Simulation-Based Comparison of Scheduling Techniques in Multiprogramming

7/27/2019 Simulation-Based Comparison of Scheduling Techniques in Multiprogramming

1/22

Simulation-based comparison of scheduling

techniques in multiprogramming operating

systems on single and multi-core processors"

Researchers :- Hala ElAarag, David Bauschlicher, and Steven Bauschilicher

Presenters:-

Name Reg.No

FHA. Shibly MS 13 9047 60

S.Sajiharan MS 13 9030 84


2/22

In this presentation..

Introduction

CPU scheduling Techniques

Objective

Methodology

Metrics & Simulations

Results

Conclusion


3/22

Introduction

CPU Scheduling is an essential component ofmulti-tasked operating systems

The objective is always to maximize CPU

utilization and hence increase computerproductivity.

This paper present three CPU scheduling

techniques Multilevel Feedback Queue (MLFQ)

Lottery Scheduling

Fair Share Scheduling


4/22

Multilevel Feedback Queue (MLFQ)

MLFQ utilizes multiple queues and allows

processes to move between queues

dynamically.

In this approach, processes "find their own

level"

Based on their CPU burst Processes that have

a larger CPU burst moves to a lower-priority

queue.


5/22

Lottery Scheduling

In Lottery Scheduling , each process is given somelottery tickets.

A lottery is held at regular intervals and the winner isdetermined by selecting a ticket at random.

The winning process gets A process will have a chanceof winning proportionate to the number of tickets ithas.

To increase the chances of winning, higher priority

processes can get more tickets. Lottery Scheduling guarantees a non-zero probability

for any process to get executed and hence solves thestarvation problem to be executed next.


6/22

Fair Share Scheduling

Fair-share scheduling is a scheduling strategy

for computer operating systems in which

the CPU usage is equally distributed among

system users or groups, as opposed to equal

distribution among processes.

That mean basic idea of fair share scheduling

is to divide CPU time evenly among users and

then among processes.


7/22

Objectives

To find and compare the performance of Fair

Share Scheduling, Lottery Scheduling and

Priority Scheduling on single and multi-core

processors


8/22

Methodology

Simulation Model


9/22

Performance Metrics and

Simulation Techniques Waiting time as performance metric.

The scheduling techniques under investigation were implemented in Javaand run under its virtual machine.

A process object was created and used for all scheduling techniques.

Each process object contained the total amount of time a process wouldtake to complete and the amount of CPU time it needed during everyexecution.

If a process's CPU execution time is low, it is considered an I/O boundprocess because it will quickly be swapped in and out of the CPU. On theother hand, if it is high, it is a CPU bound process and will probably bepreempted before it finishes.


10/22

METRICS AND SIMULATION

There are multiple performance metrics that

could be used for comparing different

scheduling algorithms. They include

Minimize waiting time

Minimize turnaround time

Minimize response time


11/22

ResultsEach technique was tested with three different ratios of CPU-boundto IO-bound processes:

a 25-75 ratio a 50-50 ratio

a 75-25 ratio

The specific CPU execution times are chosen at random but on

average will be around 350 time units for CPU bound processes and50 for I/O-bound processes.

The specific total time to execute each process is a random numberbetween 1000 and 2000. Processes are all created initially in orderto simulate a relatively standard load that can be comparedbetween all the techniques.

The average waiting time, which is the amount of time a processspends in the ready queue, for the Lottery, Fair Share and MLFQscheduling techniques.


12/22

As shown in the figures, Fair Share Schedulingperformed noticeably better than LotteryScheduling and Priority Scheduling under the

different loads. Both Lottery and Priority Scheduling processes

have similar waiting times no matter what theratio of CPU-I/O bound processes is However,

Priority Scheduling has less waiting time thanLottery Scheduling if there are many more I/Obound processes than CPU bound processes.


13/22

RESULTS OF SINGLE CORE

SIMULATIONS

Figure 1: Waiting time with process mix 75% I/O bound and 25%CPU bound


14/22

Figure 2: Waiting time with process mix 50% I/O bound and 50% CPU bound


15/22

Figure 3: Waiting time with process mix 25% I/O bound and 75% CPU bound


16/22

RESULTS OF MULTI-CORE

SIMULATIONSEach scheduling technique was simulated to run 100 processesmultiple times over a different number of processors.

From Figures 4-6, one can notice that overall, Lottery Schedulingand Priority Scheduling performed better than Fair Share

Scheduling in all cases, especially when the number of processorsincreased.

As shown in Figures 4-6, the amount of time saved by using moreprocessors seemed to increase almost linearly for each of the

scheduling technique, although the MLFQ gained the greatestadvantage by adding more processors.


17/22

Figure 4: Waiting time vs. number of cores with process mix 75% I/O bound and 25%

CPU bound


18/22

Figure 5: Waiting time vs. number of cores with process mix 50% I/O bound and 50% CPUbound


19/22

Figure 6: Waiting time vs. number of cores with process mix 25% I/O bound and 75% CPUbound


20/22

CONCLUSION

CPU scheduling is the basis of multi-

programmed operating systems. Multi-core

processors are becoming standard. In this

paper, Researchers presented a project thatwas proven to be beneficial for operating

systems students to not only understand CPU

scheduling but also study the effect of multi-core processors on different CPU scheduling

techniques.


21/22

Conclusion..

In single core simulations, Fair Share

Scheduling performed noticeably better than

Lottery Scheduling and Priority Scheduling

under the different loads.

In multi core simulations, Lottery Scheduling

and Priority Scheduling performed better than

Fair Share Scheduling in all cases, especiallywhen the number of processors increased.


22/22

Thanks

Documents

Simulation-Based Comparison of Scheduling Techniques in Multiprogramming