Berkeley RAD Lab: Robust, Adaptive, Distributed Systems

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Berkeley RAD LabRobust, Adaptive, Distributed
Systems

• Armando Fox, Randy Katz, Michael Jordan, Dave
  Patterson, Scott Shenker, Ion Stoica
• November 2005

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RAD Lab

• The 5-year Vision
• Single person can go from vision to a
  next-generation IT service (the Fortune 1
  million)
• E.g., over long holiday weekend in 1995, Pierre
  Omidyar created Ebay v1.0
• The Vehicle
• Interdisciplinary Center creates core technical
  competency to demo 10X to 100X
• Researchers are leaders in machine learning,
  networking, and systems
• Industrial Participants leading companies in HW,
  systems SW, and online services
• Called RAD Lab for Reliable, Adaptable,
  Distributed systems

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RAD Lab
Cap Dado (The section of a pedestal between
cap and base) Base

• The Science
• Both shorter-term and longer-term solutions
• Develop using primitives ? functions (MapReduce),
  services (Craigslist)
• Assess/debug using deterministic replay and
  finding new metrics
• Deploy using Internet-in-a-Box via FPGAs under
  failure/slowdown workloads
• Operate using Statistical Learning
  Theory-friendly, Control Theory-friendly software
  architectures and visualization tools

• Added Value to Industrial Participants
• Working with leading people and companies from
  different industries on long-range,
  pre-competitive technology
• Training of dozens of future leaders of IT in
  multiple disciplines, and their recruitment by
  industrial participants
• Working with researchers with successful track
  record of rapid transfer of new technology

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Steps vs. Process

• Steps Traditional, Static Handoff Model, N groups

• Process SupportDADO Evolution, 1 group

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DADO - Develop

• Create abstractions, primitives, toolkit for
  large scale systems that make it easy to
  invent/deploy functions (e.g, MapReduce)
• For example, Distributed Hash Tables (OpenDHT)
• Already setting the trend for IETF standards

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DADO - Assess

• We improve what we can measure
• Inspect box visibility into networks, usually
  data poor
• Servers data rich data often discarded
• Statistical and Machine Learning (SML) to the
  rescue. It works well when
• You have lots of raw data
• You have reason to believe the raw data is
  related to some high-level effect youre
  interested in
• You dont have a model of what that relationship
  is
• Note SML advances ? fast analysis

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DADO - Deploy

• Re-engineer RAMP to act like 1000 node
  distributed system under realistic failure and
  slowdown workloads
• RAMP emulates data center wide area systems as
  well as MPP
• Collect and apply failure data from real world
• RAMP vs. Clusters Larger scale, easier to
  develop/debug, flexible HW/SW configuration,
  inexpensive so no need to share
• Explore via repeatable experiments as vary
  parameters, configurations vs. observations on
  single (aging) cluster that is often idiosyncratic

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DADO - Operate

• Idea when site misbehaves, users notice, and
  change their behavior use as failure detector
• Approach combine visualization with Statistical
  and Machine Learning analysis so operator see
  anomalies too
• Experiment does distribution of hits to various
  pages match the historical distribution?
• Each minute, compare hit counts of top N pages to
  hit counts over last 6 hours using Bayesian
  networks and ?2 test, real Ebates data

To learn more, see Combining Visualization and
Statistical Analysis to Improve Operator
Confidence and Efficiency for Failure Detection
and Localization, In Proc. 2nd IEEE Intl Conf.
on Autonomic Computing, June 2005, by Peter
Bodik, Greg Friedman, Lukas Biewald, Helen Levine
(Ebates,com), George Candea, Kayur Patel, Gilman
Tolle, Jon Hui, Armando Fox, Michael I. Jordan,
David Patterson.
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Account page problem
anomalyscore
Novel Visualization
I see and understand Winning operator trust
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Founding the RADLab Start 12/1

• Looking for 3 to 5 founding companies to fund 5
  years _at_ cost of 0.5M / year
• 25 grad students 15 undergrads 6 faculty 2
  staff
• Founding companies Google, Microsoft, Sun
  Microsystems
• RADS Consortium model
• Preference to founding partner technology in
  prototypes
• Designate employees to act as consultants
• Head start for participants on research results
• Putting IP in Public Domain so partners use not
  sued
• Press release of founding RAD Lab partners
  December 1?
• Mid project review after 3 years by founding
  partners

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RAD Lab Opportunity New Research Model

