Intel Research @ Berkeley
and
Extreme Networked Systems
www.intel-research.net/berkeley
David Culler
8/12/2002
Where this presentation might go...
aka Outline
• new models of industry/academic research
collaboration
• vast networks of tiny devices in the physical
world
• open infrastructure for emerging planetary-scale
services
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New model for ind/acad collaboration
• Key challenges ahead in EECS are fundamentally
problems of scale
– require level of investigation and engineering beyond what is
sustainable within the university and beyond what a company
can commit outside product scope
– industry possesses key technology and expertise
– requires insights from many perspectives
• A new lab stucture built around deep research
collaboration and intimate ties to the EECS
department
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industry contributes substantial effort of high quality
projects span boundaries
faculty co-direct lab
student / faculty cycles drive the continuous motion
• Operate in uniquely open fashion
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Intel Network of Lablets Concept
• Network of small labs working closely with top
computer science departments around the world
on deeply collaborative projects.
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Berkeley
– extreme network systems
Washington – HCI
CMU
– distributed storage
Cambridge
• Complement the corporate labs
– explore off the roadmap, long range, high risk
• Complement the external-research council
– drive projects of significant scale and impact
• Expand the channel
– Bi-directional transfer of people, ideas, technology
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lablet mission
• Leadership role in emerging and important areas
• Combining the unique strengths of Intel and Univ.
• Bi-directional exchange of breakthough ideas,
technology and people
University
Advance of the
research ecosystem
Lablet
Novel component
technology
SRPs
Advanced
Applications
Intel Labs
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Berkeley Emphasis
• Cross-cutting problems of scale.
• Extreme Interconnected Systems
• “endonets”
– dense, fine-grain networked systems deeply embedded in or
interacting with physical environment
– sensor networks
– ubiquitous computing architectures
– computational fabrics, surfaces, structures
• “exonets”
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broad coverage networked systems at societal scale
world-wide storage systems
composable infrastructure services
massive servers for millions of users
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Scale and structure
Active day-to-day involvement
• ~20 full-time Intel Researchers and Engineers
– currently 13
• ~5 part-time Intel folks
• 20 faculty, students, visitors, research
consultants
Two-in-a-box co-directors
• University Director + Intel Director
• Report to David Tennenhouse, VP Research
Project focused
• ~6-year projects starting about every two years
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Two Major Lab Projects
• Define and Develop complete ‘network system
stack’ for deeply embedded sensor/effector
networks
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enabling technology
create the community
core architecture, OS, networking, service foundations
demonstrate revolutionary applications
• Create an Open Laboratory for Widelydistributed “Planetary Scale” Services to explore
architecture, services and applications
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enabling resource catalyzes community
distributed development effort
foundations: scalable, secure slice-able platform
infra and service design trade-offs (DHT, Dist-storage)
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Open Collaborative Research Agreement
• Master Agreement states
– intent: Open
– terms, conditions (IP addendum)
• Research Project Descriptions
– what, who, where
• scope of work defines boundary of openness!
