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Parametric Optimization Of Some Critical Operating System Functions An Alternative Approach To The Study Of Operating Systems Design.

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Presentation on theme: "Parametric Optimization Of Some Critical Operating System Functions An Alternative Approach To The Study Of Operating Systems Design."— Presentation transcript:

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2 Parametric Optimization Of Some Critical Operating System Functions An Alternative Approach To The Study Of Operating Systems Design

3 Parametric Optimization An Alternative Approach of OS Study Some Critical Operating System Functions CPU Scheduling Synchronization and Deadlock Handling Memory Management Disc Scheduling study parameter performance relationships achieve parametric optimization using simulation technique … … … … … … The Integrated Perspective

4 CPU Scheduling An Introduction  An operating system must select processes for execution in some fashion.  CPU scheduling deals with the problem of deciding which of the processes in the ready queue is to be allocated the CPU.  The selection process is carried out by an appropriate scheduling algorithm (in our simulation we have used the multilevel feedback queue).

5 CPU Scheduling Parameters Involved  Round Robin Queue Time Slot  FIFO, Priority and SJF Queue Aging Thresholds  Preemptive vs. Non-Preemptive Scheduling (2 switches in priority and SJF queues)  Context Switching Time   -values and initial execution time estimates

6 CPU Scheduling Performance Measures  Average Turnaround Time  Average Waiting Time  CPU utilization  CPU throughput

7 CPU Scheduling Parameter-Performance Relationships  Effect of Round Robin Time Slot: Small round robin quantum value lowers system performance with increased context switching time. Large quantum values result in FIFO behavior with effective CPU utilization, lower turnaround and waiting times as also the potential of starvation.  Effect of Aging Thresholds: A very large value of aging thresholds makes the waiting and turnaround times unacceptable. These are signs of processes nearing starvation. On the other hand, a very small value makes it equivalent to one round robin queue.

8 CPU Scheduling Implementation Details Some specifications of the implemented module: The scheduler consists of 4 linear Q's: The first Q is FIFO, second Q is priority-based, third Q is SJF.and the fourth (highest Priority) is round robin. A switch provides choice between pre-emptive and non pre-emptive scheduling in the SJF, and priority-based Q's. Jobs cannot execute in a Q unless there are no jobs in higher priority Q's. Some variable parameters: Feed back occurs through aging, aging parameters differ, i.e., each Q has a different aging threshold The time slot for the Round Robin Q Context switching time

9 CPU Scheduling Implementation Details (contd.) The jobs are created with the following fields in their PCB: Job number, arrival time, actual execution time, priority,Queue number (process type 1 - 4). The creation is done randomly. Output indicates a time line, i.e, every time step, it indicates which processes are created (if any), which ones are completed (if any), processes which aged in different Q's, etc. The eventual goal is to optimize several performance measures (enlisted earlier)

10 CPU Scheduling Sample Screenshot of Simulation Sample Run 1: Round Robin Q Time slot: 2

11 CPU Scheduling Sample tabulated data from simulation RRTimeSlot Av.Turnaround Time Av. Waiting Time CPU Utilization Throughput 219.751766.67 %0.026 322.672075.19 %0.023 443.674180.00 %0.024 526.52583.33 %0.017 638.53786.21 %0.017

12 CPU Scheduling Sample Graphs(using data from simulation)

13 CPU Scheduling Conclusions from the sample simulation  The following emerged as the optimizing parameters for the given process mix: optimal value of the round robin quantum smallest possible context switching time  update had no effect on the performance measures in this case

14 Parametric Optimization Advantages of this approach  Presents operating systems design in a generic and objective manner  Students learn the scientific technique of parametric optimization which can be easily applied to other engineering problems  Students can simulate the modules using a programming language of their choice on a platform of their choice.


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