us_news-­‐environment/

9. DramaKc
changes
in
precipitaKon
• Every
conKnent
has
suffered
record
rainfalls
• Rains
submerged
one-­‐fioh
of
Pakistan,
a
thousand-­‐year
deluge
swamped
Nashville
and
storms
just
north
of
Rio
caused
the
deadliest
landslides
Brazil
has
ever
seen.
• Observed
increase
in
precipitaKon
in
the
last
few
decades
has
been
due
in
large
part
to
a
disproporKonate
increase
in
heavy
and
extreme
precipitaKon
rates
which
are
exceeding
predicKons
made
in
models

10. New
Challenge:
Climate
AdaptaKon
• Obama’s
NaKonal
Science
Advisor
John
Holdren
“MiKgaKon
alone
won’t
work,
because
the
climate
is
already
changing,
we’re
already
experiencing
impacts….A
miKgaKon
only
strategy
would
be
insanity,”
• Equal
emphasis
given
to
adaptaKon
–
avoiding
the
unmanageable,
and
adaptaKon
–
managing
the
unavoidable.”
• Obama’s
Climate
AdaptaKon
ExecuKve
Order
– hIp://www.stumbleupon.com/su/1tU8go/www.good.is/post/obama-­‐s-­‐secret-­‐climate-­‐adaptaKon-­‐plan/

11. Climate
Change
Impact
on
Internet
and
NRENs
• UK
Government
study
Climate
Change
could
ruin
the
Internet
– hIp://www.grist.org/list/2011-­‐05-­‐09-­‐climate-­‐change-­‐could-­‐ruin-­‐the-­‐internet
• California
aims
to
have
30%
renewable
power
– Impact
on
reliability
of
power
systems
• Last
year
Nuclear
power
plants
in
France
were
forced
to
shut
down
because
cooling
water
was
too
warm
• Germany
is
commiIed
to
shuvng
down
all
of
its
nuclear
plants
• Droughts
will
restrict
producKon
of
hydro-­‐electric
power
• Energy
shortages
and
disrupKons
are
predicted
to
increase
in
the
coming
years

12. Impact
on
ICT
sector
According
to
IEA
ICT
will
represent
40%
of
all
energy
consump7on
by
2030
www.smart2020.org
ICT
represent
8%
of
global
electricity
consumpKon
Future
Broadband-­‐
Internet
alone
is
expected
to
consume
5%
of
all
electricity
hIp://www.ee.unimelb.edu.au/people/rst/talks/files/Tucker_Green_Plenary.pdf

13. R&E
biggest
consumer!!
Per
employee
Per
sector
Australian
Computer
Society
Study
hIp://www.acs.org.au/aIachments/ICFACSV4100412.pdf

14. The
ICT
energy
consumpKon
in
higher-­‐
ed
• Campus
compuKng
20-­‐40%
electrical
energy
consumpKon
on
most
campuses
– Studies
in
UK
and
The
Netherlands
– hIp://goo.gl/k9Kib
• Closet
clusters
represent
up
to
15%
of
electrical
consumpKon
– hIp://isis.sauder.ubc.ca/research/clean-­‐technology-­‐and-­‐energy/green-­‐it/
• Campus
data
center
alone
represents
8-­‐20%
of
electrical
consumpKon
– hIp://www.iisd.org/publicaKons/pub.aspx?pno=1341
• IISD
study
demonstrated
that
moving
Canadian
research
to
cloud
would
pay
for
itself
in
energy
savings
and
CO2
reducKon
– hIp://www.iisd.org/publicaKons/pub.aspx?pno=1341

15. The
real
cost
of
campus
compuKng
• Land
-­‐
2%
• Core
and
shell
costs
–
9%
Belady,
C.,
“In
the
Data
Center,
Power
and
Cooling
Costs
More
than
IT
Equipment
it
Supports”,
Electronics
Cooling
• Architectural
–
7%
Magazine
(February
2007)
• Mechanical/Electrical
–
82%
– 16%
increase/year
since
2004
Source:
ChrisKan
Belady

16. The
Data
Deluge
2004:
36
TB
2012:
2,300
TB
Genomic
sequencing
output
x2
every
Climate
9
month
model
intercomparison
project
(CMIP)
of
the
IPCC
MACHO
et
al.:
1
TB
Palomar:
3
TB
2MASS:
10
TB
GALEX:
30
TB
Sloan:
40
TB
Pan-­‐STARRS:
40,000
TB
1330
molec.
bio
databases
Nucleic
Acids
Research
(96
in
Jan
2001)
Source:
Ian
Foster,
UoChicago

