distributed process scheduling: 5.1 a system performance model
Post on 14-Jan-2016
50 Views
Preview:
DESCRIPTION
TRANSCRIPT
Distributed Process Scheduling: 5.1 A System Performance Model
Shuman Guo
CSc 8320, Spring 2007
1
Outline
Overview A System Performance Model Processor Pool and Workstation Queuing
Models References
2
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.
3
Outline
Overview A System Performance Model Processor Pool and Workstation Queuing
Models References
4
A System Performance Model
We used graph models to describe process communication.
5
Four processes mapped to a two-processor multiple computer system
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
6
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
7
System Performance Model
8
Attempt to minimize the total completion time of (makespan) of a set of interacting processes
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
9
System Performance Model (Cont.)
10
Pi: the computation time ofthe concurrent algorithm onnode i
(RP 1)
System Performance Model cont’d
11
(The smaller, the better)
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
12
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
13
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
14
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.
15
Processor-Pool and Workstation Queueing Models
16
Static Load SharingDynamic Load Balancing
M for Markovian distribution
Examples of Real World Queuing Systems? [Lawrence]
17
Commercial Queuing SystemsCommercial organizations serving external customersEx. Medical[Huang,07], bank, ATM, gas stations, plumber, garage
Transportation service systemsVehicles are customers or serversEx. Vehicles waiting at toll stations and traffic lights, trucks or ships waiting to be loaded[Yeon,07] ,taxi cabs, fire engines, elevators, buses …
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 …
18
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
19
Thank you!
Any questions?
20
top related