OS Spring’03

Performance Evaluation

Operating Systems Spring 2003

OS Spring’03

Performance evaluation There are several approaches for

implementing the same OS functionality

Different scheduling algorithmsDifferent memory management schemes

Performance evaluation deals with the question how to compare wellness of different approaches

Metrics, methods for evaluating metrics

OS Spring’03

Performance Metrics

Is something wrong with the following statement:

The complexity of my OS is O(n)?

This statement is inherently flawedThe reason: OS is a reactive program

What is the performance metric for the sorting algorithms?

OS Spring’03

Performance metrics Response time Throughput Utilization Other metrics:

Mean Time Between Failures (MTBF)Supportable load

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Response time The time interval between a user’s

request and the system responseResponse time, reaction time, turnaround time, etc.

Small response time is good:For the user: waiting lessFor the system: free to do other things

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Throughput Number of work units done per time

unitApplications being run, files transferred, etc.

High throughput is goodFor the system: was able to serve many clientsFor the user: might imply worse service

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Utilization Percentage of time the system is

busy servicing clientsImportant for expensive shared systemLess important (if at all) for single user systems, for real time

systems

Utilization and response time are interrelated

Very high utilization may negatively affect response time

OS Spring’03

Performance evaluation methods

Mathematical analysisBased on a rigorous mathematical model

SimulationSimulate the system operation (usually only small parts thereof)

MeasurementImplement the system in full and measure its performance directly

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Analysis: Pros and Cons+Provides the best insight into the

effects of different parameters and their interaction• Is it better to configure the system with

one fast disk or with two slow disks?

+Can be done before the system is built and takes a short time

- Rarely accurateDepends on host of simplifying assumptions

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Simulation: Pros and Cons+Flexibility: full control of

Simulation model, parameters, Level of detail Disk: average seek time vs. acceleration and

stabilization of the head

+Can be done before the system is built- Simulation of a full system is infeasible- Simulation of the system parts does

not take everything into account

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Measurements: Pros and Cons

+The most convincing- Effects of varying parameter values

cannot (if at all) be easily isolatedOften confused with random changes in the environment

- High cost:Implement the system in full, buy hardware

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The bottom line Simulation is the most widely used

technique Combination of techniques

Never trust the results produced by the single method Validate with another one E.g., simulation + analysis, simulation +

measurements, etc.

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Workload Workload is the sequence of things to

doSequence of jobs submitted to the system Arrival time, resources needed

File system: Sequence of I/O operations Number of bytes to access

Workload is the input of the reactive system

The system performance depends on the workload

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Workload analysis Workload modeling

Use past measurements to create a model E.g., fit them into a distribution

Analysis, simulation, measurement

Recorded workloadUse past workload directly to drive evaluationSimulation, measurement

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Statistical characterization Every workload item is sampled at

random from the distribution of some random variable

Workload is characterized by a distributionE.g., take all possible job times and fit them to a distribution

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The Exponential Distribution A lot of low values and a few high

values The distributions of salaries, lifetimes, and

waiting times are often fit the exponential distribution

The distribution ofJob runtimes

Job inter-arrival times

File sizes

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Exponential Probability Density Function (pdf) If X has an exponential distribution with

parameter , then its probability density function is given by:

where...718.2e

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The Exponential Distribution

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Mean and Variance

If X~Exponential( ), then its mean and variance are given by:

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Exponential Probabilities

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Memoryless Property The exponential is the only distribution

with the property that

For modeling runtimes: the probability to run for additional b time units is the same regardless of how much the process has been running already

in average

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Fat-tailed distribution The real life workloads frequently do not

fit the exponential distribution Fat-tailed distributions:

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Pareto Distribution

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Mean is unboundedThe more you wait, the more additional time you should expect to waitThe longer a job has been running, the longer additional time it is expected to run

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Exponential vs. Pareto The mean additional time to wait is

determined by the shape of the tailThe fatter tail, the more additional time to wait

For exp.: the tail shape is the same regardless of how much we have waited already=>

The mean additional time stays the same For Pareto: The more we wait, the

fatter tail becomesThe more we wait, the more additional time we will wait

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Exp. vs. Pareto: Focus on tail

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Queuing Systems

Computing system can be viewed as a network of queues and servers

CPU

DiskA

DiskB

queue

queue

queue

newjobs

finishedjobs

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The role of randomness Arrival (departure) are random

processesDeviations from the average are possibleThe deviation probabilities depend on the inter-arrival time distribution

Randomness makes you wait in queueEach job takes exactly 100ms to completeIf jobs arrive each 100ms exactly, utilization is 100%But what if both these values are on average?

OS Spring’03

Queuing analysis

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Little’s Law

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How response time depends on utilization?

Write the average number of jobs as a function of arrival and service rates

Queuing analysis

Substitute it to the Little’s law

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M/M/1 queue analysis

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Response time (utilization)

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Summary What are the three main performance

evaluation metrics? What are the three main performance

evaluation techniques? What is the most important thing for

performance evaluation? Which workload models do you know? What does make you to wait in

queue? How response time depends on

utilization?

OS Spring’03

To read more Notes Stallings, Appendix A Raj Jain, The Art of Computer

Performance Analysis

OS Spring’03

Next: Processes