– an openness agreement is all about defining reach-through
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System Stack for Deeply Embedded
Networks
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Bridging the Technology-Appln Gap
service
network
system
architecture
algorithm / theory
data
mgmt
mgmt / diag / debug
application
technology
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prog / data model
Monitoring & Managing Spaces and Things
Deeply Embedded Networks
• # nodes >> # people
• sensor/actuator data stream
• unattended
• inaccessible
• prolonged deployment
• energy constrained
• operate in aggregate
• in-network processing necessary
• what they do changes over time
=> must be programmed over the network
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Project Activities
• Core Platform
– architecture, TinyOS, Networking
– simulation and debugging tools
• Programming Support
– NesC (TinyOS modularity and concurrency)
– Cooperating FSMs, atomicity
– Macroprogramming
• Sensor-Network databases
– streaming, noisy data, with in-network query processing
• Delay Tolerant Networking
– overlay for diverse, challenged internets
• Interactive Environments and Things
– ambient displays, remote physical communication
– context-aware tools for the handicapped
• Habitat and Environmental Monitoring
– dense sensor networks in the hands of life scientists
• Generic Sensor Kit
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Platform Architecture
• Goal
– create a small wireless device that would enable us to explore
the system design space, applns to be attempted, and a new
research community
– develop the architecture in response to observed system
design
application
• Approach
– joined in the series of UCB COTS mote designs
» WeC -> Rene -> iDot -> MICA
– look to silicon for full architecture
• New ideas
data
mgmt
service
network
system
– rich interfaces allow radical system optimizations
architecture
» analog wake-up, Tx-Rx time synch
– federation of accelerators, not dedicate protocol proc. technology
– HW/SW multithreading for low power, passive vigilance
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Berkeley Wireless Sensor ‘Motes’
Mote Type
Date
WeC
Rene
Rene2
Dot
Mica
Sep-99
Oct-00
Jun-01
Aug-01
Feb-02
Microcontroller (4MHz)
Type
Prog. Mem. (KB)
RAM (KB)
AT90LS8535
ATMega163
ATMega103/128
8
16
128
0.5
1
4
Communication
Radio
Rate (Kbps)
Modulation Type
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RFM TR1000
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10/40
OOK
OOK/ASK
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application
TinyOS Application Graph
Route map
router
sensor appln
packet
Radio byte
bit
Radio Packet
byte
Active Messages
RFM
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Serial Packet
UART
Temp
photo
SW
HW
ADC
clocks
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Example: self-organized adhoc, multi-hop routing of
photo sensor readings
3450 B code
226 B data
Graph of cooperating
state machines
on shared stack
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It is a noisy world after all...
• Get to rethink each of the layers in
a new context
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coding, framing
mac
routing
transport,
rate control
discovery
multicast
aggregation
naming
security
...
• Resource constrained, power
aware, highly variable, ...
• Every node is also a router
• No entrenched ‘dusty packets’
probability of reception from center
node vs xmit strength
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Example “epidemic” tree formation
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Habitat Monitoring
Acadia National Park
Mt. Desert Island, ME
Ongoing research
Great Duck Island
Nature Conservancy
LAN
WAN
(satcast)
http://www.greatduckisland.net
sensor nets
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application
service
data mgmt
network
system
architecture
algorithm / theory
Programming environments
Deep & scalable simulation
Algorithm behavior at scale
Operating on prob.
distributions
• Fine-Grain Inverse problems
• Pseudo-imaging
• Constructive foundations of
self-organization
mgmt / diag / debug
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prog / data model
Cross-cutting issues?
technology
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The Other Extreme
- Planetary Scale Services
www.planet-lab.org
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Motivation
• A new class of services & applications is emerging
that spread over a sizable fraction of the web
– CDNs as the first examples
– Peer-to-peer, ...
• Architectural components are beginning to emerge
– Distributed hash tables to provide scalable translation
– Distributed storage, caching, instrumentation, mapping, events ...
• The next internet will be created as an overlay on the
current one
– as did the last one
– it will be defined by its services, not its transport
» translation, storage, caching, event notification, management
• There will soon be vehicle to try out the next n great
ideas in this area
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Confluence of Technologies
• Cluster-based scalable distribution, remote execution,
management, monitoring tools
– UCB Millennium, OSCAR, ..., Utah Emulab, ModelNet...
• CDNS and P2Ps
– Gnutella, Kazaa, ... ,Pastry, Chord, CAN, Tapestry
• Proxies routine
• Virtual machines & Sandboxing
– VMWare, Janos, Denali,...
web-host slices (EnSim)
• Overlay networks becoming ubiquitous
– XBONE, RON, Detour...
Akamai, Digital Island, ....
