Distributed Process Scheduling: 5.1 A System Performance Model

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Distributed Process Scheduling 5.1 A System
Performance Model

• Shuman Guo
• CSc 8320, Spring 2007

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Outline

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

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OverviewRandy 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.

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Outline

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

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A System Performance Model

• We used graph models to describe process
  communication.

Four processes mapped to a two-processor multiple
computer system
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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

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Process Models contd

• 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

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

• 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

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System Performance Model (Cont.)
Pi the computation time ofthe concurrent
algorithm onnode i
(RP ? 1)
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System Performance Model contd
(The smaller, the better)
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System Performance Model contd

• 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

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System Performance Model contd

• RC Relative concurrency
• How far from optimal the usage of the n-processor
  is
• RC1 ? 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

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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

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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.

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Processor-Pool and Workstation Queueing Models
Static Load Sharing
Dynamic Load Balancing
M for Markovian distribution
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Examples of Real World Queuing Systems? Lawrence
Commercial Queuing Systems Commercial
organizations serving external customers Ex.
MedicalHuang,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
loadedYeon,07 ,taxi cabs, fire engines,
elevators, buses
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Examples contd

• 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

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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?qcachehCreyAHJ8WgJ
  leedsfaculty.colorado.edu/lawrence/SYST4060/Lectur
  es/6a25202520Intro2520to2520Queueing.pptqueui
  ngsimulationpptlawrencehlenctclnkcd1glu
  s
• 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

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Thank you!

• Any questions?