Software Framework for Managing Heterogeneity in Mobile Collaborative Systems

1. Software Framework for Managing Heterogeneity in Mobile Collaborative Systems Carlos D. Correa, Ivan Marsic Rutgers University 2003

2. Abstract • Heterogeneity Difference in user’s interest Semantic Conflicts Disparate Device Capabilities • Consistency maintenance framework • Lossy graph mappings • Peer-to-peer scenario • Example applications • Evaluation Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

3. User’s Interest Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

4. Semantic Conflicts Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

5. Disparate Devices Workstation (lots of RAM/CPU) Network: High speed connection Interaction: Mouse/Tracker/Joystick PDA Wireless connection Stylus/Buttons Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

6. Consistency Maintenance Framework Overview Application 1 Application 2 Application 1 in PDA Application state Define Bi-directional Lossy Mappings • Lossy Mapping specification • Consistency Maintenance Algorithms Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

7. Super-graph Client graph desk desk (simplified) top drawer computer pencil leg box cone box cone box cone box box box computer pencil branch group branch group transform group transform group transform group transform group shape 1 shape 1 shape 2 shape 2 Lossy Graph Mappings Subgraph Mapping Vertex Contraction Path Merging Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

8. Property Mappers, User-defined Policies • Example, and inverse Position2D.value = (x,y), where Position3D.value = (x,y,z) Position3D.value = (x,y,0), where Position2D.value = (x,y) Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

9. Document Document Chapter Chapter SubChapter GROUP Paragraph Paragraph Paragraph GROUP SubChapter Paragraph Paragraph Paragraph Chapter Chapter Paragraph Paragraph GROUP Table Info Mapping Specification (2,1) (2,1) (10,2) (10,2) (50,3) (benefit,resources) Group nodes such that benefit is maximized and resources < R Rule: Group anything below depth=3 Automated Rule based Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

10. Peer-to-Peer Collaboration GS Client 1, 2 originally connected to Server G1 G2 GS M1c M2c Server sends smallest possible mapping Mic such that: G1 G2 Mic: Gi Gv (virtual server) Client 1, 2 can communicate with each other, as if a virtual server Gc was present. G1 G2 GV Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

11. Document Document Title Title Paragraph Paragraph Image Image SubTitle SubTitle Paragraph Paragraph Footnote Example Scenarios Interest Management and Filtering <DEFINE name="types" type="consistency.graph.SimpleSetImpl"> <ELEMENT>Document</ELEMENT> <ELEMENT>Table</ELEMENT> <ELEMENT>Paragraph</ELEMENT> <ELEMENT>Title</ELEMENT> <ELEMENT>Subtitle</ELEMENT> </DEFINE> <RULE> <BIND>types</BIND> <EXISTS object="x" set="types"> <EXPRESSION object="v" property="type" operator="equals" value="x"/> </EXISTS> <ACTION> <SETACTION object="v" set="subgraph" operator="insert"/> </ACTION> </RULE> Server graph Client graph Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

12. Example Scenarios (cont.) Interoperation of heterogeneous editors 2D Editor 3D Editor With the right mapping, simulate 3D view in a 2D editor Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

13. Example Scenarios (cont.) Constraint-based Simplification User WS Greedy simplification Fast, but… User’s utility NOT maximized Optimal simplification NOT so fast, but… User’s utility is maximized User1 PDA (impatient) User2 PDA (quality-demanding) Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

14. Implementation Clients Server Graph Mapping User Policies Property Mapping Topology Mapping Cli ent 1 graph Graph Mapping Rule - based Mapper Server Graph User Policies Automated Mapper Solver Property Mapping Client 2 graph Resource Benefit Topology Mapping Monitor Metric Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

15. Evaluation Resources Estimation (a) Rule-based mapping (b) Constraint-based mapping Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

16. Evaluation (cont.) Latency Latency = network_delay + tG + max { tR, tA } Clients Server Graph Mapping Network_delay tG Cli ent 1 graph tR Graph Mapping Rule - based Mapper Network_delay Server Graph tG Automated Mapper tA Client 2 graph Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

17. Evaluation (cont.) ADAPTATION TO LATENCY: AverageRTT = AverageRTT + (1 ) SampleRTT Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

18. Conclusions • Flexible Framework for Managing Heterogeneity: can be customized to support different applications. • Property Mappers/Policies/Rules • Not only provides interoperation of heterogeneous collaboraitve systems, can be used for resource adaptation: • Quality vs. resources tradeoff • Latency • Necessary in Mobile Systems Carlos D. Correa, Ivan Marsic. Rutgers University. 2003

19. Thank You! More Info: http://www.caip.rutgers.edu/disciple http://www.caip.rutgers.edu/~cdcorrea/gm Carlos D. Correa, Ivan Marsic. Rutgers University. 2003