W-ch06.pdfcentral processing unit scheduling

Silberschatz, Galvin and Gagne ©2013
Operating System Concepts – 9th Edit9on
Chapter 6: CPU
Scheduling

6.2 Modified By Dr. Khaled Wassif
Operating System Concepts – 9th Edition
Chapter 6: CPU Scheduling
 Basic Concepts
 Scheduling Criteria
 Scheduling Algorithms
 Thread Scheduling
 Multiple-Processor Scheduling
 Real-Time CPU Scheduling
 Operating Systems Examples
 Algorithm Evaluation

Objectives
 To introduce CPU scheduling, which is the basis
for multiprogrammed operating systems
 To describe various CPU-scheduling algorithms
 To discuss evaluation criteria for selecting a
CPU-scheduling algorithm for a particular system
 To examine the scheduling algorithms of several
operating systems

Basic Concepts
 Maximum CPU utilization
obtained with
multiprogramming
 CPU–I/O Burst Cycle –
Process execution
consists of a cycle of CPU
execution and I/O wait
 CPU burst followed by I/O
burst
 CPU burst distribution is of
main concern

Histogram of CPU-burst Times

CPU Scheduler
 Short-term scheduler selects from among the
processes in ready queue, and allocates the
CPU to one of them
 Ready queue may be ordered in various ways
 CPU scheduling decisions may take place when
a process:
1.Switches from running to waiting state
2.Switches from running to ready state
3.Switches from waiting to ready
4.Terminates
 Scheduling under 1 and 4 is nonpreemptive
 All other scheduling is preemptive
 Consider access to shared data, while in kernel mode,
and interrupts occurring during critical OS activities

Dispatcher
 Dispatcher module gives control of the CPU to
the process selected by the short-term scheduler;
this involves:
 switching context
 switching to user mode
 jumping to the proper location in the user program to
restart that program
 Dispatch latency – time it takes for the
dispatcher to stop one process and start another
running

Scheduling Criteria
 CPU utilization – keep the CPU as busy as
possible
 Throughput – # of processes that complete
their execution per time unit
 Turnaround time – amount of time to execute a
particular process
 Waiting time – amount of time a process has
been waiting in the ready queue
 Response time – amount of time it takes from
when a request was submitted until the first
response is produced, not output (for time-
sharing environment)

Scheduling Algorithm Optimization Criteria
 Max CPU utilization
 Max throughput
 Min turnaround time
 Min waiting time
 Min response time

First-Come, First-Served (FCFS)
Scheduling
Process Burst Time
P1 24
P2 3
P3 3
 Suppose that the processes arrive in the
order: P1 , P2 , P3
 The Gantt Chart for the schedule is:
 Waiting time for P1 = 0; P2 = 24; P3 = 27
 Average waiting time: (0 + 24 + 27)/3 = 17
P1 P2 P3
24 27 30
0

FCFS Scheduling (Cont.)
 Suppose that the processes arrive in the order:
P2 , P3 , P1
 The Gantt chart for the schedule is:
 Waiting time for P1 = 6; P2 = 0; P3 = 3
 Average waiting time: (6 + 0 + 3)/3 = 3
 Much better than previous case
 Convoy effect - short process behind long process
 Consider one CPU-bound and many I/O-bound processes
P1
P3
P2
6
3 30
0

Shortest-Job-First (SJF)
Scheduling
 Associate with each process the length of its
next CPU burst
 Use these lengths to schedule the process with
the shortest time
 SJF is optimal – gives minimum average
waiting time for a given set of processes
 The difficulty is knowing the length of the next
CPU request
 Could ask the user

Example of SJF
Process Burst Time
P1 0.0 6
P2 2.0 8
P3 4.0 7
P4 5.0 3
 SJF scheduling chart
 Average waiting time = (3 + 16 + 9 + 0) / 4 = 7
P4 P3
P1
3 16
0 9
P2
24

Determining Length of
Next CPU Burst
 Can only estimate the length – should be
similar to the previous one
 Then pick process with shortest predicted next
CPU burst
 Can be done by using the length of previous
CPU bursts, using exponential averaging
 Commonly, α set to ½
:
define
1,
0
,
for
Then
burst
CPU
next
for the
value
predicted
and
burst
CPU
of
length
actual
Let
1







n
th
n n
t
  .
1
1 n
n
n t 


 




