1. Regulating Data Flow in J2EE Application Server
Wei Xu
Zhangxi Tan
Armando Fox
David Patterson

2. Motivation Regulate workload flow in software systems Examples
Workload pushed through multiple black-box components
Different resource requirement in each tier
Hand tuning is hard
Overloading causes performance and safety problems
Examples
Streaming database (DSOM’05)
J2EE application

3. Problem and Control Loop
Database overload => deadlock
Need to regulate incoming workload at the web server
Prevent client visible failure

4. Our approach Explicit feedback loop for flow control
Take advantage of global view in software systems
Simpler than networking flow control
Feedback database workload
Queuing algorithms to simplify system
Queuing algorithms usually used to control fairness among different flows
A separate policy decision from flow control
Queuing policy “shapes” the workload
Certain queuing policy simplifies system model
Many existing algorithms, auto parameter tuning
Queue requests based on their database demand

5. Single queue flow control
All requests queued in a single queue at web server
Use feedback control to determine number of requests to admit
Problem
Each HTTP request causes different load on db
Too noisy for controller
=> Need better request -> db prediction

6. Other problems Unfair Punishing small requests for no reason
Inefficient
Web server is under utilized
Punish small requests
While web server still not fully utilized
Response time with a single queue

7. Classifying workload Classifying workload at web server
Based on its potential database usage
No need to be accurate, feedback loop will fix the error
k-means
Small/large request
by approx. db usage
Offline profiling, but can be online

8. Dynamic Probabilistic Scheduling
Naive admission control punish small requests
Web server is under-utilized
Give small requests higher priority
Keep fairness to large requests
Need to adapt to changes in workload
Response time with 2 queues, round robin
Response time with 2 queues, DPS

9. Conclusion Using control theory to regulate dataflow Future work
Explicit feedback is used for flow control
Queueing policy used to shape workload, simplify control design
Exploits web application specific requirements
Future work
Workload classification in more complex applications
Online classification without profiling (requires better instrumentation and profiling in software)
Interact with resource allocation controller

10. Simple control – can still crash

11. Control Diagram

12. Controller Performance CDF

13. Dynamic Probabilistic Scheduling

14. Performance of DPS (Ratio of blocking probability)