os lacture

Operating Systems

Lecture 18

Syed Mansoor Sarwar

April 13, 2023 © Copyright Virtual University of Pakistan

Agenda for Today

Review of previous lectureUNIX System V scheduling Algorithm evaluationProcess synchronizationRecap of lecture

Review of Lecture 17

Multi-level queues schedulingMulti-level feedback queues

schedulingUNIX System V scheduling

algorithm

Every second, the priority number of all those processes that are in the main memory and ready to run is updated by using the following formula:

Priority# = (Recent CPU Usage)/2 + Thr. Pri.+ nice

Threshold priority and nice values are always positive to prevent a user from migrating out of its assigned group

UNIX System V Scheduling Algorithm

Priority

CPU Count

01…6030

78…6733

Priority

CPU Count

01…6030

78…6733

Priority

CPU Count

01…6030

UNIX System V Example

Round Robin Scheduling and Process Priorities

B A B B A runs first

Algorithm Evaluation Analytic Evaluation

The algorithm and some system workload are used to produce a formula or number which gives the performance of the algorithm for that workload.

Deterministic modeling Queuing models

Implementation

Deterministic Modeling Predetermined workload and

performance of each algorithm for that workload. Use of Gantt charts.

Simple and fast Exact numbers for comparison Requires exact input Performance figures may not be

true in general

Process Arrival Time Burst Time

P1 0.0 7

P2 2.0 4

P3 4.0 1

P4 5.0 4

Gantt chart

Average waiting time = (9 + 1 + 0 +2)/4 = 3

42 110

Deterministic Modeling

Queuing Modeling Computer system viewed as a

network of queues and servers: ready queue, I/O queue, event queues, CPUs, I/O device controllers, etc.

Input: Arrival and service rates Output: CPU utilization, average

queue length, average waiting time, …

Queuing Modeling

Little’s Formula:n = λ* W

wheren = average queue lengthλ = average arrival rateW = average waiting time in a

Queuing ModelingLet the average job arrival rate be 0.5

Algorithm Average Wait TimeW=tw

Average Queue Length(n)

FCFS 4.6 2.3

SJF 3.6 1.8

SRTF 3.2 1.6

RR (q=1) 7.0 3.5

RR (q=4) 6.0 3.0

Queuing Modeling Complicated mathematics Distributions (Poisson, uniform,

exponential, etc) for the arrival and departure rates can be difficult to work with

Assumptions may not be accurate Approximation of the real system

SimulationProgramming model for the

computer systemWorkload generated by

assuming some distribution and a random number generator, or by collecting data from the actual system.

SimulationCharacteristics

Expensive: hours of programming and execution time

May be erroneous because of the assumptions about distributions

Simulation

Implementation

BestMost expensive

Good option due to Open Source kernels such as Linux

Process Synchronization

Concurrent access to shared data may result in data inconsistency.

Maintaining data consistency requires mechanisms to ensure that cooperating processes access shared data sequentially.

Bounded-Buffer Problem

Shared data#define BUFFER_SIZE 10typedef struct {

. . .} item;

item buffer[BUFFER_SIZE];int in = 0, out = 0;int counter = 0;

Producer processitem nextProduced;…while (1) { while (counter == BUFFER_SIZE) ; buffer[in] = nextProduced; in = (in + 1) % BUFFER_SIZE; counter++;}

item nextConsumed;while (1) { while (counter == 0) ; nextConsumed = buffer[out]; out = (out + 1) % BUFFER_SIZE; counter--;}

Bounded-Buffer ProblemConsumer process

“counter++” in assembly language

MOV R1, counter

INC R1 MOV counter, R1

“counter--” in assembly languageMOV R2, counter

DEC R2 MOV counter, R2

If both the producer and consumer attempt to update the buffer concurrently, the machine language statements may get interleaved.

Interleaving depends upon how the producer and consumer processes are scheduled.

Assume counter is initially 5. One interleaving of statements is:

producer: MOV R1, counter (R1 = 5) INC R1 (R1 = 6)consumer: MOV R2, counter (R2 = 5) DEC R2 (R2 = 4)producer: MOV counter, R1 (counter = 6)consumer: MOV counter, R2 (counter = 4)

The value of count may be either 4 or 6, where the correct result should be 5.

Race Condition: The situation where several processes access and manipulate shared data concurrently, the final value of the data depends on which process finishes last.

Critical Section: A piece of code in a cooperating process in which the process may updates shared data (variable, file, database, etc.).

Critical Section Problem: Serialize executions of critical sections in cooperating processes

Software based solutions Hardware based solutions Operating system based

solution

Solution of the Critical Problem

critical section

reminder section

} while (1);

entry section

exit section

Structure of Solution

Recap of Lecture

UNIX System V scheduling Algorithm evaluationProcess synchronizationRecap of lecture

Operating Systems

Lecture 18

Syed Mansoor Sarwar

os lacture

copyright virtual9

university of1

size counter

r1 counter

boundedbuffer problem

r1 mov counter

int counter

r2 mov counter

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