• Service Composition Frameworks
– yahoo, ninja, .net, websphere, Eliza
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Established internet ‘crossroads’ – colos
Web Services / Utility Computing
Grid authentication infrastructure
Packet processing,
The Time is NOW
– Anets, .... layer 7 switches, NATs, firewalls
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Internet instrumentation
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Guidelines (1)
• Thousand viewpoints on “the cloud” is what matters
– not the thousand servers
– not the routers, per se
– not the pipes, per se
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Guidelines (2)
• and you miust have the vantage points of the crossroads
– primarily co-location centers
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Guidelines (3)
• Each service needs an overlay covering many
points
– logically isolated
• Many concurrent services and applications
– must be able to slice nodes => VM per service
– service has a slice across large subset
• Must be able to run each service / app over long
period to build meaningful workload
– traffic capture/generator must be part of facility
• Consensus on “a node” more important than
“which node”
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Guidelines (4)
Management, Management, Management
• Test-lab as a whole must be up a lot
– global remote administration and management
» mission control
– redundancy within
• Each service will require its own remote management
capability
• Testlab nodes cannot “bring down” their site
– generally not on main forwarding path
– proxy path
– must be able to extend overlay out to user nodes?
• Relationship to firewalls and proxies is key
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Guidelines (5)
• Storage has to be a part of it
– edge nodes have significant capacity
• Needs a basic well-managed capability
– but growing to the [email protected] model should be considered at
some stage
– may be essential for some services
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Initial Researchers (mar 02)
Washington
Tom Anderson
Steven Gribble
David Wetherall
MIT
Frans Kaashoek
Hari Balakrishnan
Robert Morris
David Anderson
Berkeley
Ion Stoica
Joe Helerstein
Eric Brewer
John Kubi
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Intel Research
David Culler
Timothy Roscoe
Sylvia Ratnasamy
Gaetano Borriello
Satya
Milan Milenkovic
http://www.planet-lab.org/
Rice
Peter Druschel
Utah
Jay Lepreau
CMU
Srini Seshan
Hui Zhang
UCSD
Duke
Amin Vadat
Jeff Chase
Princeton
Larry Peterson
Randy Wang
Vivek Pai
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Stefan Savage
Columbia
Andrew Campbell
ICIR
Scott Shenker
Mark Handley
Eddie Kohler
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Initial Planet-Lab Candidate Sites
UBC
UW
WI
Chicago
UPenn
Harvard
Utah
Intel Seattle
Intel
MIT
Intel OR
Intel Berkeley
Cornell
CMU
ICIR
Princeton
UCB
St. Louis
Columbia
Duke
UCSB
Washu
KY
UCLA
Rice GIT
UCSD
UT
ISI
Uppsala
Copenhagen
Cambridge
Amsterdam
Karlsruhe
Barcelona
Beijing
Tokyo
Melbourne
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Approach:Service-Centric Virtualization
• Virtual Machine Technology has re-emerged for hosting
complete desktop environments on non-native OS’s and
potentially on machine monitors.
– ex. VMWare, ...
• Sandboxing has emerged to emulate multiple virtual machines
per server with limited /bin, (no /dev)
– ex. ENSim web hosting
• Network Services require fundamentally simpler virtual
machines, can be made far more scalable (VMs per PM),
focused on service requirements
– ex. Jail, Denali, scalable and fast, but no full legacy OS
– access to overlays (controlled access to raw sockets)
– allocation & isolation
» proportional scheduling across resource container - CPU, net, disk
– foundation of security model
– fast packet/flow processing puts specific design pressures
• Instrumentation and management are additional virtualized
‘slices’
– distributed workload generation,
data collection
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Hard problems/challenges
• “Slice-ability” – multiple experimental services deployed over many
nodes
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Distributed Virtualization
Isolation & Resource Containment
Proportional Scheduling
Scalability
• Security & Integrity - remotely accessed and fully exposed
– Authentication / Key Infrastructure proven, if only systems were bug free
– Build secure scalable platform for distributed services
» Narrow API vs. Tiny Machine Monitor
• Management
– Resource Discovery, Provisioning, Overlay->IP
– Create management services (not people) and environment for innovation
in management
» Deal with many as if one
• Building Blocks and Primitives
– Ubiquitous overlays
• Instrumentation
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Emerging Extreme Internet
Wide-Area Broad-Coverage Services
DeeplyEmbedded
Networks
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Traditional pt-pt Internet
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backup
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Mission for the Network of Labs
• Bold new form of Industry-University collaboration that
reflects the changing nature of the information age.