• Chance to Partner with the Top University in
  Computer Systems on the Next Great Thing
• National Academy of Engineering mentions Berkeley
  in 7 of 19 1B industries that came from IT
  research
• NAE mentions Berkeley 7 times, Stanford 5 Times,
  MIT 5, CMU 3 Timesharing (SDS 940), Client-Server
  Computing (BSD Unix), Graphics, Entertainment,
  Internet, LANs, Workstations, GUI, VLSI Design
  (Spice) ECAD 5B?/yr , RISC 10B?/yr ,
  Relational DB (Ingres/Postgres) RDB 15B?/yr,
  Parallel DB, Data Mining, Parallel Computing,
  RAID 15B?/yr , Portable Communication (BWRC),
  WWW, Speech Recognition, Broadband
• Berkeley one of the top suppliers of systems
  students to industry and academia
• US News World Report ranking of CS Systems
  universities 1 Berkeley, 2 CMU, 2 MIT, 4 Stanford

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RAD Lab Interdisciplinary Center for Reliable,
Adaptive, Distributed Systems

• Working with different industries on long-range,
  pre-competitive technology
• Training of dozens of future leaders of IT, plus
  their recruitment
• Working with researchers with track records of
  successful technology transfer

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Backup Slides
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References
To learn more, see

• Combining Visualization and Statistical Analysis
  to Improve Operator Confidence and Efficiency for
  Failure Detection and Localization, In Proc. 2nd
  IEEE Intl Conf. on Autonomic Computing, June
  2005, by Peter Bodik, Greg Friedman, Lukas
  Biewald, Helen Levine (Ebates,com), George
  Candea, Kayur Patel, Gilman Tolle, Jon Hui,
  Armando Fox, Michael I. Jordan, David Patterson.
• Microreboot -- A Technique for Cheap Recovery,
  George Candea, Shinichi Kawamoto, Yuichi Fujiki,
  Greg Friedman, and Armando Fox. Proc. 6th Symp.
  on Operating Systems Design and Implementation
  (OSDI), San Francisco, CA, Dec. 2004.
• Path-Based Failure and Evolution Management,
  Mike Y. Chen, Anthony Accardi, Emre Kiciman, Jim
  Lloyd, Dave Patterson, Armando Fox, and Eric
  Brewer In Proc. 1st USENIX/ACM Symp. on Networked
  Systems Design and Implementation (NSDI '04), San
  Francisco, CA, March 2004.
• "Scalable Statistical Bug Isolation," Ben Liblit,
  M. Naik, Alice. X. Zheng, Alex Aiken, and Micheal
  I. Jordan, PLDI, 2005.

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Sustaining Innovation/Training Engine in 21st
Century

• Replicate research centers based primarily on
  industrial funding to expand IT market and to
  train next generation of IT leaders
• Berkeley Wireless Research Center (BWRC) 50
  grad students, 30 undergrads _at_ 5M per year
• Stanford Network Research Center (SNRC) 50 Grad
  students _at_ 5M per year
• MIT Tparty 4M per year (100 from Quanta)
• Industry largely funds
• N companies, where N is 5?
• Exciting, long term technical vision
• Demonstrated by prototype(s)

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State of Research Funding Today

• Most industry research shorter term
• DARPA exiting long-term (exp.) IT research
• 03-05 BAAs IPTO 9 AI, 2 classified, 1 SW
  radio, 1 sensor net, 1 reliability, all have 12
  to 18 month go/no go milestones
• Academic led funding reduced 50 (so far) 2001 to
  2004
• Faculty consultants in consortia led by defense
  contractor, get grants support 1-2 students (
  NSF funding level)
• NSF swamped with proposals, conservative
• 2000 to 6500 proposals in 5 years
• IT has lowest acceptance rate at NSF (between 8
  to 16)
• Ambitious proposal is a negative review
• Even if get NSF funding, proposal reduced to
  stretch NSF e.g., got 3 x 1/3 faculty, 6 grad
  students, 0 staff, 3 years
• (To learn more, see www.cra.org/research)

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RAD Lab Timeline

• 2005 Launch RAD Lab 12/1
• 2006 Collect workloads, Internet in a Box
• 2007 SLT/CT distributed architectures, Iboxes,
  annotative layer, class testing
• 2008 Development toolkit 1.0, tuple space, class
  testing Mid Project Review
• 2009 RAD Lab software suite 1.0, class testing
• 2010 End of Project Party