1. Project II Rule Optimizer for the Atlas Reactivity Engine CNT 5517-5564
Dr. Sumi Helal
Computer & Information Science & Engineering Department
University of Florida, Gainesville, FL 32611

2. Manuals & Downloads The Atlas Class Web Page
The Atlas Reactivity Engine

3. Overall Architecture RE Optimizer Atlas Reactivity Engine
Atlas Middleware
RE Optimizer
Atlas Emulator
Atlas
Reactivity Engine
Command
Interpreter

4. Atlas RE Engine An Event/Condition/Action paradigm
A programming model for pervasive space
A Command line interface to interact with the engine
View Basic Events & Actions.
Predefine: Events, Conditions and Actions
Define Rules
Accept commands & Provide results/feedback
Engine interacts with the sensors /actuators through the Atlas middleware

5. EVENTS

6. Conditions

7. Actions
Rules

8. Commands

9. Tokens & Delimiters

10. Optimization Frameworks
Push/Pull Envelop
Caching
Evaluation Short Cuts
Application Characteristics

11. Push / Pull Envelop
Configuration of which sensors should participate, and if so, in which mode (push or pull, and if latter, at which frequency).
push
pull
pure push
pure pull
sensor
Atlas Reactivity Engine ……
Optimal push/pull envelope
Application Layer

12. Evaluation Short Cuts Exploit Dominant Events
For a composite event whose sub-events are connected by logic +, some of its sub-events may have significantly higher probability of occurrence than others which makes them a dominant factor in determining the value of the composite event. ( Likewise, for composite events whose sub-events are connected by logic *, sub-events with lower probability of occurrence become dominant )

13. Project II Summary
Form groups of 4 by no later than Friday Nov 5 noon.
Understand all the components
Study source code of RE Engine
Develop the Optimizer
Think, formalize and create your algorithms
Implement
Assess the success/failure of your Optimizer

14. Deliverables
You will deliver source code with detailed documentation and about 5-10 pages report describing the following:
Status of your project: what is completed and what not; what works and what not
Your optimization ideas and strategies
Formal description of your optimization algorithm
Your experiment results
Any additional features implemented and the rationale
Conclusions