1. A Method for Transparent Admission Control and Request Scheduling in E-Commerce Web Sites S. Elnikety, E. Nahum, J. Tracey and W. Zwaenpoel Presented By Tyrel Russell

2. The Idea Reduce contention at the database server No modification of existing systems Increase response time and maintain peak throughput

3. Outline Introduction The Gatekeeper Proxy Experimental Environment Conclusion and Discussion

4. The Problems Two problems for e-commerce web sites Overload Overload Responsiveness Responsiveness Solution is admission control Measures performance Measures performance Enables overload protection Enables overload protection Improves response time Improves response time

5. The Architecture

6. The Gatekeeper Proxy Intercepts dynamic web content requests Requests generated by Java servlets on the application server Estimates expected service times for each type Determines the capacity of the system Schedules the requests to improve performance

7. Estimated Load Generates estimated load from expected service times for each servlet This is a measure based on recent executions of requests Much greater variation between servlets than within servlets Service times vary based the load

8. System Capacity Determined offline Capacity is defined as the maximum load level that produces the highest capacity Found by searching the space using candidate values and experimentally verifying them

9. Admission Control Maintains running estimate of system load When request is made, the work estimate is checked against the capacity If there is room, it is admitted otherwise the request is queued When jobs are finished, the queued requests are admitted when the room allows

10. Request Scheduling To reduce average response time, they use the shortest-job first policy Queues request based on their expected processing times To prevent starvation, they used an agin mechanism which enforces an upper bound on the length of time the request can stay in the queue

11. Experimental Environment Locking is done by using concurrency control at the application server They don’t control the serving of images as they are stored at the web server Evaluate throughput and response time as a function of load

12. Discussion and Conclusion Is the concavity assumption of capacity reasonable? Is the sliding window average of execution time actually a good measure of expected execution time? Is the assumption that servlets will have relatively uniform loads justified? Is locking at the server side a good idea? For example, what happens if multiple application servers are used for the same database? They use a finite-state Markov model is used to model user client behaviour. How were the transition probabilities generated and is this realistic? How does holding up requests at the proxy effect the scheduling and optimization mechanisms of the database server?