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Chapter 3
System Performance
and Models
Systems and Models
• The concept of modeling in the study of the
dynamic behavior of simple system is be able to
calculating their performances.
• System is the part of the real world under study,
which composed of a set of entities interacting
among themselves and with the environment.
• A model is an abstract representation of a
system.
• The system behavior is dependent on the input
data and actions from the environment.
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Abstraction
• The most important concept in analysis and
design
• It hide nonessential properties and behavior of
the system so the model is used to describe,
study, and/or design the system.
• A high-level description of a collection of objects
• Only the relevant properties and features of the
objects are included.
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System
A system has:
• Structure
• Behavior
The model of a system is simpler than the real
system in its structure and behavior. But it
should be equivalent to the system.
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Using Models
A user can:
• Manipulate the model by supplying it with a
set of inputs
• Observe its behavior and/or measure its
output when given some input/output.
• Predict the behavior of the real system by
analyzing the behavior of the model under the
same circumstances.
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Types of Model
The most general categories of models are:
• Physical models (scale models) is resemble the physical
attributes of an actual object by scaling down the physical
system.
• Graphical models use graphical representations to display
the behavior of a system.
• Mathematical models is a set of Mathematical expression
and logical relations to express the relationships among
the entities of objects of the system.
• Models are the most flexible ones and are the ones
studied here.
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Mathematical Models
• Analytic, the solution is a set of expressions
that define the behavior of the model.
• Numeric, mathematical techniques are used
to derive values for the model behavior within
given intervals.
• Deterministic and stochastic models are
solved with numerical techniques.
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Other Categories of Models
Models are further categorized as:
• Deterministic - models that display a completely
predictable behavior (with 100% certainty)
• Stochastic - models that display some level of
uncertainty in their behavior. This random
behavior is implemented with random variables.
• Most simulation models are stochastic because
the real systems being modeled, they exhibit
inherent uncertainty properties.
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Stochastic Models
• A stochastic model is one which includes some
uncertainty in its behavior.
• One or more attributes (implemented as random
variables) change value according to some
probability distribution.
• Example of random variables in the simple batch
OS model:
– Inter-arrival time of jobs
– CPU service time of jobs
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Model of a Simple Batch System
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Performance Metrics
• Performance Metrics is measurement or
quantity that helps characterize, together with
other performances metrics, to show the
effectiveness of the system.
The result of running a performance model shows
the values of the performance metrics.
• Average wait time – period that jobs wait in
the system since their arrival time.
• Average Throughput – the average number of
jobs completed in some specific time
Relevant Performance Measures
• CPU utilization
• Resource Utilization
• Average response time
• Average turnaround time
• Availability
• Reliability
• Capacity
• Fairness
• Speedup
Efficiency and Reliability
• The efficiency of a system is the ratio usable
capacity to nominal capacity
• The reliability of a system is measured by the
probability of errors. Also defined as the mean
time between errors.
• Availability is the fraction of time that the
system is available for user requests, also
called the system uptime.
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Simulation Models
• A simulation model is a mathematical model
implemented with a general-purpose
programming language, or a simulation
language.
• A simulation run is an experiment carried out
for some observation period and with the
simulation model to study the behavior of the
model.
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Simulation Models cont.
Simulation model divided into two categories:
• Continuous models change their state
continuously with time. Mathematical
notation is used to completely define the
behavior. For example, the free-falling object.
• Discrete models only change their state at
discrete instants. For example, arrival of a job
in the Simple batch OS model.
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Model of a Simple Batch System
The job must be
wait for the
input Queue
Job arrive at
discrete points ,
request memory &
CPU service
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At later time
some jobs
receives some
amount of CPU
time.
Simulation Results
For every simulation run there are two types of
output:
• Trace - sequence of events that occur during
the simulation period
• Performance measures – summarizes the
statistics of the simulation run.
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Performance: Internal Goals
• Performance Goals for sub-systems that are internal
to the computer(s)
• Examples
– Maximize CPU Utilization
• % time CPU is busy
– Maximize Disk Utilization
• % time Disk is busy
– Minimize Disk Access Time
• Time it takes to perform a disk request
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Workload on a System
• The workload on the system is the performance
measures depend on the current workload of
the system
– The workload for a system can be characterized by
another series of measures, which are made on the
input to the system
– Errors in characterizing the workload may have
serious consequences.
