Intel Research @ Berkeley and Extreme Networked Systems www.intel-research.net/berkeley

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Intel Research _at_ Berkeleyand Extreme Networked
Systemswww.intel-research.net/berkeley

• David Culler
• 8/12/2002

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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
• 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.
• 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
SRPs
Novel component technology
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
• 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
• enabling technology
• create the community
• core architecture, OS, networking, service
  foundations
• demonstrate revolutionary applications
• Create an Open Laboratory for Widely-distributed
  Planetary Scale Services to explore
  architecture, services and applications
• 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
mgmt / diag / debug
algorithm / theory
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Deeply Embedded Networks

• nodes gtgt people
• sensor/actuator data stream
• unattended
• inaccessible
• prolonged deployment
• energy constrained
• operate in aggregate
• in-network processing necessary
• what they do changes over time
• gt 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
• Approach
• joined in the series of UCB COTS mote designs
• WeC -gt Rene -gt iDot -gt MICA
• look to silicon for full architecture
• New ideas
• rich interfaces allow radical system
  optimizations
• analog wake-up, Tx-Rx time synch
• federation of accelerators, not dedicate protocol
  proc.
• HW/SW multithreading for low power, passive
  vigilance

application
service
data mgmt
network
system
architecture
technology
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Berkeley Wireless Sensor Motes
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TinyOS Application Graph
Route map
router
sensor appln
application
Active Messages
Radio Packet
Serial Packet
packet
Temp
photo
SW
Example self-organized ad- hoc, multi-hop
routing of photo sensor readings
HW
UART
Radio byte
ADC
byte
3450 B code 226 B data
clocks
RFM
bit
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
• 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
http//www.greatduckisland.net
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Cross-cutting issues?

• Programming environments
• Deep scalable simulation
• Algorithm behavior at scale
• Operating on prob. distributions
• Fine-Grain Inverse problems
• Pseudo-imaging
• Constructive foundations of self-organization

application
service
data mgmt
prog / data model
network
mgmt / diag / debug
algorithm / theory
system
architecture
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
• Established internet crossroads colos
• Web Services / Utility Computing
• Grid authentication infrastructure
• Packet processing,
• Anets, .... layer 7 switches, NATs, firewalls
• Internet instrumentation

The Time is NOW
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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 gt 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 seti_at_home model should be
  considered at some stage
• may be essential for some services

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Initial Researchers (mar 02)
http//www.planet-lab.org/

• 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

Intel Research David Culler Timothy Roscoe Sylvia
Ratnasamy Gaetano Borriello Satya Milan
Milenkovic Duke Amin Vadat Jeff
Chase Princeton Larry Peterson Randy Wang Vivek
Pai
Rice Peter Druschel Utah Jay Lepreau CMU Srini
Seshan Hui Zhang UCSD Stefan Savage Columbia Andre
w Campbell ICIR Scott Shenker Mark Handley Eddie
Kohler
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Initial Planet-Lab Candidate Sites
Uppsala
Copenhagen
UBC
UW
Cambridge
WI
UPenn
Chicago
Amsterdam
Harvard
Utah
Intel Seattle
Tokyo
Karlsruhe
MIT
Intel
Intel OR
Beijing
Barcelona
Intel Berkeley
Cornell
CMU
ICIR
Princeton
UCB
Columbia
St. Louis
Duke
UCSB
Washu
KY
UCLA
GIT
Rice
UCSD
UT
ISI

Melbourne

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ApproachService-Centric Virtualization

• Virtual Machine Technology has re-emerged for
  hosting complete desktop environments on
  non-native OSs 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
• 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-gtIP
• 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
Deeply- Embedded Networks
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

• Hans Mulder co-director, IA64
• Kevin Fall UCSD, ISI, UCB, NetBoost, Intel
• high speed ip networking
• Alan Mainwaring TMC, UCB, Sun, Intel
• virtual networks, deep scalable network systems
• Anind Dey Georgia Tech, aware house
• framework for context aware applns, ubicom
• David Gay UCB
• Prog. Lang. design/Imp for novel comm. layers
• Wei Hong, UCB, Illustra, Cohera, PeopleSoft
• Federated databases
• Su Ping Intel
• Software Engineering, embedded systems
• Eric Paulos UCB
• HCI, robotics, ubicomp

• Timothy Roscoe Cambridge, Sprint
• Operating systems, Distributed Computing,
  Infrastructure Services
• Brent Chun UCB, CIT
• cluster systems, resource management
• Matt Welsh, UCB (Post Doc)
• Operating Systems, internet service design
• Phil Buonodonna, UCB (abd intern)
• Storage Area Networks, networks
• Silvia Ratnasamy, UCB/ICSI (abd)
• Networking, P2P
• Justin Tomilson, Part Time
• optimization, IEOR PhD Student
• Earl Hines operations mgr

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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
• Shankar Sastry Distributed Robotics