China Mobile Leader

1
China Mobile Leaders ProgrammeMobile
TechnologyJon Crowcroft

• http//www.cl.cam.ac.uk/jac22
• Jon.crowcroft_at_cl.cam.ac.uk
• gmail, hotmail
• 441223763633
• 447733 231822
• linkedin, facebook, myspace

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4 Areas

• Mobile Social Networks
• Data Collection
• Energy
• Programming

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1. Online Mobile Social Nets Real Life
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We meet, we connect, we communicate

• We meet in real life in the real world
• We use text messages, phones, IM
• We make friends on facebook, Second Life
• How are these related?
• How do they affect each other?
• How do they change with new technology?

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Thank you but you are in the opposite direction!
I have 100M bytes of data, who can carry for me?
Give it to me, I have 1G bytes phone flash.
I can also carry for you!
Dont give to me! I am running out of storage.
Reach an access point.
There is one in my pocket
Internet
Search La Bonheme.mp3 for me
Finally, it arrive
Search La Bonheme.mp3 for me
Search La Bonheme.mp3 for me
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My facebook friendswheel
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My email statistics!
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Cliques and Communities
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Dunbars Number Trust

• Dunbars number-150 (for humans)
• Size of simple communities of humans
• Reflects ability to cope with group
• Humans gossip rather than physical grooming
• Language lets us abstract
• We can reason up to 5 levels of intentionality
• (Shakespear does 6 -)
• T 1 / 3.xN
• T is trust metric
• 3.x is a number between 3 and 4
• N is distance in social net

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Conjecture on N?

• N 0 Kin (sex)
• N 1 friends (beer/drugs)
• N 2 or more acquaintances (dancing/music/laugh
  ing at same jokes)
• How does this help in facebook?

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Conjecture on Online v. Real

• Were looking at co-lo networks
• c.f. haggle, cityware - bluetooth etc
• AND online social networks
• Friendship graph on orkut,li,facebook
• AND communication networks
• Email address book, sms, phonecalls
• Can use to infer real relationship
• I.e. type of edge in graph (and value of N)

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Conjectures on Trust

• Trust in terms of revelation/disclosure
• Or carrying data (in ferry net)
• Or simple automated/default grouping for ACLs
• Need to do some experiments
• Figure out how ties are broken
• Forgetting
• How new tools/technology affect
• Size and dynamics of social net

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EU Social Net Project Questions

• What net/edge type is more likely to cause an
  edge in another net?
• Does meeting someone dominate over online or vice
  versa -
• i.e. how does new tech affect x (size of
  immediate gang) and
• N (scope of gang/level of intentionality
  reasoning?)?
• Can you use this to detect dodgy behaviour (spam,
  bullying, etc)?

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Ongoing studies

• Data?
• We have large datasets for single
  edge-type/modality
• (6M phone call timeloc, 1M social net)
• But only very small datasets for 2 or 3
  modalities
• 30 army base people -gt retirement
• 100 school leavers -gt University
• Very heavy-lifting
• Not only lots of data processsing, but worse-
• Interview eahc user for context
• Privacy?
• Correlating (datamining) the different nets is
  massive breach of trust
• Usefulness?

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Usefulness?

1. Improve privacy
2. As mentioned, could auto-default Fb settings and
   relate to phone/locn
3. Could also use as interest based filter
4. Fundamental understanding of social groups
5. How society/technology co-evolve
6. Social inclusion and accessibility (!)
7. Epidemiology ()
8. Buzztraq
9. Use currency of local interest to
10. Fetch content

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Epidemiology

• Two projects -
• Emulation (ESRC)
• Run s/w on smart phone that mimics a disease
• Has a vector and SIR(!) parameter per person
• Run on real socieity based on meeting
  duration/proximity/frequency
• Flubook (Horizon)
• Panic button (Not well/Feelin better)
• Uploads list of contacts in last week via free
  SMS
• Puts anonymized data on google maps
• Alerts trusted friendship group on facebook

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SIR

• Susceptibility, Infectiousness, Recovery
• Given contact distribution,
• Can compute progress of epidemic
• Whether collapse (S, I low, R high)
• Or go pandemic (S, I high)
• As with relationship between online and RL
  behaviour for socialising,
• Flubook might alter contact rate
• .systematically for subset of population
• (social or geographic) with high S/I
• Help prevent/collapse epidemic

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Thank you

• Questions?

