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

1. Intel Research @ Berkeleyand Extreme Networked Systemswww.intel-research.net/berkeley David Culler 8/12/2002

2. 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 IRB/XIS

3. 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 IRB/XIS

4. 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 IRB/XIS

5. 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 IRB/XIS

6. 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 IRB/XIS

7. 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 IRB/XIS

8. 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) IRB/XIS

9. 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 IRB/XIS

10. System Stack for Deeply Embedded Networks IRB/XIS

11. Monitoring & Managing Spaces and Things application service data mgmt prog / data model network system architecture technology Bridging the Technology-Appln Gap mgmt / diag / debug algorithm / theory IRB/XIS

12. 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 IRB/XIS

13. 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 IRB/XIS

14. 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 -> Rene -> iDot -> 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 IRB/XIS

15. Berkeley Wireless Sensor ‘Motes’ IRB/XIS

16. TinyOS Application Graph Route map router sensor appln application Active Messages Serial Packet Radio 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 IRB/XIS

17. 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 IRB/XIS

18. Example “epidemic” tree formation IRB/XIS

19. LAN WAN (satcast) Acadia National Park Mt. Desert Island, ME sensor nets Great Duck Island Nature Conservancy Ongoing research Habitat Monitoring http://www.greatduckisland.net IRB/XIS

20. 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 IRB/XIS

21. The Other Extreme - Planetary Scale Services www.planet-lab.org IRB/XIS

22. 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 IRB/XIS

23. 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 IRB/XIS

24. Guidelines (1) • Thousand viewpoints on “the cloud” is what matters • not the thousand servers • not the routers, per se • not the pipes, per se IRB/XIS

25. Guidelines (2) • and you miust have the vantage points of the crossroads • primarily co-location centers IRB/XIS

26. 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” IRB/XIS

27. Guidelines (4) • 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 Management, Management, Management IRB/XIS

28. 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@home model should be considered at some stage • may be essential for some services IRB/XIS

29. http://www.planet-lab.org/ Initial Researchers (mar 02) • 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 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 • Rice • Peter Druschel • Utah • Jay Lepreau • CMU • Srini Seshan • Hui Zhang • UCSD • Stefan Savage • Columbia • Andrew Campbell • ICIR • Scott Shenker • Mark Handley • Eddie Kohler IRB/XIS

30. Initial Planet-Lab Candidate Sites Uppsala UBC Copenhagen UW Cambridge WI Chicago UPenn Amsterdam Harvard Utah Intel Seattle Karlsruhe Tokyo Intel MIT Intel OR Beijing Barcelona Intel Berkeley Cornell CMU ICIR Princeton UCB St. Louis Columbia Duke UCSB Washu KY UCLA GIT Rice UCSD UT ISI Melbourne IRB/XIS

31. 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 IRB/XIS

32. 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->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 IRB/XIS

33. Wide-Area Broad-Coverage Services Emerging Extreme Internet Deeply- Embedded Networks Traditional pt-pt Internet IRB/XIS

34. backup IRB/XIS

35. 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 IRB/XIS

36. 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 IRB/XIS

37. 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 Current Research Team IRB/XIS

38. 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 IRB/XIS

39. 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 IRB/XIS