1. Haggle Architecture Erik Nordström, Christian Rohner

2. 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

3. Haggle – “Ad hoc Google” Opportunistic Pocket-switched Community “Search the neighborhood”

4. Searching and Forwarding Interests Search for matching content 4 3 2 1 1 2 3 4

5. Haggle Scenario The scenario: – 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?

6. Haggle Architecture Invariants Data-centric Application-layer framing (“data objects”) Dissemination instead of conversation Late binding Asynchronous

7. Architecture Issues Resolving “destinations” – Who and what is out there? Interfacing – Physical – Language / Protocol Content and priority Forwarding ?

8. www.cnn.com news.bbc.co.uk www.foxnews.com Host-centric vs. Data-centric news.google.com

9. 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?

10. “Searching” in Early Haggle 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 RootServiceCameraResolution640x480Data-typePictureAccessibilityPublicCityWashingtonBuilding White house INS

11. 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)

12. Relation Graph

13. 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

14. Relation Graph Uppsala Cambridge Haggle Uppsala Cambridge Haggle Cambridge Haggle Cambridge Haggle Music Haggle Music Haggle Food Haggle Music CoRe Food Haggle Music CoRe Food Stockholm Food Stockholm Beer Music CoRe Beer Music CoRe Computer Beer Film Computer Beer Film Beer Computer Film Beer Computer 2 1 1 1 2 1 2 1 3 1

18. 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

19. Demo

20. Filter – Local Demultiplex Demux = filtering associated with an actor Data object Attribute Induced subgraph

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

22. Cut in Relation Graph Ranked result = {v 1,v 2 } || {v 2,v 1 }

23. Flexible Primitives Primitives exist and what they are We do Not define how exactly how they operate Different weighting algorithms

24. 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

25. Data Object Format

26. 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

27. 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?

29. Weighting

30. Query Time