• Conduct the highest quality research in emerging,
important areas of CS and IT.
• Join the unique strengths of Universities and the company
in concurrent, collaborative efforts that are both broad in
scope and deeply penetrating in exploration.
• Operate in a uniquely open fashion, promoting a powerful,
bidirectional exchange of groundbreaking ideas,
technology, and people.
• Leadership role in the creation of new research
ecosystems spanning the continuum from academic study
to product development.
• Labs will be project-focused with an active, constantly
evolving agenda involving Intel researchers, University
researchers, and members of the larger research
community
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Berkeley Focus
Extreme Interconnected Systems
• Invent, develop, explore, analyze, and understand
highly interconnected systems at the extremes of the
computing and networking spectrum - the very large,
the very small, and the very numerous
• Do leading-edge Computer Science on problems of
scale, cutting across traditional areas of architecture,
operating systems, networks, and languages to
enable a wide range of explorations in ubiquitous
computing, both embedded in the environment or
carried easily on moving objects and people
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Current Research Team
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Hans Mulder – co-director, IA64
Kevin Fall: UCSD, ISI, UCB,
NetBoost, Intel
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– Operating systems, Distributed
Computing, Infrastructure Services
– high speed ip networking
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Alan Mainwaring: TMC, UCB,
Sun, Intel
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– virtual networks, deep scalable
network systems
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David Gay: UCB
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Wei Hong, UCB, Illustra, Cohera,
PeopleSoft
Su Ping: Intel
Phil Buonodonna, UCB (abd
intern)
– Storage Area Networks, networks
Silvia Ratnasamy, UCB/ICSI (abd)
– Networking, P2P
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– Federated databases
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Matt Welsh, UCB (Post Doc)
– Operating Systems, internet service
design
Anind Dey: Georgia Tech, aware
house
– Prog. Lang. design/Imp for novel
comm. layers
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Brent Chun: UCB, CIT
– cluster systems, resource management
– framework for context aware applns,
ubicom
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Timothy Roscoe: Cambridge,
Sprint
Justin Tomilson, Part Time
– optimization, IEOR PhD Student
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Earl Hines – operations mgr
– Software Engineering, embedded
systems
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Eric Paulos: UCB
– HCI, robotics, ubicomp
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Additional Researchers
• Joe Hellerstein, Faculty Consultant (next AD)
– streaming database, sensor database, P2P
• Eric Brewer, Faculty Consultant
– systems, language design
• Larry Peterson, Consultant/Sabattical
• Deborah Estrin, Faculty consultant
– internet, multicast, rsvp,...sensor nets
• Paul Wright, Former Faculty consultant
– infopad, BWRC, cybercut
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Current Faculty Research Associates
• James Demmel
large-scale comp. sci
• Michael Franklin
Sensor Databases
• Steven Glaser
structural dynamics
• Joe Hellerstein
Streaming Databases
• John Kubiatowicz planetary storage
• James Landay
HCI
• David A Patterson Architecture
• Kris Pister
MEMS, Smart Dust
• Jan Rabaey
Low power systems
• Satish Rao
Distr. Systems Theory
• Ion Stoika
Networking
• Vivek Subramanian Disposable devices
• David Wagner
Security
• Kathy Yelick
Parallel Languages
• Jennifer Mankoff
HCI
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• Shankar Sastry
Distributed
Robotics
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