17. Big
science
has
achieved
big
successes
OSG:
1.4M
CPU-­‐hours/day,
>90
sites,
>3000
users,
>260
pubs
in
2010
LIGO:
1
PB
data
in
last
science
run,
distributed
worldwide
Robust
producKon
soluKons
SubstanKal
teams
and
expense
Sustained,
mulK-­‐year
effort
ApplicaKon-­‐specific
soluKons,
built
on
common
technology
ESG:
1.2
PB
climate
data
delivered
to
23,000
users;
600+
pubs
Source:
Ian
Foster,
UoChicago

18. But
small
science
is
struggling
More
data,
more
complex
data
Ad-­‐hoc
soluKons
Inadequate
sooware,
hardware
Data
plan
mandates
Source:
Ian
Foster,
UoChicago

19. Growth
in
sensor
networks
and
CiKzen
Science
Glacier
Tracking
Real
Time
Health
Monitoring
Smart
Trash
19

20. THE
CHALLENGE
We
need
soluKons
to
address
climate
change,
data
deluge,
needs
of
scienKsts,
global
collaboraKon,
the
evolving
network
of
any
Kme,
any
place,
any
device
and
yet
addresses
the
challenge
of
disappearing
IT
on
campus
while
sKll
providing
a
leadership
role
in
next
generaKon
Internet
and
broadband,
and
find
ways
to
pay
for
it
all
in
an
era
of
severe
fiscal
constraint.

21. THE
SOLUTION
1. Brokered
Green
Clouds
and
off
site
campus
IT
2. Sooware
Defined
Networks
(OpenFlow)
3. NREN
naKonal
wireless
network
4. Global
Interconnected
Dynamic
OpKcal
Networks
5. eScience
Pla|orms
with
next
gen
IdM
6. Community
anchor
IXPs
with
CDN
and
M2M
hosKng
7. New
billion
dollar
revolving
green
energy
funds
at
many
universiKes
21

22. 1.
Brokered
Green
Clouds
and
off
site
CAMPUS
IT
22

23. UniversiKes
moving
to
eliminate
IT
departments
• Already
many
primary
funcKons
of
IT
department
are
being
outsourced
to
the
cloud
– E-­‐mail,
web,
DNS,
research
compuKng,
etc
– University
of
Western
Australia
has
outsourced
virtually
all
campus
servers
to
an
external
private
cloud
• Even
rouKng,
network
and
firewall
funcKons
being
outsourced
to
NREN
– AARnet,
SUnet
and
other
NRENs
offering
border
gateway
rouKng
services
with
collapsed
IP
backbones
– Sooware
Defined
Networks
makes
it
easy
to
configure
outsourced
LAN
– Network
faciliKes
can
be
located
• Increasingly
most
traffic
is
in/out
of
campus,
instead
of
within
– Social
networking,
P2P,
Clouds,
Kuali,
Blackboard
– Future
of
Campus
IT
–
high
speed
opKcal
network
connected
to
WiFi/5G
hot
spots
with
tablets
– No
servers,
no
LAN

24. MIT
to
build
zero
carbon
data
center
in
Holyoke
MA
• The
data
center
will
be
managed
and
funded
by
the
four
main
partners
in
the
facility:
the
MassachuseIs
InsKtute
of
Technology,
Cisco
Systems,
the
University
of
MassachuseIs
and
EMC.
• It
will
be
a
high-­‐performance
compuKng
environment
that
will
help
expand
the
research
and
development
capabiliKes
of
the
companies
and
schools
in
Holyoke
– hIp://www.greenercompuKng.com/news/2009/06/11/
cisco-­‐emc-­‐team-­‐mit-­‐launch-­‐100m-­‐green-­‐data-­‐center

25. NREN
Brokered
Cloud
for
IT
departments
and
Researchers
• Internet
2
Net
+
– Provisioning
of
mulK
vendor
cloud
services
leveraging
the
Internet2
Network
and
InCommon
Federated
AuthenKcaKon
– Interoperable
marketplace
for
services
where
individual
insKtuKons
might
procure
services
from
a
wide
range
of
cloud
services
providers.
• HEFCE
and
JISC
to
Deliver
Cloud-­‐Based
Services
for
UK
Research
– Besides
providing
brokered
cloud
services
they
are
also
providing
cloud
“soluKons”
for
IT
departments
and
researchers
– hIp://www.hpcinthecloud.com/hpccloud/2011-­‐06-­‐27/hefce_and_jisc_to_deliver_cloud-­‐
based_services_for_uk_research.html?utm_medium=twiIer&utm_source=twiIerfeed
• SURFnet:
Community
Cloud
Models
and
the
Role
of
the
R&E
network
as
a
broker
for
cloud
services
– hIp://www.slideshare.net/haroldteunissen/community-­‐clouds-­‐shared-­‐infrastructure-­‐as-­‐a-­‐service

