OceanStore%20Global-Scale%20Persistent%20Storage

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OceanStoreGlobal-Scale Persistent Storage

• John Kubiatowicz
• University of California at Berkeley

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Context Project EndeavourInterdisciplinary,
Technology-Centered Team

• Alex Aiken, PL
• Eric Brewer, OS
• John Canny, AI
• David Culler, OS/Arch
• Joseph Hellerstein, DB
• Michael Jordan, Learning
• Anthony Joseph, OS
• Randy Katz, Nets
• John Kubiatowicz, Arch
• James Landay, UI

• Jitendra Malik, Vision
• George Necula, PL
• Christos Papadimitriou, Theory
• David Patterson, Arch
• Kris Pister, Mems
• Larry Rowe, MM
• Alberto Sangiovanni-Vincentelli, CAD
• Doug Tygar, Security
• Robert Wilensky, DL/AI

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Endeavour Goals

• Enhancing human understanding
• Help people to interact with information,
  devices, and people - exploit Moores law growth
  in everything
• Enable new approaches for problem solving
  learning
• Figure of merit how effectively we amplify and
  leverage human intellect
• Enabling and exploiting ubiquitous computing
• Small devices, sensors, smart materials, cars,
  etc
• New methods for design, construction, and
  administration of ultra-scale systems
• Planetary-scale Information Utilities
• Infrastructure is transparent and always active
• Extensive use of redundancy of hardware and data
• Devices that negotiate their interfaces
  automatically
• Elements that tune, repair, and maintain
  themselves

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Endeavour Maxims

• Exploit Moores law growth for better behavior
• Use of excess capacity for better human
  interface
• Personal Information Mgmt is the Killer App
• Not corporate processing but management,
  analysis, aggregation, dissemination, filtering
  for the individual
• Automated extraction and organization of daily
  activities to assist people
• Time to move beyond the Desktop
• Community computing infer relationships among
  information, delegate control, establish
  authority
• Information Technology as a Utility
• Continuous service delivery, on a
  planetary-scale, on top of a highly dynamic
  information base

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Endeavour Approach

• Information Devices
• Beyond desktop computers to MEMS-sensors/actuators
  with capture/display to yield enhanced activity
  spaces
• Information Utility
• Information Applications
• High Speed/Collaborative Decision Making and
  Learning
• Augmented Smart Spaces Rooms and Vehicles
• Design Methodology
• User-centric Design withHW/SW Co-design
• Formal methods for safe and trustworthy
  decomposable and reusable components

• Fluid, Network-Centric System Software
• Partitioning and management of state between soft
  and persistent state
• Data processing placement and movement
• Component discovery and negotiation
• Flexible capture, self-organization, and re-use
  of information

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OceanStore Context Ubiquitous Computing

• Computing everywhere
• Desktop, Laptop, Palmtop
• Cars, Cellphones
• Shoes? Clothing? Walls?
• Connectivity everywhere
• Rapid growth of bandwidth in the interior of the
  net
• Broadband to the home and office
• Wireless technologies such as CMDA, Satelite,
  laser
• Rise of the thin-client metaphor
• Services provided by interior of network
• Incredibly thin clients on the leaves
• MEMs devices -- sensorsCPUwireless net in 1mm3
• Mobile society people move and devices are
  disposable

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Questions about information

• Where is persistent information stored?
• 20th-century tie between location and content
  outdated (we all survived the Feb 29th bug --
  lets move on!)
• In world-scale system, locality is key
• How is it protected?
• Can disgruntled employee of ISP sell your
  secrets?
• Cant trust anyone (how paranoid are you?)
• Can we make it indestructible?
• Want our data to survive the big one!
• Highly resistant to hackers (denial of service)
• Wide-scale disaster recovery
• Is it hard to manage?
• Worst failures are human-related
• Want automatic (introspective) diagnose and
  repair

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First ObservationWant Utility Infrastructure

• Mark Weiser from Xerox Transparent computing is
  the ultimate goal
• Computers should disappear into the background
• In storage context
• Dont want to worry about backup
• Dont want to worry about obsolescence
• Need lots of resources to make data secure and
  highly available, BUT dont want to own them
• Outsourcing of storage already becoming popular
• Pay monthly fee and your data is out there
• Simple payment interface ? one bill from one
  company

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Second ObservationNeed wide-scale deployment

• Many components with geographic separation
• System not disabled by natural disasters
• Can adapt to changes in demand and regional
  outages
• Gain in stability through statistics
• Difference between thermodynamics and mechanics?
  surprising stability of temperature and pressure
  given 1030 molecules with highly variable
  behavior!
• Wide-scale use and sharing also requires
  wide-scale deployment
• Bandwidth increasing rapidly, but latency bounded
  by speed of light
• Handling many people with same system leads to
  economies of scale

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OceanStoreEveryones data, One big Utility

• The data is just out there
• Separate information from location
• Locality is an only an optimization (an important
  one!)
• Wide-scale coding and replication for durability
• All information is globally identified
• Unique identifiers are hashes over names keys
• Single uniform lookup interface replaces DNS,
  server location, data location
• No centralized namespace required (such as SDSI)