Prediction of the Length of the
Next CPU Burst

Examples of Exponential Averaging
  =0
 n+1 = n
 Recent history does not count
  =1
 n+1 = tn
 Only the actual last CPU burst counts
 If we expand the formula, we get:
n+1 =  tn+(1 - ) tn -1 + …+(1 -  )j  tn -j + … +(1 -  )n +1 0
 Since both  and (1 - ) are less than or equal
to 1, each successive term has less weight
than its predecessor

 Associate with each process the length of its
next CPU burst and use these lengths to schedule
the process with the shortest time.
 SJF is optimal – gives minimum average waiting
time for a given set of processes.
 Two schemes:
 Nonpreemptive – once CPU given to the process it
cannot be preempted until completes its CPU burst.
 Preemptive – if a new process arrives with CPU burst
length less than remaining time of current executing
process, preempt. This scheme is know as the
Shortest-Remaining-Time-First (SRTF).
Shortest-Job-First (SJF)
Scheduling

Process Arrival Time Burst Time
P1 0 7
P2 2 4
P3 4 1
P4 5 4
 Now we add the concepts of varying arrival times
 Non-preemptive SJF Gantt Chart
 Average waiting time = (0 + 6 + 3 + 7)/4 = 4
Example of Non-Preemptive SJF
P1 P3 P2
7 16
0
P4
8 12

Example of Preemptive SJF (SRTF)
Process Arrival Time Burst Time
P1 0 7
P2 2 4
P3 4 1
P4 5 4
 Preemptive SJF Gantt Chart
 Average waiting time = (9 + 1 + 0 +2)/4 = 3
P1 P3
P2
4
2 11
0
P4
5 7
P2 P1
16

Example of SRTF
ProcessA Arrival TimeT Burst Time
P1 0 8
P2 1 4
P3 2 9
P4 3 5
 SRTF Gantt Chart
 Average waiting time = (9+0+15+2)/4 = 6.5
P1
P1
P2
1 17
0 10
P3
26
5
P4

Priority Scheduling
 A priority number (integer) is associated with
each process
 CPU is allocated to the process with the highest
priority (smallest integer  highest priority)
 Preemptive
 Non-preemptive
 SJF is priority scheduling where priority is the
inverse of predicted next CPU burst time
 Problem  Starvation – low priority processes
may never execute
 Solution  Aging – as time progresses increase
the priority of the process

Example of Priority Scheduling
ProcessAa Burst Time Priority
P1 10 3
P2 1 1
P3 2 4
P4 1 5
P5 5 2
 Priority scheduling Gantt Chart:
 Average waiting time = (6+0+16+18+1)/5 = 8.2
P2 P3
P5
1 18
0 16
P4
19
6
P1

Round Robin (RR)
 Each process gets a small unit of CPU time
(time quantum q), usually 10-100 milliseconds.
 After this time has elapsed, the process is preempted
and added to the end of the ready queue.
 If there are n processes in the ready queue and
the time quantum is q, then:
 Each process gets 1/n of the CPU time in chunks of at
most q time units at once.
 No process waits more than (n-1)q time units.
 Timer interrupts every q to schedule next process
 Performance
 q large  FIFO
 q small  q must be large with respect to context
switch, otherwise overhead is too high

Example of RR (Time Quantum = 4)
Process Burst Time
P1 24
P2 3
P3 3
 The Gantt chart is:
 Average waiting time = (6+4+7)/3 = 5.66
 Typically, higher average turnaround than SJF,
but better response
P1 P2 P3 P1 P1 P1 P1 P1
0 4 7 10 14 18 22 26 30

Process Burst Time
P1 53
P2 17
P3 68
P4 24
 The Gantt chart is:
 Average waiting time = (81+20+86+97)/4 = 71
Example of RR (Time Quantum = 20)
P1 P2 P3 P4 P1 P3 P4 P1 P3 P3
0 20 37 57 77 97 117 121 134 154 162

Time Quantum and Context Switch Time

Turnaround Time Varies With
The Time Quantum
80% of CPU bursts
should be shorter than q