26. 2.
Soware
Defined
networks
26

27. GreenStar
Network
World’s
First
Zero
Carbon
Cloud/Internet

28. OpenFlow
Follow
the
wind/Follow
the
sun
Canadian
GSN
European
GSN
Domain
Domain
Export
VM
NoKfy
EU
Cloud
Manager
Cloud
Manager
Cloud
Manager
Internet
Dynamically
Configure
IP
Tunnel
Host
Network
Host
Resource
Manager
Resource
•
Shudown
VM
•
Copy
Image
•
Update
VM
Context
Mantychore2
•
Start
VM
Shared
VM
storage
Shared
storage
VM
Lightpath
OpKcal
switch
OpKcal
switch
Host
Cloud
Proxy
Cloud
Proxy
Host

29. OpenFlow-­‐based
cloud
OpenFlow
Network
A
OpenFlow
Network
B
VM
VM
VM
VM
VM
VM
VM
VM
eth1
eth0
eth1
eth0
eth1
eth0
eth1
eth0
Open
Virtual
Open
Virtual
Open
Virtual
Open
Virtual
Switch
(OVS)
Switch
(OVS)
Switch
(OVS)
Switch
(OVS)
Host
Host
Host
Host
OF
Controller
Ethernet
Switch
OpenFlow
Control
plane
Internet
OpenFlow
Data
plane
OVS
OVS
eth0
eth1
eth0
eth1

30. Green
Clouds
InternaKonal
• GreenLight
explores
how
researchers
can
take
advantage
of
data
centers
linked
by
high-­‐speed
networking
in
an
era
of
carbon-­‐thrioy
compuKng
• Recent
studies
migraKng
virtual
machines
to
green
energy
sites
indicate
that
100
Gb/s
networks
are
far
superior
to
10
Gb/s
to
make
this
transparent.
• SURFnet
7
lightpath
connecKon
to
GreenQCloud
in
Iceland
SURFconecxt
control
of
lightpath
to
Future
Global
Network
of
Green
Clouds
GreenQCloud
in
Iceland
interconnected
by
GLIF

31. Science
Cloud
CommunicaKon
Services
Network
• Enterprise
clouds
use
commodity
internet;
computaKonal
clouds
for
data-­‐intensive
science
require
dynamic
cloud
provisioning
integrated
with
dynamic
high
performance.
• TransCloud:
example
of
dynamic
networking
&
dynamic
cloud
provisioning
Example
of
working
in
the
TransCloud
[1]
Build
trans-­‐con7nental
applica7ons
spanning
clouds:
•
Distributed
query
applica7on
based
on
Hadoop/Pig
•
Store
archived
Network
trace
data
using
HDFS
•
Query
data
using
Pig
over
Hadoop
clusters
[2]
Perform
distributed
query
on
TransCloud,
which
currently
spans
the
following
sites:
•
HP
OpenCirrus
•
Northwestern
OpenCloud
•
UC
San
Diego
•
Kaiserslautern
Source:
Maxine
Brown

32. 3.0
NREN
Na7onal
Wireless
Network
32

33. Building
a
NREN
wireless
network
• Vision:
to
allow
students,
researchers
and
employees
to
collaborate,
research,
learn
anyKme
and
anywhere
they
seem
fit!
• Also
Internet
of
Things
–
Machine
to
Machine
communicaKons
• ExisKng
3G
and
4G
networks
cannot
handle
data
load
– Roaming
gateways
prevent
global
seamless
access
– Voice
centric
architectures
• New
mobile
networks
seamlessly
integrate
with
WiFi
on
campus
– New
Wifi
2.0
standards
802.11u
allow
for
data
handoff
from
3G
networks
– Eduroam
can
be
the
global
authorizaKon
tool
– OpenFlow
can
be
used
to
architect
integrated
soluKons
from
wireless
node
across
opKcal
network

34. Impact
of
NREN
wireless
networks
• The
phone
is
a
also
a
sensor
pla|orm
• Processing
is
done
in
real
Kme
in
the
cloud
– Allowing
processing
that
can’t
be
done
on
the
device
– Big
data
analysis
• New
campus
or
hot
spot
centric
architectures
integraKng
LTE
and
Wifi
– See
SURFnet
pilot
hIp://www.surfnet.nl/en/nieuws/Pages/BackgroundarKcle.aspx
• WiFi
nodes
can
be
powered
by
renewable
sources
such
as
roof
top
solar
panel
over
400Hz
power
systems
or
ethernet
power
34

35. The
Regulatory
Challenge
• Today’s
SIM-­‐card
locks
user
to
the
network
• If
NREN
becomes
a
MVNO
with
own
SIM-­‐cards,
users
could
roam
seamlessly
around
the
globe
• Only
public
service
providers
have
access
to
IMSI-­‐numbers
for
SIM-­‐cards
• One
opKon
is
to
lobby
regulators
to
give
R&E
networks
access
to
IMSI-­‐numbers