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Meaning of Performance Measures
• The average number of jobs in the system
• The average number of jobs in the queue(s) (i.e., that are
waiting)
• The average time that a job spends in the system
• The average time that a job spends in the queue(s)
• The CPU utilization
• Throughput - the total number of jobs serviced.
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Arrival Interval & Rate
• Arrival Interval is the time between 2
resource requests arriving
• Arrival Rate = 1 / Arrival Interval
– How often requests for a resource arrive
– Denoted by λ (Means on a combinations of dependent variables)
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Service Time & Rate
• Service Time is the time to actually perform a
request
– e.g. 500 Msec / request
• Service Rate = 1 / Service Time
– Denoted by μ
– e.g. 2 requests / second
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System Capacity
• The capacity of a system is determined by its
maximum performance
• The nominal capacity of a system is given by the
maximum achievable throughput under ideal
conditions
• The usable capacity is the maximum throughput
achievable under specified constraints.
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Bottleneck
• The computer system reaches capacity when one
or more of its servers or resources reach a
utilization close to 100%.
• The bottleneck of the system will be localized in
the server or resource with a utilization close to
100%, while the other servers and resources each
have utilization significantly below 100%.
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Modeling Bottleneck
The bottleneck of the computer system described
here can be localized at the processor, the
queue, or the memory.
– The queue may become full (reaches capacity) very
often as the processor utilization increases.
– The memory capacity may also be used at capacity
(100%).
– Thus, in any of the three cases, the processor, the
queue, or the memory will need to be replaced or
increased in capacity.
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Chapter 4
Systems with
Multiprogramming
Abraham Siberschatz, Peter Galvin,
& Greg Gagne From Operating
Systems Concepts Textbook
Multiprogramming
• An operating system can support several
processes in memory.
• While one process receives service from the
CPU, another process receives service from an
I/O device and the other processes are waiting
in some queues.
• The number of processes that the system
supports is called the degree of
multiprogramming.
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Multi-Programming
• In most computer systems, the I/O controllers
enable a system to overlap device I/O
operation with processor (CPU) operation.
• This results in a more efficient utilization of the
system facilities
• Several processes can be in memory at a time,
one receiving CPU service and another
receiving I/O service.
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A More Complete Model of a
Computer System
• One process receives
service from CPU
• Another process receiving
service from an I/O
• Other processes are
waiting in the queues
All occurs the same time
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Requirements for Multiprogramming
• The OS must allocate the CPU and other resources to
the various processes in such a way that the CPU and
other active resources are maintained busy the
longest period possible.
• If there is only one CPU in the system, then only one
process can be in execution at any given time.
• The other processes are ready, waiting for CPU service.
Processes also request access to passive resources,
such as memory locations.
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Process Service Requests
• A process requests CPU and I/O services at various
times and usually will have to wait for these services.
• The OS maintains a waiting line or queue of
processes for every one of these services.
• At some point in time, the process will be in a queue
waiting for CPU service. At some other point in time,
the process will be in a different queue waiting for
I/O service.
• When the process completes all service requests, it
terminates and exits the system.
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Process Management - Review
• Process management is one of the major functions of
the operating system; it involves creating processes
and controlling their execution.
• In most operating systems, several processes are
stored in memory at the same time and the
operating system (OS) manages the sharing of the
CPU and other resources among the various
processes.
• This technique in the operating system is called
multiprogramming.
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System with Multiples Stations
1. MM allocates
memory to the
waiting jobs in the
input queue.
3. I/O queue & I/O device that
provide service to the
processes waiting in the
equivalent I/O queue.
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2. Ready queue and
the CPU provide
CPU service to the
process waiting in
the ready queue
Service Demand
• The total CPU and I/O service requirements called
Service demand.
Service demands is a process is divided into shorter CPU
and I/O requests
• A process will require several CPU and I/O bursts.
