Written with computer scientists and engineers in mind, this book brings queueing theory decisively back to computer science. Through a lively mix of motivation and intuition, with illustrations, examples and more than 300 exercises, readers acquire the skills needed to model, analyze and design large-scale systems with good performance and low cost.
Written with computer scientists and engineers in mind, this book brings queueing theory decisively back to computer science. Through a lively mix of motivation and intuition, with illustrations, examples and more than 300 exercises, readers acquire the skills needed to model, analyze and design large-scale systems with good performance and low cost.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Mor Harchol-Balter is an Associate Professor in the Computer Science Department at Carnegie Mellon University. She is a recipient of the McCandless Chair, the NSF CAREER award, the NSF Postdoctoral Fellowship in the Mathematical Sciences, multiple best paper awards and several teaching awards, including the Herbert A. Simon Award for Teaching Excellence and the campus-wide Teaching Effectiveness Award. She is a leader in the ACM SIGMETRICS/Performance community, for which she recently served as Technical Program Chair, and has served on the Technical Program Committee twelve times. Harchol-Balter's work integrates queueing theoretic analysis with low-level computer systems implementation. Her research is on designing new resource allocation policies (load balancing policies, power management policies and scheduling policies) for server farms and distributed systems in general, where she emphasizes integrating measured workload distributions into the problem solution.
Inhaltsangabe
Part I. Introduction to Queueing: 1. Motivating examples; 2. Queueing theory terminology; Part II. Necessary Probability Background: 3. Probability review; 4. Generating random variables; 5. Sample paths, convergence, and averages; Part III. The Predictive Power of Simple Operational Laws: 'What If' Questions and Answers: 6. Operational laws; 7. Modification analysis; Part IV. From Markov Chains to Simple Queues: 8. Discrete-time Markov Chains; 9. Ergodicity theory; 10. Real-world examples: Google, Aloha; 11. Generating functions for Markov Chains; 12. Exponential distributions and Poisson Process; 13. Transition to continuous-time Markov Chains; 14. M/M/1 and PASTA; Part V. Server Farms and Networks: Multi-server, Multi-queue Systems: 15. Server farms: M/M/k and M/M/k/k; 16. Capacity provisioning for server farms; 17. Time-reversibility and Burke's Theorem; 18. Jackson network of queues; 19. Classed network of queues; 20. Closed networks of queues; Part VI. Real-World Workloads: High-Variability and Heavy Tails: 21. Tales of tails: real-world workloads; 22. Phase-type workloads and matrix-analytic; 23. Networks of time-sharing (PS) servers; 24. M/G/I queue and inspection paradox; 25. Task assignment for server farms; 26. Transform analysis; 27. M/G/I transform analysis; 28. Power optimization application; Part VII. Smart Scheduling: 29. Performance metrics; 30. Non-preemptive, non-size-based policies; 31. Preemptive, non-size-based policies; 32. Non-preemptive, size-based policies; 33. Preemptive, size-based policies; 34. Scheduling: SRPT and fairness.
Part I. Introduction to Queueing: 1. Motivating examples; 2. Queueing theory terminology; Part II. Necessary Probability Background: 3. Probability review; 4. Generating random variables; 5. Sample paths, convergence, and averages; Part III. The Predictive Power of Simple Operational Laws: 'What If' Questions and Answers: 6. Operational laws; 7. Modification analysis; Part IV. From Markov Chains to Simple Queues: 8. Discrete-time Markov Chains; 9. Ergodicity theory; 10. Real-world examples: Google, Aloha; 11. Generating functions for Markov Chains; 12. Exponential distributions and Poisson Process; 13. Transition to continuous-time Markov Chains; 14. M/M/1 and PASTA; Part V. Server Farms and Networks: Multi-server, Multi-queue Systems: 15. Server farms: M/M/k and M/M/k/k; 16. Capacity provisioning for server farms; 17. Time-reversibility and Burke's Theorem; 18. Jackson network of queues; 19. Classed network of queues; 20. Closed networks of queues; Part VI. Real-World Workloads: High-Variability and Heavy Tails: 21. Tales of tails: real-world workloads; 22. Phase-type workloads and matrix-analytic; 23. Networks of time-sharing (PS) servers; 24. M/G/I queue and inspection paradox; 25. Task assignment for server farms; 26. Transform analysis; 27. M/G/I transform analysis; 28. Power optimization application; Part VII. Smart Scheduling: 29. Performance metrics; 30. Non-preemptive, non-size-based policies; 31. Preemptive, non-size-based policies; 32. Non-preemptive, size-based policies; 33. Preemptive, size-based policies; 34. Scheduling: SRPT and fairness.
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