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And another thing

• Virtualising online social self
• Floating it in the cloud
• Crypt content, but allow cloud/fb to match
  interests (for advertising)
• Migrate it to track user (and handset)
• Performance gain
• handset can be meagre cpu/memory
• Latency reduced
• Synchronisation/persistence assured
• Dont care if handset lost/stolen -)

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Snakes (and Ladders) on a Plane

• Human
• Node
• World

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Threads of your life

• Human level is activities relationships
• Nodal level is processing and storage
• World level is location and context

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Idea is

• To allow mobile (compact/portable) representation
  of your activities and relationships (0wned by
  ou)
• Roam across arbitrary nodes in environment
  (embedded or handset owned by anyone)
• While recording where you are and context (
  other people)

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2. Data Collection for Modelling Contact Networks

• Eiko Yoneki and Jon Crowcroft
• eiko.yoneki_at_cl.cam.ac.uk
• Systems Research Group
• University of Cambridge Computer Laboratory

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Outline

• Purposes of Data Collection
• ? Modelling Human Contact Networks
• Proximity Data Collection Methodology
• Issues for Data Collection
• Examples of Data Analysis
• Extending to Collect/Correlate Online Data
• Conclusion

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Purpose of Data Collection

• Building communication protocol based on
  proximity
• EU FP6 Haggle Project
• Inferring social interaction, opinion dynamics ?
  Apply results to networking and computer systems
• EU FP7 Socialnets, EU FP7 Recognition
• Network modelling for epidemiology
• EPSRC Data Driven Network Modelling for
  Epidemiology
• Understanding behaviour to infectious disease
  outbreak - social and economic influences
• ESRC FluPhone Project

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Haggle Pocket Switched Networks

• Networked distributed database over
  opportunistically connected devices (e.g. Mobile
  phones)

Legacy network (e.g. the Internet)
Ex. Haggle Twitter
EU FP6 Haggle http//www.haggleproject.org
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FluPhone Project

• Understanding behavioural responses to infectious
  disease outbreaks
• Extending data collection to general public
• https//www.fluphone.org

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Purpose of Data Collection

• Robust data collection from real world
• Post-facto analysis and modelling yield insight
  into human interactions
• Data is useful from building communication
  protocol to understanding disease spread

Modelling Contact Networks Empirical Approach
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Proximity Data Collection

• Sensor board (iMote), mobile phone
• Proximity detection by Bluetooth, and/or GPS
• Environmental information (e.g. in train, on road)

AroundYou
iMote
FluPhone
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Proximity Detection by Bluetooth

• Only 15 of devices Bluetooth on
• Scanning Interval
• 2 mins iMote (one week battery life)
• 5 mins phone (one day battery life)
• or continuous scanning by station nodes
• Bluetooth inquiry (e.g. 5.12 seconds) gives gt90
  chance of finding device
• Complex discovery protocol
• Two modes discovery and being discovered
• 510m discover range

Can it produce reliable data (negligible noise)?
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Sensor Board or Phone or ...

• iMote needs disposable battery
• Expensive
• Third world experiment
• Mobile phone
• Rechargeable
• Additional functions (messaging, tracing)
• Smart phone location assist applications
• Provide device or software
• Combine with online information (e.g. Twitter)

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Phone Price vs Functionality

• lt20 GBP range
• Single task (no phone call when application is
  running)
• gt100 GBP
• GPS capability
• Multiple tasks run application as a background
  job
• Challenge to provide software for every operation
  system of mobile phone

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Location Data

• Location data necessary?
• Ethic approval gets tougher
• Use of WiFi Access Points or Cell Towers
• Use of GPS but not inside of buildings
• Infer location using various information
• Online Data (Social Network Services, Google)
• Us of limited location information Post
  localisation

Scanner Location in Bath
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Target Population

• Provide devices to limited population or target
  general public
• For epidemiology study 100 coverage may be
  required
• Fluphone project participants will be general
  public
• Or school as mixing centres

?
?
?
?
?
?
?
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Experiment Parameters vs Data Quality

• Battery life vs Granularity of detection interval
• Duration of experiments
• Day, week, month, or year?
• Data rate
• Data Storage
• Contact /GPS data lt50K per device per day (in
  compressed format)
• Server data storage for receiving data from
  devices
• Extend storage by larger memory card
• Collected data using different parameters or
  methods ? aggregated?

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Data Retrieval Methods

• Retrieving collected data
• Tracking station
• Online (3G, SMS)
• Uploading via Web
• via memory card
• Incentive for participating experiments
• Collection cycle real-time, day, or week?

