1. Some Future Research Directions SIGMETRICS 2007 Don Towsley UMass-Amherst

2. Overview  PE concerned with solving problems  implications?  some challenges  education for the system

3. PE confluence of many areas nail -> hammer screw -> screw driver nut -> wrench PE  problem solving design exploration -> stochastic models measurements -> statistics resource allocation -> optimization theory dynamic rsrc alloc -> control theory

4. stochastic processes statistics machine learning optimization information theory control theory signal processing PE game theory

5. Information theory and PE  IT concerned with minimizing communication resources  entropy – communication usage bound  sensor networks characterized by  severe resource constraints  highly correlated data streams  network monitoring, radar networks, habitat sensor nets, …

6. Query processing in data sensor networks Challenge: given set of queries, minimize resource consumption to satisfy query result metric Resources: bandwidth, power, processing, storage Metrics: error in result (rate distortion), power consumption, … Issues: complexity, resource constraints Tools: traditional PE, information theory, control theory, ML, …

7. PE, control optimization, game theory Many PE problems are optimization problems  storage management  call admission  congestion/flow control Often between competing parties Need to address entire problem – not just evaluate performance of one instance

8. Multiple controllers  network control  routing, congestion control, call admission  add an overlay  and another Control

9. Multiple controllers  network control  routing, congestion control, call admission  add an overlay  and another  or an application Control Result?  controller mismatch?  well-tuned machine?  performance implications?

10. Multiple controllers Issues: complex interactions among self- interested players Tools: traditional PE, control theory, game theory, economic theory

11. Training for PE  background in  probability theory, stochastic processes, statistics  course(s) in performance evaluation  how to handle real world problems –right questions? assumptions  iterative modeling/validation process  combining analysis, simulation, measurements  use good case studies  exposure to (some of)  ML, information theory, convex optimization, differential equations, game theory, control theory, …

12. Thanks! Questions?