Distributed Process Scheduling: 5.1 A System Performance Model

1. Distributed Process Scheduling: 5.1 A System Performance Model Shuman Guo CSc 8320, Spring 2007

2. Outline • Overview • A System Performance Model • Processor Pool and Workstation Queuing Models • References

3. Overview[Randy Chow, 97] • Before execution, processes need to be scheduled and allocated with resources • The objective of scheduling • Primary: Enhance overall system performance metrics • Process completion time and processor utilization • Secondary: achieve location and performance transparencies • This chapter presents a model for capturing the effect of communication and system architectures on scheduling.

4. Outline • Overview • A System Performance Model • Processor Pool and Workstation Queuing Models • References

5. A System Performance Model • We used graph models to describe process communication. Four processes mapped to a two-processor multiple computer system

6. Process Models • Precedence process model: • Represent precedence relationships between processes • Minimize total completion time of task (computation + communication) • Communication process model • Represent the need for communication between processes

7. Process Models cont’d • Optimize the total cost of communication and computation • Disjoint process model • Processes can be run independently and completed in finite time • Maximize utilization of processors and minimize turnaround time of processes

8. System Performance Model Attempt to minimize the total completion time of (makespan) of a set of interacting processes

9. System Performance Model cont’d • Related parameters • OSPT= optimal sequential processing time; • CPT= concurrent processing time; • OCPTideal =optimal concurrent processing time on an ideal system; • Si =ideal speedup obtained by using a multiple processor system over the best sequential time • Sd = the degradation of the system due to actual implementation compared to an ideal system

10. System Performance Model (Cont.) Pi: the computation time ofthe concurrent algorithm onnode i (RP  1)

11. System Performance Model cont’d (The smaller, the better)

12. System Performance Model cont’d • RP: Relative processing • Shows how much loss of speedup is due to the substitution of the best sequential algorithm by an algorithm better adapted for concurrent implementation but which may have a greater total processing need • Sd • Degradation of parallelism due to algorithm implementation

13. System Performance Model cont’d • RC: Relative concurrency • How far from optimal the usage of the n-processor is • RC=1  best use of the processors • : Efficiency Loss is loss of parallelism when implemented on a real machine. •  can be decomposed into two terms:  = sched + syst

14. Workload Distribution • Performance can be further improved by workload distribution • Load sharing: static workload distribution • Dispatch process to the idle processors statically upon arrival • Corresponding to processor pool model • Load balancing: dynamic workload distribution • Migrate processes dynamically from heavily loaded processors to lightly loaded processors • Corresponding to migration workstation model

15. Queuing Theory • Performance of systems described as queuing models can be computed using queuing theory. An X/Y/c queue is one where: • X: Arrival Process, Y: Service time distribution, c: Numbers of servers • : arrival rate; : service rate; : migration rate • : depends on channel bandwidth, migration protocol, context and state information of the process being transferred.

16. Processor-Pool and Workstation Queueing Models Static Load Sharing Dynamic Load Balancing M for Markovian distribution

17. Examples of Real World Queuing Systems? [Lawrence] • Commercial Queuing Systems • Commercial organizations serving external customers • Ex. Medical[Huang,07], bank, ATM, gas stations, plumber, garage • Transportation service systems • Vehicles are customers or servers • Ex. Vehicles waiting at toll stations and traffic lights, trucks or ships waiting to be loaded[Yeon,07] ,taxi cabs, fire engines, elevators, buses …

18. Examples cont’d • Business-internal service systems • Customers receiving service are internal to the organization providing the service • Ex. Inspection stations, conveyor belts, computer support … • Social service systems • Ex. Judicial process, the ER at a hospital, waiting lists for organ transplants or student dorm rooms …

19. References [1] Randy Chow & Theodore Johnson, 1997,“Distributed Operating Systems & Algorithms”, (Addison-Wesley), p. 149 to 156. [2] Stephen Lawrence.”Queuing & Simulation”. http://209.85.165.104/search?q=cache:hCreyAHJ8WgJ:leedsfaculty.colorado.edu/lawrence/SYST4060/Lectures/6a%2520%2520Intro%2520to%2520Queueing.ppt+queuing+simulation+ppt+lawrence&hl=en&ct=clnk&cd=1&gl=us [3] Yeon, Jiyoun; Ko, Byungkon. ”Comparison of Travel Time Estimation Using Analysis and Queuing Theory to Field Data Along Freeways”. Multimedia and Ubiquitous Engineering, 2007. MUE ‘07 International Conference onApril 2007 Page(s):530 - 538 . [4] Ean-Wen Huang; Der-Ming Liou. ”Performance Analysis of a Medical Record Exchanges Model”. Information Technology in Biomedicine, IEEE Transactions on March 2007 Page(s):153 - 160

20. Thank you! • Any questions?