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Data Transformation for Analysis

• Transform to discrete version of contact data
• Deal with noise and missing data
• Ex. transitivity closure
• Data analysis requires high performance computer
  and storage
• Low volume - raw data in compact format
• Transformation of raw data for analysis increases
  data volume

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Security and Privacy

• Current method Basic anonymisation of identities
  (MAC address)
• FluPhone Project use of HTTPS for data
  transmission via 3G
• Anonymising identities may not be enough?
• Simple anonymisation does not prevent to be found
  the social graph
• Ethic approval tough!
• 40 pages of study protocol document for FluPhone
  project took several months to get approval

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Consent
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Human Connectivity Traces

• Capture Human Interactions
• ..thus far not large scale
• Crawdad DB http//crawdad.cs.dartmouth.edu/
• Contact 025d04b2b3f 4650000025d0
  5416492246711621549 5416492246711644527
• Location 0025d0e113da lon -3.384610278596745E12
  5 lat 1.3168305280597862E182 506661995017043176
  3

HAGGLE
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Regularity of Network Activity

• Size of largest connected nodes shows network
  dynamics

Tuesday
5 Days
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Inter Contact Time of Pair Nodes

• Power law distribution ( exponential decay)

Time
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Classification of Node Pairs

• I Community
• High Frequency - Long Duration
• II Familiar Stranger
• High Frequency - Short Duration
• III Stranger
• Low Frequency Short Duration
• IV Friend
• Low Frequency - High Duration

I
II
Number of Contact
III
IV
Contact Duration
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Betweenness Centrality

• Frequency of a node that falls on the shortest
  path between two other nodes

MIT
Cambridge
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Uncovering Community

• Contact trace in form of weighted (multi) graphs
• Contact Frequency and Duration
• Use community detection algorithms from complex
  network studies
• K-clique, Weighted network analysis, Betweenness,
  Modularity, Fiedler Clustering etc.

Fiedler Clustering
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• Visualisation of Community Dynamics

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Extending Data Collection to OSN

• Online Social Networks (e.g. Facebook, Twitter)
• Potential to obtain data of dynamic behaviour
• High volume of data
• Does Facebook matter?
• Over 190 M users
• Growth rates for 2008 around the world
• Italy 2900, Argentina 2000, Indonesia 600

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Power Law Degree Distribution

• Crawled original Stanford (15043 Nodes), Harvard
  (18273 nodes) networks
• From era when UIDs assign sequentially
• Obtains friends of each user, and their
  affiliations
• 2.1 million links, Maximum degree 911

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Information Cascade thru Social Networks

• Use Google geo-coding API - predict the
  geographical access patterns
• T0........................................
  ........Tk

Texas
Illinois
Florida
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Conclusions

• Real World Data is Powerful!
• Analyse Network Structure of Social Systems to
  Model Dynamics ? Emerging Research Area
• Weighted networks
• Modularity
• Centrality (e.g. Degree)
• Community evolution and dynamics
• Network measurement metrics
• Patterns of interactions
• Plan purpose of data collection first that leads
  to decide data collection method
• Solve ethic issues/approval in advance
• Combine data collection using device and
  available online data for efficiency and accuracy
  

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Conclusions

• Real World Data is Powerful!
• Analyse Network Structure of Social Systems to
  Model Dynamics ? Emerging Research Area
• Weighted networks
• Modularity
• Centrality (e.g. Degree)
• Community evolution and dynamics
• Network measurement metrics
• Patterns of interactions
• Plan purpose of data collection first that leads
  to decide data collection method
• Solve ethic issues/approval in advance
• Combine data collection using device and
  available online data for efficiency and accuracy
  

Thank You!
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3. Challenging Opportunities

• Jon Crowcroft,
• http//www.cl.cam.ac.uk/jac22

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History (personal-)

• Manet
• Mobileman
• Tschudin et al
• Incredibles
• Dtn
• Interplanetary/Oceanographic
• Pocket Switched Mobile Social
• Oppnet
• Drive-Thru
• Disaster

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Choosing Adversity

• Perverse, but valid research motive
• Make the network really really bad
• (like it was in 1970s)
• And maybe neat new ideas will emerge
• Which will work really, really well on a
  rock-solid network

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Compete with Infrastructure

• They have the guns, we have the numbers
• But maybe opportunities give us information the
  infrastructure guys cant or wont get

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Incentives

• Hard to compute
• Mostly assume rational selfish players
• Recent market failures prove this is nonsense
• What to do instead?
• Use a priori social knowledge
• Travel plans, SIM, Fb/Buzz data

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Privacy and Risk Aversion

• May be over sold
• Known younger people are more cavalier with
  their online presence than older (pre web)
  generation
• But needs respect
• at least informed choice (opt out) by user
• Prob. With idloc is it is 2/3 of what you need
  to find out everything
• (2 digits of postcode, age gender)
• There may be some trigger event which will change
  public view

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Back to drawing board 0

• Information theory and opportunities
• What can we infer
• popularity in meeting
• Popularity in communicating
• Hub/centrality
• Clique/giant component
• Predictive patterns of behaviour
• Latest barabasi science paper on locn
• Other?