• Each CPU request is called a CPU burst
• Each I/O request is called an I/O burst
• In normal processing of a process, it alternates from a
CPU burst to an I/O burst and repeats
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CPU Service
• The total CPU service demand for a process is
the sum of all its CPU bursts
• Each CPU burst has a different duration 
• The total CPU service requested by process Pi
is:
i = 1 + 2 + ... m
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Batch OS with I/O
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Context Switch
• Context Switch are an inherent characteristics of
multiprogramming, changeover from the current
process being executed to the next process to
execute.
• This process is saved in its descriptor or program
control block (PCB) and the context of next
process is loaded from it PCB.
• Context switch time is overhead time
• Time is dependent on hardware support
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Components of Simulation Model
• The active resources of the system are modeled as
active objects
• The environment is modeled as an active object
because the environment generates the jobs that will
arrive into the system/model
• The processes are also modeled as active objects --they arrive requesting resources and services
• The other system resources are modeled as passive
objects (memory, queues, etc)
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Classes Defined in the Models
• A class for active objects is a class that inherits
class Process, which is a Psim library class
• A class for passive objects can be any other
class
• The processes, each one representing a
computational unit to be serviced in the
computer system, under control of the
operating system. The processes are
implemented as active objects of class Job.
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Classes and Objects in the Simulation
Model
• The environment, which generates the arriving
processes. This is an active object of class
Arrivals. This object creates instances of class Job
according to the inter-arrival period.
• The CPU, which represents the processor that
provides CPU service (execution) to the
processes according to their service demands.
The CPU is an active object of class Processor.
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Processes and Multiprogramming
• Processes are program instances
• Several processes can be active at a time (O.S
with multiprogramming)
• Only one process is actually running at any instant
of time
• The CPU switches from one process to another
rapidly (context switching)
• The selection of which process to run next is
made by the scheduler
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Random Variables
• Random variables are used in the model to
represent several parameters and each random
variable is derived from a specific probability
distribution.
• The inter-arrival periods are generated from an
exponential distribution.
• The CPU and I/O service periods, also known as
service demands, each generated from an
exponential distribution.
• The memory demands for the processes are
generated from a uniform distribution.
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First Part of Output of A Simulation
Run
Psim/Java project: System with Multiprogramming - CPU and I/O
Simulation date: 2/18/2005 17:34
Ave. (mean) inter-arrival per.: 2.3
mean CPU service per.: 18.5; mean I/O service per.: 18.75
Min memory req.: 10, Max memory req: 65
Total system memory: 512
Degree of multiprogramming: 20; Queue size: 125
Job1 requiring service 5.456 arrives at time 0.721
Processor starting CPU burst of Job1 at 0.721
Processor completed CPU burst of Job1 at 2.752
I/O dev starting I/O burst of Job1 at 2.752
I/O dev completed burst Job1 at 4.811
Processor starting CPU burst of Job1 at 4.811
Processor completed CPU burst of Job1 at 6.092
...
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Summary of Output
Simulation closing at: 811.1911086822296
Memory usage: 0.9150302684027927
avg num items used: 476.90258114299934
End Simulation of System with Multiprogramming CPU and I/O servers, clock: 811.1911086822296
Results of simulation:
----------------------------------------------------Service factor: 0.048
Total number of jobs that arrived: 231
Total number of rejected jobs: 56
Throughput: 44
Maximum number of jobs in memory: 20
Proportion rejected/arrived jobs: 0.242
Proportion of rejected/completed jobs: 1.273
Average job wait period: 177.092
Processor utilization: 0.969
I/O dev utilization: 0.935
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A System with no
Multiprogramming
• The C++ simulation model for the batch operating
system with I/O is implemented in file
batchmio.cpp
• Only one process is allowed to be stored in
memory at a time. The degree of
multiprogramming is set to 1
• All processes request a certain amount of
memory a ( passive resource), a CPU burst, an I/O
burst, and finally, another CPU burst.
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1. When any changes happened on memory allocation, it
will effect the performances, such as decrease and increase
utilization;
2. Any changes occurs on the parameters, it will effect work
load average mean, Jobs arrival mean, average mean CPUs.
service request fluctuate (Decrease and Increase on the
parameter).
3. Work load parameters effect processor utilization
changes
4. adding memory size, implies that we are requesting more
job to be service and process utilization will reduce because
time of CPU will be divided to more services request.