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Basic StructureIrregular Mesh of Pools
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Amusing back of the envelope calculation(courtesy
Bill Bolotsky, Microsoft)

• How many files in the OceanStore?
• Assume 1010 people in world
• Say 10,000 files/person (very conservative?)
• So 1014 files in OceanStore!
• If 1 gig files (not likely), get 1 mole of files!
• Truly impressive number of elements
• but small relative to physical constants

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Utility-based Infrastructure
Canadian OceanStore
Sprint
ATT
IBM
Pac Bell
IBM

• Service provided by confederation of companies
• Monthly fee paid to one service provider
• Companies buy and sell capacity from each other

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Outline

• Motivation
• Properties of the OceanStore and Assumptions
• Specific Technologies and approaches
• Conflict resolution on encrypted data
• Replication and Deep archival storage
• Naming and Data Location
• Introspective computing for optimization and
  repair
• Economic models
• Conclusion

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Ubiquitous Devices ? Ubiquitous Storage

• Consumers of data move, change from one device to
  another, work in cafes, cars, airplanes, the
  office, etc.
• Properties REQUIRED for OceanStore storage
  substrate
• Strong Security data encrypted in the
  infrastructure resistance to monitoring and
  denial of service attacks
• Coherence too much data for naïve users to keep
  coherent by hand
• Automatic replica management and optimization
  huge quantities of data cannot be managed
  manually
• Simple and automatic recovery from disasters
  probability of failure increases with size of
  system
• Utility model world-scale system requires
  cooperation across administrative boundaries

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State of the Art?

• Widely deployed systems NFS, AFS (/DFS)
• Single regions of failure, caching only at
  endpoints
• ClearText exposed at various levels of system
• Compromised server?? all data on server
  compromised
• Mobile computing community Coda, Ficus, Bayou
• Small scale, fixed coherence mechanism
• Not optimized to take advantage of high-bandwidth
  connections between server components
• ClearText also exposed at various levels of
  system
• Web caching community Inktomi, Akamai
• Specialized, incremental solutions
• Caching along client/server path, various
  bottlenecks
• Database Community
• Interfaces not usable by legacy applications
• ACID update semantics not always appropriate

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OceanStore Assumptions

• Untrusted Infrastructure
• The OceanStore is comprised of untrusted
  components
• Only cyphertext within the infrastructure
• Information must not be leaked over time
• Principle Party
• There is one organization that is financially
  responsible for the integrity of your data
• Mostly Well-Connected
• Data producers and consumers are connected to a
  high-bandwidth network most of the time
• Exploit multicast for quicker consistency when
  possible
• Promiscuous Caching
• Data may be cached anywhere, anytime
• Operations Interface with Conflict Resolution
• Applications employ an operations-oriented
  interface, rather than a file-systems interface
• Coherence is centered around conflict resolution

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OceanStore Technologies INaming and Data
Location

• Requirements
• System-level names should help to authenticate
  data
• Route to nearby data without global communication
• Dont inhibit rapid relocation of data
• OceanStore approach Two-level search with
  embedded routing
• Underlying namespace is flat and built from
  secure cryptographic hashes (160-bit SHA-1)
• Search process combines quick, probabilistic
  search with slower guaranteed search
• Long-distance data location and routing are
  integrated
• Every source/destination pair has multiple
  routing paths
• Continuous, on-line optimization adapts for hot
  spots, denial of service, and inefficiencies in
  routing

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Universal Location Facility

• Takes 160-bit unique identifier (GUID) and
  Returns the nearest object that matches

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Some current results

• Have a working algorithm for local search
• Uses attenuated bloom filters
• Performs search by passing messages from node to
  node. All state kept in messages!
• Updates filters through semi-chaotic passing of
  information between neighbors
• Resembles compiler dataflow algorithm
• Can be shown to converge
• Have candidate for backing store index
• Randomized data structure with locality
  properties
• Every document has multiple roots in the
  OceanStore
• Searches close to copy tend to find copy
  quickly
• Redundant, insensitive to faults, and repairable
• Investigating algorithms to continually adapt
  routing structure to adjust for faults and denial
  of service

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OceanStore Technologies IIRapid Update in an
Untrusted Infrastructure

• Requirements
• Scalable coherence mechanism which can operate
  directly on encrypted data without revealing
  information
• Handle Byzantine failures
• Rapid dissemination of committed information
• OceanStore Approach
• Operations-based interface using conflict
  resolution
• Modeled after Xerox Bayou ? updates packets
  includePredicate/update pairs which operate on
  encryped data
• Use of oblivious function techniques to perform
  this update
• Use of incremental cryptographic techniques
• User signs Updates and principle party signs
  commits
• Committed data multicast to clients

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Tentative UpdatesEpidemic Disemination
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Committed UpdatesMulticast Dissemination
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Our State of the Art