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Back to drawing board 1

• Non rational players
• Tools to measure adapt to
• Herding
• Cascading
• Opinion dynamics

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Back to drawing board 2

• One small step at a time
• Pair of nodes -
• why share anything?
• Whats useful
• What does it cost
• Micro-research agenda

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Share between just 1 pair of phones

• Now a phone is much more than a computer
• GPS, Camera, Mike,
• Compass, Accelerometer
• several networks
• Several (heterogeneous) cores in processor
• We could share these
• e.g. lots of people taking panoramic tiled
  photos,
• or 1 GPS providing lots of people with location

62
Lets look at actual resource costs

• Phone OS now about same as Desktop
• Android Linux
• Iphone OSX
• Windows Mobile 6 (actually Windows 7!)
• Etc etc
• Software uses resources too
• E.g. Java garbage collector surprise
• Power/network aware applications

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Narseos results

• Weve started looking at resource use in battery
  terms
• Calibrate OS tools for battery charge reporting
• By opening up phone and putting probe on
  battery)
• Then run experiment with lots of users

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Principal components on bs phone
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Principal components on Ts phone
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Ns phone charging correlogram
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Ns cell location correlogram
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Ns screen on correlogram
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Js interaction v. location
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Js net usage by location
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PCA Analysis
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Average principal components
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Fooling the user

• Buzz/Mobile Social
• Driving License
• Smart Badges)

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Back to Drawing Board 3

• What business model fools user best?
• What are the ethics?
• Buzz was first big bang social mix
• Take 1 network (gmail contacts, sorted by
  frequency of interaction)
• And bootstrap another with it
• How big a cognitive dissonance would this be to
  do on an opportunistic net?
• Without informed consent, would cause major major
  headaches
• Possibly illegal viz healthcare workers

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Acknowledgements

• Thanks to MSR for a bunch of WiMo phones
• Thanks to Google for a bunch of Android phones
• Thanks to volunteers in Cambridge for abandoning
  almost all privacy -)

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Questions

• Do we need both the guns and the numbers?
• The truth is out there

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D3N 4 Programming Distributed Computation in
Pocket Switched Networks

• Eiko Yoneki, Ioannis Baltopoulos and Jon
  Crowcroft
• University of Cambridge Computer Laboratory
• Systems Research Group

Data Driven Declarative Networking
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Rise of Sparse Disconnected Networks

• Haggle EU FP6 New communication paradigm using
  dynamic interconnectedness http//www.haggle
  project.org
• Disconnected
• By necessity or design
• Mobile
• With enough mobility for some connectivity over
  time
• Path existing over time
• Data has to be delay tolerant
• Opportunistic Forwarding instead Routing
• 116

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Pocket Switched Networks

• Topology changes every time unit
• Node 35 is a hub

Human-to-Human Use of dynamic human
connectivity http//www.cl.cam.ac.uk/ey204/Haggl
e/Vis/
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Haggle Node Architecture

• Each node maintains a data store its current
  view of global namespace
• Persistence of search delay tolerance and
  opportunism
• Semantics of publish/subscribe and an
  event-driven asynchronous operation
• Multi-platform
• (written in C and C)
• Windows mobile
• Mac OS X, iPhone
• Linux
• Android

Unified Metadata Namespace
data
node
Append
Search
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D3N Data-Driven Declarative Networking

• How to program distributed computation?
• Use Declarative Networking ?

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Declarative Networking

• Declarative is new idea in networking
• e.g. Search what to look for rather than how
  to look for
• Abstract complexity in networking/data processing
• P2 Building overlay using Overlog
• Network properties specified declaratively
• LINQ extend .NET with language integrated
  operations for query/store/transform data
• DryadLINQ extends LINQ similar to Googles
  Map-Reduce
• Automatic parallelization from sequential
  declarative code
• Opis Functional-reactive approach in OCaml

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D3N Data-Driven Declarative Networking

• How to program distributed computation?
• Use Declarative Networking
• Use of Functional Programming
• Simple/clean semantics, expressive, inherent
  parallelism
• Queries/Filer etc. can be expressed as
  higher-order functions that are applied in a
  distributed setting
• Runtime system provides the necessary native
  library functions that are specific to each
  device
• Prototype F .NET for mobile devices

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D3N and Functional Programming I