36. 4.
Global
Interconnected
Dynamic
Op7cal
Networks
36

37. GLIF
37

38. e-­‐Research
Scenario
GOLE
38
Source:
SURFnet

39. Importance
of
GOLEs
• Increasingly
more
research
and
educaKon
is
internaKonal
collaboraKon
– Cornell-­‐
Technion
announcement
– US
overseas
university
campuses
in
UK
and
elsewhere
– GOLES
enable
direct
peering
of
regional
networks
or
even
insKtuKons
• Many
researchers
need
access
to
commercial
clouds
and
data
specialists
– AUP
issues
ooen
prevent
NRENs
from
directly
connecKng
up
these
insKtuKons
– Genomics
and
bio-­‐informaKcs
processing
and
climate
modeling
• Many
commercial
research
insKtuKons
need
access
to
lightpaths
– GOLES
provide
neutral
access
points
for
interconnect
to
AUP
free
lightpaths
• Enables
new
services
– Sooware
Defined
Network
using
Switched
lambdas
39

40. 5.
eScience
Plaiorms
with
next
gen
IdM
40

41. Towards
“research
IT
as
a
service”
Scientific data management as a service
GO-Store GO-Collaborate GO-Galaxy GO-Transfer
GO-Compute GO-Catalog GO-Team GO-User
Source:
Ian
Foster,
UoChicago
41

42. SaaS
services
in
acKon:
The
XSEDE
vision
Academic institution = Standard
interface
XUAS
Globus Online: Hosted persistent services
User Team Catalog Transfer Compute ...
2
InCommon
... Open
Commercial Data Science
XSEDE service provider provider provider Grid
42

43. Virtual
OrganisaKons
CollaboraKon
Infrastructure
Netherlands BioInformatics Centre (NBIC)
(SURFconext)
GuestsNBI
N=6 N=10 N=30 N=20 C
A6ri Gro
grou provisioni b.
N=66
up
AAI
ps
ng
mgm …
t
Generic
Broker
Supporting Services
Network
Storage
Compute
Instrument
• SURFfederatie
Virt
Broker
Broker
Broker
Broker
• SURFteamsual
Comput Instrum • OpenSocial
Network
Storage
e
ent
IdP
Services
Services
Services
Services
Experiment
Publisher
Grid res.
PubMed
My
Apps.NB
IC.nl
Source:
SURFnet

44. 6.
eScience
and
Big
Data
for
CiKzen
Science
and
Community
44

45. Extending
science
and
educaKon
to
the
community
• Community
anchor
Internet
Exchange
Points
help
clear
the
boIleneck
of
content
peering
– Co-­‐hosKng
of
CDN
caching
boxes
– Managed
by
NREN
– Examples
include
KAREN
(New
Zealand),
BCnet
and
UNINETT
(Norway)
• Minimize
tromboning
of
R&E
traffic
to
homes
and
schools
• Can
support
extension
of
Eduroam
to
community
WiFi
spots
and/or
community
last
mile
networks
• Allows
for
M2M
traffic
and
anywhere,
anyKme
traffic
to
propagate
through
the
community
Community
IXP
managed
by
NREN
45

46. 7.
How
to
pay
for
it
all
46

47. $1
billion
funding
program
• Green
revolving
funds
are
either
part
of
a
university
endowment
program
or
publicly
traded
enKKes.
– hIp://www.sustainablebusiness.com/index.cfm/go/news.display/id/23028
• They
make
investments
in
energy
efficiency
and
GHG
reducKon
iniKaKves.
Payback
typically
32%
• ICT
can
represent
up
to
40%
of
the
electrical
energy
consumpKon
at
university
and
growing
• The
obvious
low
hanging
fruit
is
to
move,
as
much
as
possible
the
closet
clusters
and
campus
data
center
faciliKes
to
commercial
clouds.
Next
is
network
infrastructure
such
as
rouKng
and
servers
• Other
obvious
money
saving
pracKces
are
to
power
laptop
and
cell
phone
charging
staKons
with
roof
top
solar
panels
or
micro
windmills,
deploy
solar/wind
powered
WiFi
nodes,
and
use
on
the
move
electric
charging
for
campus
uKlity
vehicles,
etc
• Campus
IT
folk
and
NRENs
need
to
educate
managers
of
such
funds
the
IT
and
networking
can
play
a
much
more
significant
role
in
reducing
energy
consumpKon
and
GHG
emissions
47
then
tradiKonal
faciliKes
based
soluKons

48. Cyber-­‐infrastructure
in
a
Carbon
Constrained
World
hIp://net.educause.edu/ir/library/pdf/ERM0960.pdf

49. Let’s
Keep
The
ConversaKon
Going
E-­‐mail
list
Bill.St.Arnaud@gmail.com
Blogspot
Bill
St.
Arnaud
hIp://green-­‐broadband.blogspot.com
TwiIer
hIp://twiIer.com/BillStArnaud