• Have techniques for protecting metadata
• Uses encryption and signatures to provide
  protection against substitution attacks
• Provides secure pointer technology
• Have a working scheme that can do some forms of
  conflict resolution directly on encryped data
• Uses new technique for searching on encrypted
  data.
• Can be generalized to perform optimistic
  concurrency, but at cost in performance and
  possibly privacy
• Byzantine assumptions for update commitment
• Signatures on update requests from clients
• Compromised servers are unable to produce valid
  updates
• Uncompromised second-tier servers can make
  consistent ordering decision with respect to
  tentative commits
• Use of threshold cryptography in inner-tier of
  servers
• Signatures on update stream from inner-tier
• Use of chained MACs to reduce overhead

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OceanStore Technologies IIIHigh-Availability
and Disaster Recovery

• Requirements
• Handle diverse, unstable participants in
  OceanStore
• Mitigate denial of service attacks
• Eliminate backup as independent (and fallible)
  technology
• Flexible disaster recovery for everyone
• OceanStore Approach
• Use of erasure-codes to provide stable storage
  for archival copies and snapshots of live data
• Mobile replicas are self-contained centers for
  logging and conflict resolution
• Version-based update for painless recovery
• Continuous introspection repairs data structures
  and degree of redundancy

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Floating Replicas and Deep Archival Coding

• Floating Replicas are per-object virtual servers
• Complete copy of data
• logging for updates/conflict resolution
• Interaction with other centers to keep data
  consistent
• May appear and disappear like bubbles
• Erasure coded fragments provide very stable store
• Multi-level codes spread over 1000s of nodes
• Could lose 1/2 of nodes and still recover data
• Archive old versions of data and checkpoints
• Inactive data may only be in erasure-coded form

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Floating Replica and Deep Archival Coding
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Structure of Archival Checkpoints
Checkpoint Reference (GUID)
. . . . .
NOTE Each Block needs a GUID
Blocks

• All blocks and fragments signed
• Copy on Write behavior
• Older metablocks fragmented also

Unit of Archival Storage
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Proactive Self-Maintenance

• Continuous testing and repair of information
• Slow sweep through all information to make sure
  there are sufficient erasure-coded fragments
• Continuously reevaluate of risk and redistribute
  data
• Slow sweep and repair of metadata/search trees
• Continuous online self-testing of HW and SW
• detects flaky, failing, or buggy components via
• fault injection triggering hardware and software
  error handling paths to verify their
  integrity/existence
• stress testing pushing HW/SW components past
  normal operating parameters
• scrubbing periodic restoration of potentially
  decaying hardware or software state
• automates preventive maintenance

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OceanStore Technologies IVIntrospective
Optimization

• Requirements
• Reasonable job on global-scale optimization
  problem
• Take advantage of locality whenever possible
• Sensitivity to limited storage and bandwidth at
  endpoints
• Repair of data structures, increasing of
  redundancy
• Stability in chaotic environment ? Active
  Feedback
• OceanStore Approach
• Introspective Monitoring and analysis of
  relationships to cluster information by
  relatedness
• Time series-analysis of user and data motion
• Rearrangement and replication in response to
  monitoring
• Clustered prefetching fetch related objects
• Proactive-prefetching get data there before
  needed
• Rearrangement in response to overload and attack

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Example Client Introspection

• Client observer and optimizer components
• greedy agents working on the behalf of the client
• Watches client activity/combines with historical
  info
• Performs clustering and time-series analysis
• forwards results to infrastructure (privacy
  issues!)
• Monitoring of state of network to adapt behavior
• Typical Actions
• cluster related files together
• prefetch files that will be needed soon
• Create/destroy floating replicas

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OceanStore Technologies VThe oceanic data market

• Properties
• Utility providers have resources (storage and
  bandwidth)
• Clients use resources both directly and
  indirectly
• Use of data storage and bandwidth on demand
• Data movement on behalf of users
• Some customers are more important than others
• Techniques that we are exploring (very
  preliminary)
• Data market driven by principle party
• Tradeoff between performance (replication) and
  cost
• Secure signatures on data packets permit
• Accounting of bandwidth and CPU utilization
• Access control policies (Bays in OceanStore
  nomenclature)
• Use of challenge-response protocols (similar to
  zero-knowledge proofs) to demonstrate possession
  of data

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Two-Phase Implementation

• This term Read-Mostly Prototype
• Construction of data location facility
• Initial introspective gathering of tacit info and
  adaptation
• Initial archival techniques (use of erasure
  codes)
• Unix file-system interface under Linux (legacy
  apps)
• Later? Full Prototype
• Final conflict resolution and encryption
  techniques
• More sophisticated tacit info gathering and
  rearrangement
• Final object interface and integration with
  Endeavour applications
• Wide-scale deployment via NTON and Internet-2

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OceanStore Conclusion

• The Time is now for a Universal Data Utility
• Ubiquitous computing and connectivity is (almost)
  here!
• Confederation of utility providers is right model
• OceanStore holds all data, everywhere
• Local storage is a cache on global storage
• Provides security in an untrusted infrastructure
• Large scale system has good statistical
  properties
• Use of introspection for performance and
  stability
• Quality of individual servers enhances
  reliability
• Exploits economies of scale to
• Provide high-availability and extreme
  survivability
• Lower maintenance cost
• self-diagnosis and repair
• Insensitivity to technology changesJust unplug
  one set of servers, plug in others