Transcript Slides from Don's remarks

Some Future Research Directions
SIGMETRICS 2007
Don Towsley
UMass-Amherst
Overview

PE concerned with solving problems
 implications?
some challenges
 education for the system

PE confluence of many areas
PE  problem solving
nail -> hammer
screw -> screw driver
nut -> wrench
design exploration
-> stochastic models
measurements
-> statistics
resource allocation
-> optimization theory
dynamic rsrc alloc
-> control theory
optimization
machine
learning
game
theory
PE
statistics
stochastic processes
control
theory
signal
processing
information
theory
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, …
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, …
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
Multiple controllers

network control
 routing,
congestion
control, call admission
add an overlay
 and another

Control
Multiple controllers

network control




routing, congestion control,
call admission
add an overlay
and another
or an application
Result?
 controller
mismatch?
 well-tuned machine?
 performance implications?
Control
Multiple controllers
Issues: complex interactions among selfinterested players
Tools: traditional PE, control theory, game
theory, economic theory
Training for PE

background in
 probability
statistics

theory, stochastic processes,
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, …
Thanks!
Questions?