Haggle architecture
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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

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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)


Relation graph

Relation Graph


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


Haggle architecture

Demo


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


Data object format

Data Object Format


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?


Weighting

Weighting


Query time

Query Time


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