Performance modeling and design of computer systems: queueing theory in action

 Content: I. Introduction to Queueing: 1. Motivating examples; 2. Queueing theory terminology --
 II. Necessary Probability Background: 3. Probability review; 4. Generating random variables for simulation; 5. Sample paths, convergence, and averages --
 Part III. The Predictive Power of Simple Operational Laws: 'What-If' Questions and Answers; 6. Little's law and other operational laws; 7. Modification analysis: ''what-if'' for closed systems --
 Part IV. From Markov Chains to Simple Queues: 8. Discrete-time Markov Chains; 9. Ergodicity theory; 10. Real-world examples: Google, Aloha, and harder chains; 11. Exponential distribution and the Poisson process; 12. Transition to continuous-time Markov Chains; 13. M/M/I and PASTA --
 V. Server Farms and Networks: Multi-server, Multi-queue Systems: 14. Server farms: M/M/k and M/M/k/k; 15. Capacity provisioning for server farms; 16. Time-reversibility and Burke's Theorem; 17. Networks of queues and Jackson product form; 18. Classed network of queues; 19. Closed networks of queues --
 VI. Real-World Workloads: High-Variability and Heavy Tails: 20. Tales of tails: real-world workloads; 21. Phase-type workloads and matrix-analytic methods; 22. Networks with time-sharing (PS) servers (BCMP); 23. The M/G/I queue and inspection paradox; 24. Task assignment for server farms; 25. Transform analysis; 26. M/G/I transform analysis; 27. Power optimization application --
 VII. Smart Scheduling in the M/G/I: 28. Performance metrics; 29. Scheduling: non-preemptive, non-size-based policies; 30. Scheduling: preemptive, non-size-based policies; 31. Scheduling: non-preemptive, size-based policies; 32. Scheduling: preemptive, size-based policies; 33. Scheduling: SRPT and fairness.
 Abstract:             
              Written with computer scientists and engineers in mind, this book brings queueing theory decisively back to computer science.                 Read more...