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Haggle Architecture. Erik Nordström , Christian Rohner. Haggle Project. 4 Year EU project 8 partners : Uppsala, Cambridge, Thomson, CNR, Eurecom , SUPSI, EPFL, LG (former Intel) Uppsala: Testbed (Virtual-APE) Architecture design and implementation People

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haggle architecture

Haggle Architecture

Erik Nordström, Christian Rohner

haggle project
Haggle Project
  • 4 Year EU project
  • 8 partners: Uppsala, Cambridge, Thomson, CNR, Eurecom, SUPSI, EPFL, LG (former Intel)
  • Uppsala:
    • Testbed (Virtual-APE)
    • Architecture design and implementation
  • People
    • Erik, Christian, Daniel, Fredrik
haggle ad hoc google
Haggle – “Ad hoc Google”

Community

“Search the neighborhood”

Opportunistic

Pocket-switched

searching and forwarding
Searching and Forwarding

Search for matching content

Search for matching content

1

Interests

2

3

4

Interests

4

3

2

1

haggle architecture invariants
Haggle Architecture Invariants
  • Data-centric
  • Application-layer framing (“data objects”)
  • Dissemination instead of conversation
  • Late binding
  • Asynchronous
architecture issues
Architecture Issues
  • Resolving “destinations”
    • Who and what is out there?
  • Interfacing
    • Physical
    • Language / Protocol
  • Content and priority
  • Forwarding

?

host centric vs data centric
Host-centric vs. Data-centric

news.bbc.co.uk

www.cnn.com

www.foxnews.com

news.google.com

a search based network architecture
A Search-based Network Architecture
  • Make searching a first class networking primitive
  • What does searching imply?
    • Unstructured (meta)data
    • Query - Keywords/interests
    • Ranked results
  • How can searching help us in a Haggle-style networking context?
searching in early haggle
“Searching” in Early Haggle

INS

  • INS-inspired namespace
    • Structured metadata
    • Hierarchical (name graph/tree)
  • Used to map from higher level name to lower level protocol/interface
    • Static, and pre-defined mappings
  • No searching – just lookup / tree traversal
  • How map data to user?
    • Implies destination oriented communication
searching on the desktop and the web
Searching on the Desktop and the Web
  • Consistent namespaces
    • Semantic filesystem (Gifford et al. 1991)
      • File attributes along file names
      • User explicitly adds metadata
    • Metadata extraction and indexing
  • Content-based search
    • Probabilistic models map metadata (term freq., language models) to search terms
  • Context enhanced search using graph models
    • Google’s PageRank
    • Connections (Soule et al. 2005)
haggle relation graph
Haggle Relation Graph
  • Each Haggle node maintains a relation graph
  • Vertices are data objects
  • Edges are relations = two data objects share an attribute
  • Primitives on the relation graph = network operations
  • Shares similarities with (local) search
    • E.g., Connections [Soules et. al 2006], Apple Spotlight, Google Desktop
relation graph1
Relation Graph
  • Computer
  • Beer
  • Film
  • Music
  • Haggle
  • Food
  • Haggle
  • Music
  • CoRe
  • Film
  • Beer
  • Computer
  • Uppsala
  • Cambridge
  • Haggle

2

3

1

1

1

2

2

1

1

  • Beer
  • Music
  • CoRe
  • Cambridge
  • Haggle

1

  • Food
  • Stockholm
  • Haggle
benefits of a search approach
Benefits of a Search Approach
  • Flexible “naming and addressing”
    • No e2e end-point identifiers
  • Late binding resolutions
  • Late binding demultiplexing
  • Content dissemination and forwarding
    • Ordered forwarding
    • Delegate forwarding and interest-based forwarding
  • Resource and congestion control
    • Limit queries – only get best matching content
filter local demultiplex
Filter – Local Demultiplex

Data object

Induced subgraph

Attribute

Demux = filtering associated with an actor

query weighting the graph
Query – Weighting the graph

There may be many ways to do the weighting!

cut in relation graph
Cut in Relation Graph

Ranked result = {v1,v2} || {v2,v1}

exchanging data objects
Exchanging Data Objects

Resolve data/content

Resolve node

  • Since content and nodes are both data objects, these two operations are (more ore less) the same
searching in haggle
Searching in Haggle
  • Use searching to resolve mappings between data and receivers
    • Analogy: Top 5 hits on Google
  • Content ranked (priority)
  • Results change with the content carried
conclusions
Conclusions
  • Search primitives are useful abstractions for DTN-style networking
  • Novel naming and addressing
  • Ranking useful for dissemination
    • Resource/congestion control
    • Ordered forwarding (priorities)
  • Better understanding of scaling needed
    • Query time
    • Effect on battery life?
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