• Functions are first-class values
• They can be both input and output of other
  functions
• They can be shared between different nodes (code
  mobility)
• Not only data but also functions flow
• Language syntax does not have state
• Variables are only ever assigned once hence
  reasoning about programs becomes easier
• (of course message passing and threads ? encode
  states)
• Strongly typed
• Static assurance that the program does not go
  wrong at runtime unlike script languages
• Type inference
• Types are not declared explicitly, hence programs
  are less verbose

85
D3N and Functional Programming II

• Integrated features from query language
• Assurance as in logical programming
• Appropriate level of abstraction
• Imperative languages closely specify the
  implementation details (how) declarative
  languages abstract too much (what)
• Imperative predictable result about performance
• Declarative language abstract away many
  implementation issues

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Overview of D3N Architecture

• Each node is responsible for storing, indexing,
  searching, and delivering data
• Primitive functions associated with core D3N
  calculus syntax are part of the runtime system
• Prototype on MS Mobile .NET

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D3N Syntax and Semantics I

• Very few primitives
• Integer, strings, lists, floating point numbers
  and other primitives are recovered through
  constructor application
• Standard FP features
• Declaring and naming functions through
  let-bindings
• Calling primitive and user-defined functions
  (function application)
• Pattern matching (similar to switch statement)
• Standard features as ordinary programming
  languages (e.g. ML or Haskell)

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D3N Syntax and Semantics II

• Advanced features
• Concurrency (fork)
• Communication (send/receive primitives)
• Query expressions (local and distributed select)

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D3N Language (Core Calculus Syntax)
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Runtime System

• Language relies on a small runtime system
• Operations implemented in the runtime system
  written in F
• Each node is responsible on data
• Storing
• Indexing
• Searching
• Delivering
• Data has Time-To-Live (TTL)
• Each node propagates data to the other nodes.
• A search query w/TTL travels within the network
  until it expires
• When the node has the matching data, it forwards
  the data
• Each node gossips its own metadata when it meets
  other nodes

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Kernel Event Handler

• Kernel maintains
• An event queue (queue)
• A list of functions for each event (fenc, fdep)
• Kernel processes
• It removes an event from the front of the queue
  (e)
• Pattern matches against the event type
• Calls all the registered functions for the
  particular event

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Example Query to Networks

• Queries are part of source level syntax
• Distributed execution (single node programmer
  model)
• Familiar syntax

D3N
select name from poll() where institute
Computer Laboratory
poll() gt filter (fun r -gt r.institute
Computer Laboratory) gt map (fun r -gt r.name)
F
E
C
A
B
Message
(code, nodeid, TTL, data)
D
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Example Vote among Nodes

• Voting application implements a distributed
  voting protocol of choosing location for dinner
• Rules
• Each node votes once
• A single node initiates the application
• Ballots should not be counted twice
• No infrastructure-base communication is available
  or it is too expensive
• Top-level expression
• Node A sends the code to all nodes
• Nodes map in parallel (pmap) the function
  voteOfNode to their local data, and send back the
  result to A
• Node A aggregates (reduce) the results from all
  nodes and produces a final tally

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Sequential Map function (smap)

• Inner working
• It sends the code to execute on the remote node
• It blocks waiting for a response waiting from the
  node
• Continues mapping the function to the rest of the
  nodes in a sequential fashion
• An unavailable node blocks the entire computation

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Parallel Map Function (pmap)

• Inner working
• Similar to the sequential case
• The send/receive for each node happen in a
  separate thread
• An unavailable node does not block the entire
  computation

A
pmap
C
E
F
G
B
D
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Reduce Function

• Inner working
• The reduce function aggregates the results from a
  map
• The reduce gets executed on the initiator node
• All results must have been received before the
  reduce can proceed

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Voting Application Code
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Cascaded Map Function

• Social Graph can be exploited for map function
• Logical topology extracted from social networks
• Construct a minimum spanning tree with node A
• Use tree as navigation of task

D
G
B
E
F
A
C
(a) Social Graph
D
D
B
B
E
E
A
F
C
(b) Nodes for Map at A
(c) Nodes for Map at B
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Outlook and Future Work

• Current reference implementation
• F targeting .NET platform taking advantage of a
  vast collection of .NET libraries for
  implementing D3N primitives
• Future work
• Security issues are currently out of the scope of
  this paper. Executable code migrating from node
  to node
• Validate and verify the correctness of the design
  by implementing a compiler targeting various
  mobile devices
• Disclose code in public domain
• http//www.cl.cam.ac.uk/ey204
• Email eiko.yoneki_at_cl.cam.ac.uk