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Haggle Architecture and Reference Implementation

Haggle Architecture and Reference Implementation. Uppsala, September 29-30 Erik Nordström , Christian Rohner. Haggle Scenario. The s cenario (you all know this): People carry information with them Ad hoc/opportunistic interactions Heterogeneous connectivity Architectural p roblems :

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Haggle Architecture and Reference Implementation

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  1. Haggle Architecture and Reference Implementation Uppsala, September 29-30 Erik Nordström, Christian Rohner

  2. Haggle Scenario • The scenario (you all know this): • People carry information with them • Ad hoc/opportunistic interactions • Heterogeneous connectivity • Architectural problems: • How to agree on names and addresses? • How to exchange information (protocols, tech.)? • How to prioritize the information to exchange?

  3. 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?

  4. “Searching” in Haggle INFANT 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

  5. 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)

  6. Haggle Scenario (contd.) Search for matching content Search for matching content 1 Interests 2 3 4 Interests 4 3 2 1

  7. Searching in Haggle • Use searching to resolve mappings between data and receivers

  8. Christian • Tryffel • Christian • Tryffel • Christian • Tryffel • Christian • Tryffel • Christian • Tryffel • Christian • Tryffel • Christian • Tryffel • Christian • Tryffel

  9. Relation Graph • Each Haggle node maintains a relation graph • Vertices are data objects • Edges are relations = two data objects share an attribute • We define our primitives on the relation graph • Shares similarities with (local) search • E.g., Connections [Soules et. al 2006], Apple Spotlight, Google Desktop

  10. Filter Induced subgraph Data object Attribute Demux = filtering associated with an actor

  11. Query – Weighting the graph There may be many ways to do the weighting!

  12. Resolve = Cut in Relation Graph Ranked result = {v1,v2} || {v2,v1}

  13. 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

  14. Search Benefits • Flexible naming and addressing • Late binding resolutions • Late binding demultiplexing • Content dissemination and forwarding • Deciding delegate forwarders • Ordered forwarding • Resource and congestion control • Limit queries – only get best matching content

  15. Query Time

  16. Weighting

  17. 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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