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Performance Engineering Overview 2 Enterprise Computing Performance Brian Wilson CS 4803 EPR
Lecture Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],Enterprise Computing Performance - Course Overview
Queuing Theory Simplified A brief introduction to queuing theory, as it applies to computing performance
What is Queuing Theory? a collection of mathematical models of various queuing systems that take inputs based on probability or assumption, and that provide quantitative parameters describing the system performance.
Introduction ,[object Object],[object Object],[object Object],Enterprise Computing Performance - Course Overview
What’s Queuing Theory? ,[object Object],Enterprise Computing Performance - Course Overview input output queue server residence time =  wait  time +  service   time
Types of Queues ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Enterprise Computing Performance - Course Overview
Queuing Disciplines ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Enterprise Computing Performance - Course Overview
What’s a Bottleneck? ,[object Object],[object Object],[object Object],[object Object],Enterprise Computing Performance - Course Overview
QT Assumptions ,[object Object],[object Object],Enterprise Computing Performance - Course Overview
Formulae Notation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Enterprise Computing Performance - Course Overview
Kendall Notation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Enterprise Computing Performance - Course Overview
Example: The M/M/1 System Enterprise Computing Performance - Course Overview Job output queue Exponential server
Little’s Law 1 ,[object Object],[object Object],[object Object],Enterprise Computing Performance - Course Overview
Little’s Law 2 ,[object Object],[object Object],[object Object],Enterprise Computing Performance - Course Overview
Little’s “LAW” ,[object Object],[object Object],Enterprise Computing Performance - Course Overview
Web Server Queuing Model Enterprise Computing Performance - Course Overview
Review Questions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Enterprise Computing Performance - Course Overview
Resources ,[object Object],[object Object],Enterprise Computing Performance - Course Overview
Early Life-cycle Performance Modeling A brief overview
Sequence Diagram Example Enterprise Computing Performance - Course Overview
Expanded Sequence Enterprise Computing Performance - Course Overview
Distributed System Model Enterprise Computing Performance - Course Overview
Resource Requirements Enterprise Computing Performance - Course Overview See Page 38 Add requirements (in terms of time) for resources such as CPU, Disk, NetDelay, etc for each step of each scenario.
Performance Prediction Tools Enterprise Computing Performance - Course Overview ,[object Object],[object Object],[object Object],[object Object],[object Object]

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Georgia Tech: Performance Engineering - Queuing Theory and Predictive Modeling

Editor's Notes

  1. Enterprise Computing Performance
  2. Enterprise Computing Performance Explain the idea of pouring water into a bottle through a funnel… Q: How do you calculate how much water the funnel can handle, given a fixed time interval? What are the factors? (Production Rate, Consumption Rate, Funnel Size) Talk about each computer system resource (ask for examples) can be viewed autonomously; each having a queue that can get overfilled, thus degrading performance… give and ask for analogies.
  3. Enterprise Computing Performance Explain the idea of pouring water into a bottle through a funnel… Q: How do you calculate how much water the funnel can handle, given a fixed time interval? What are the factors? (Production Rate, Consumption Rate, Funnel Size) Talk about each computer system resource (ask for examples) can be viewed autonomously; each having a queue that can get overfilled, thus degrading performance… give and ask for analogies.
  4. Enterprise Computing Performance Explain the idea of pouring water into a bottle through a funnel… Q: How do you calculate how much water the funnel can handle, given a fixed time interval? What are the factors? (Production Rate, Consumption Rate, Funnel Size) Talk about each computer system resource (ask for examples) can be viewed autonomously; each having a queue that can get overfilled, thus degrading performance… give and ask for analogies.
  5. Enterprise Computing Performance Job Flow Balance = The assumption that the system is fast enough to handle the arrives and thus the completion rate or throughput equals the arrive rate.
  6. Enterprise Computing Performance Queuing theory models can only describe average behavior over time, NOT instantaneous or real-time data-points or complex performance trends, without mechanical means (simulation and analysis tools), as simple theory must then be extrapolated and applied to complex practice.
  7. Enterprise Computing Performance Hand this out
  8. Enterprise Computing Performance Ask for someone to give the class his/her definition, before clicking Emphasize that this will be on the final exam
  9. Enterprise Computing Performance Little's Law (N = AT) states that the average number of jobs waiting in the queue (N) is equal to the product of the average arrival rate and the average response time. Little's Law is surprisingly general, and applies to all queuing systems that are both stable and conservative (i.e., no work is lost when switching between jobs). Little's Law is especially useful when applied to queuing networks. Typically, a single queue is insufficient for modeling a complex system such as a Web server. In many such cases a system can be modeled as a graph or network in which each queue represents one node. Such queuing networks are called open if new jobs arrive from outside the network, and may eventually depart from the network.
  10. Enterprise Computing Performance Answers: When the arrival rate is less than the service rate (1/Ts)… A < (1/Ts) The system is stable – sooner or later ALL messages/requests will be serviced. The performance may be poor, but the queue will function. “ Memoryless” or exponential – THIS WILL BE ON THE TEST Markov
  11. Enterprise Computing Performance Answers: When the arrival rate is less than the service rate (1/Ts)… A < (1/Ts) The system is stable – sooner or later ALL messages/requests will be serviced. The performance may be poor, but the queue will function. “ Memoryless” or exponential – THIS WILL BE ON THE TEST Markov