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
Processes interaction is expressed differently in each of the three models.

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 Disjoint process model
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
Precedence and communication process model are interacting process models.
Turnaround time is defined as the sum of service and queuing time due to waiting for other 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
OSPT= optimal sequential processing time; the best time that can be achieved on a single processor using the best sequential algorithm
CPT= concurrent processing time; the actual time achieved on a n-processor system with the concurrent algorithm and a specific scheduling method being considered
OCPTideal =optimal concurrent processing time on an ideal system; the best time that can achieved with the concurrent algorithm being

10. System Performance Model (Cont.)
Pi: the computation time of the concurrent algorithm on node i
(RP  1)
N is the number of processors.

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 …
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 …

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”. [3] Yeon, Jiyoun; Ko, Byungkon. ” Comparison of Travel Time Estimation Using Analysis and Queuing Theory to Field Data Along Freeways”. Multimedia and Ubiquitous Engineering, MUE ‘07 International Conference on April 2007 Page(s): [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):

20. Thank you!
Any questions?