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Haggle Architecture and Reference Implementation

This presentation discusses the architectural challenges of ad-hoc networking, particularly in heterogeneous environments where users interact opportunistically and carry their information. It proposes a search-based network architecture that prioritizes searching as a first-class primitive. By utilizing unstructured metadata, hierarchical name graphs, and efficient query mechanisms, it explores how enhanced searching can facilitate content discovery, improve naming and addressing, and optimize resource usage in a Haggle-style context. We also examine the implications for content dissemination and congestion control, providing insights into better network functionality.

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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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