1. Topic Based Data Distribution in GSpace
SACC
Software Architecture = Component + Coordination
Giovanni Russello
Department of Mathematics and Computing Science,
Eindhoven University of Technology

2. Implementation Domain
Overview SACC project
Separate functional and non-functional specification
Weaving them into a component-based model via a correctness preserving transformation/refinement
Specification Domain
Implementation Domain
Functionality
Platform C
Components
Sound Transformation
Coordination
Platform Information
Timing Constraints
Observing the implementation and providing feedback / hardware-related info
Implementing the component-based model,
managing distribution of process/data, distributed scheduling

3. The Gamma basic data structure: the Multiset
Set with multiple occurrence of the same element
Absence of any constraint or hierarchy imposition between its elements
Supports/enables chaotic model of execution

4. Gamma Multiset Graphical Representation

5. The Gamma reaction structure
max : x, y  y  x  y
R: condition
A: action

6. A possible approach: Space-Based System
The Space acts like a repository to store and retrieve data
All the same characteristics as the Multiset
A natural parallel and distributed programming style
Uncoupled communication in space and time
- CBD benefits from minimizing dependencies

7. A Space-Based Java implementation : JavaSpaces
Java programming language extension
Portability
Free code availability

8. JavaSpace examples: entry: objects present in the space
write: to insert an entry in the space
take: to retrieve and withdraw an entry from the space
read: to retrieve a copy of an entry present in the space

9. Mapping the Gamma model onto JavaSpaces An Example
x = take(xTemplate);
y = take(yTemplate);
if( x  y )
write(y);
else
write(x); fi
max : x, y  y  x  y

10. The JavaSpaces drawbacks
It is not distributed
It is not Real Time
It is expensive in terms of resources

11. Single Space Implementation
Space Server
C 1
C 2
C 4
C 3
Network

12. Distributed Space Implementation
Advantages:
Scalability: built into the architecture
Reliability: no single point of failure
Space
Middleware
C 1
Space
Middleware C2
Space
Middleware Cn
...
Distributed Space
Network

13. Our approach: GSpace Research hypothesis
Efficiency
Higher efficiency through differentiated distribution policies
Adaptability through Separation of concerns
Independence between system functionality and data distribution
joint work with Maarten van Steen

14. Initial Experiment
Separate distribution policies from storage mechanism
Compare differentiated distribution policies vs. single policy
Compare distributed storage vs. centralized storage

15. GSpace Overview GSpace C1 Host1 Storage GSpace C3 Host2 Storage GSpace
Network

16. GSpace Conceptual

17. GSpace APIs: put to insert an element in the space
get to obtain an element from the space
producer to declare that a component produces an element type
consumer to declare that a component needs such a type of element

18. How to Map Distribution Policy with Datatype
Distribution Policies:
Unreliable/Reliable & Push/Pull
Associate a Distribution Policy with a datatype
Distribute policy to each host (before system starts)
Controller incorporates Policies into its local PolicyTable

19. Collaboration Diagram: Unreliable Push Model Policy - Put
Producer Component
JavaSpace
8: overwrite(message)
PolicyTable
2: getPolicy(messageType)
Controller
1: put(message)
UnrPushPolicyManager
7: processing(message)
UnrPushPolicyManager
3: put(message)
AddressTable
4: getAddresses(messageType)
PolicyTable
6: getPolicy(messageType)
Communicator
5: sendUdp(message, addresses)
UdpReceiver
Flush the message over the network

20. Collaboration Diagram: Unreliable Push Model Policy - Get
Consumer Component
PolicyTable
2: getPolicy(messageType)
Controller
1: get(messageTemplate)
UnrPushPolicyManager
3: get(messageTemplate)
JavaSpace
4: take(messageTemplate)

21. Collaboration Diagram: Reliable Pull Model Policy - Put
Producer Component
PolicyTable
2: getPolicy(messageType)
Controller
1: put(message)
RelPullPolicyManager
3: put(message)
JavaSpace
4: overwrite(message)

22. Collaboration Diagram: Reliable Pull Model Policy - Get
Consumer Component
JavaSpace
8: take(messageTemplate)
Controller
1: get(messageTemplate)
PolicyTable
2: getPolicy(messageType)
RelPullPolicyManager
7: processing(messageTemplate)
RelPullPolicyManager
3: get(messageTemplate)
AddressTable
4: getAddresses(messageType)
PolicyTable
6: getPolicy(messageType)
Communicator
5: sendReceiveTcp(messageTemplate, addresses)
9: sendBack(message)
TcpReceiver
Send the message over the network

23. A Case Study: Traffic Management
Framework Overview
Aggregator
Computation Unit
Display
Road
Sensors

24. A Case Study: Traffic Management
Framework Implementation in GSpace

25. A Case Study: Traffic Management
Framework Implementation in JavaSpace

26. Case Study Result: GSpace Policy Setting1
Signal
Aggregator
Computation Unit
Display
1sec
Reliable Push
Reliable Push
Reliable Push
Average Time: 103 millisec

27. Case Study Graphic: GSpace Policy Setting1

28. Case Study Result : GSpace Policy Setting2
Signal
Aggregator
Computation Unit
Display
1sec
Reliable Pull
Reliable Push
Reliable Push
Average Time: 75.5 millisec

29. Case Study Graphic: GSpace Policy Setting2

30. Case Study Result: JavaSpace
Signal
JavaSpace
Aggregator
Computation Unit
Display
1sec
Average Time: 140 millisec

31. Case Study Graphic: JavaSpace

32. Result and comparison: GSpace vs. JavaSpace
GSpace performs well against JavaSpace despite the fact that is in a prototype stage
It is distributed
It offers support for different distribution policies

33. Concluding Remarks & Future Work
A platform for experimentation with different distribution policies is available
The early results are encouraging
Mechanisms for providing timeliness guarantees
Integrating the scheduling & timing constraints
Mechanisms for feedback performance information