Multilevel Queue
 Ready queue is partitioned into separate queues, eg:
 foreground (interactive)
 background (batch)
 Process permanently in a given queue
 Each queue has its own scheduling algorithm:
 foreground – RR
 background – FCFS
 Scheduling must be done between the queues:
 Fixed priority scheduling; (i.e., serve all from foreground
then from background).
 Possibility of starvation.
 Time slice – each queue gets a certain amount of CPU
time which it can schedule amongst its processes;
 i.e., 80% to foreground in RR, 20% to background in FCFS

Multilevel Queue Scheduling

Multilevel Feedback Queue
 A process can move between the various
queues; aging can be implemented this way
 Multilevel-feedback-queue scheduler defined by
the following parameters:
 number of queues
 scheduling algorithms for each queue
 method used to determine when to upgrade a
process
 method used to determine when to demote a process
 method used to determine which queue a process will
enter when that process needs service

Example of
 Three queues:
 Q0 – RR with time quantum 8 milliseconds
 Q1 – RR time quantum 16 milliseconds
 Q2 – FCFS
 Scheduling
 A new job enters queue Q0 which is served FCFS
 When it gains CPU, job receives 8 milliseconds
 If it does not finish in 8 milliseconds, job is moved to queue Q1
 At Q1 job is again served FCFS and receives 16
additional milliseconds
 If it still does not complete, it is preempted and moved to
queue Q2

Example of

Multiple-Processor Scheduling
 With multiple CPUs, load sharing becomes
possible but CPU scheduling more complex
 Homogeneous processors
 Can use any processor to run any process in the ready queue
 Asymmetric multiprocessing
 Only one master processor accesses system data structures
and other processors execute only user code
 Symmetric multiprocessing (SMP)
 Each processor is self-scheduling, all processes in common
ready queue, or each has its own private ready queue
 Currently, most common
 Processor affinity – process has affinity for processor
on which it is currently running
 soft affinity or hard affinity
 Variations including processor sets

NUMA and CPU Scheduling
Note that memory-placement
algorithms can also consider affinity

Multiple-Processor Scheduling –
Load Balancing
 On SMP, need to keep workload balanced among
all CPUs to fully utilize benefits of multiprocessors
 Load balancing attempts to keep workload
evenly distributed across all processors
 Necessary only when each processor has its own
private queue of ready processes
 Push migration – a task periodically checks
load on each processor, and if found imbalance
pushes task from overloaded CPU to other CPUs
 Pull migration – idle processors pulls waiting
task from busy processor

Algorithm Evaluation
 How to select CPU-scheduling algorithm for a
particular OS?
 there are many scheduling algorithms, each with its
own parameters.
 As a result, selecting an algorithm can be difficult.
 Determine criteria, then evaluate algorithms
 Various evaluation methods we can use:
 Deterministic modeling
 Queueing models
 Simulations
 Implementation

Deterministic Evaluation
 Type of analytic evaluation
 Takes a particular predetermined workload and
defines the performance of each algorithm for
that workload
 Consider 5 processes arriving at time 0:

Deterministic Evaluation
 For each algorithm, calculate minimum average
waiting time
 FCS is 28ms:
 Non-preemptive SFJ is 13ms:
 RR is 23ms:
 Simple and fast, but requires exact numbers for
input, and its answers apply only to those cases

Queueing Models
 Normally, there is no static set of processes to
use for deterministic modeling
 However, the distribution of CPU and I/O bursts
can be measured and then simply estimated
 Describes the arrival of processes, and CPU and I/O
bursts probabilistically
 Commonly exponential, and described by mean
 Computes verage throughput, utilization, waiting time
 Computer system described as network of
servers, each with queue of waiting processes
 Knowing arrival rates and service rates
 Computes utilization, average queue length, average
wait time, etc

Queueing Models
 Little’s Formula:
 n = average queue length
 W = average waiting time in queue
 λ = average arrival rate into queue
 Little’s law – in steady state, processes leaving
queue must equal processes arriving, thus:
n = λ x W
 Valid for any scheduling algorithm and arrival
distribution
 For example, if on average 7 processes arrive per
second, and normally 14 processes in queue, then
average wait time per process = 2 seconds

Simulations
 Queueing analysis is useful but has limitations
 Mathematics of complex algorithms and distributions
can be difficult to work with
 Thus, arrival and service distributions are defined in
mathematically tractable ways
 Simulations more accurate
 Programmed model of computer system
 Data structures represent components of the system
 Clock is a variable
 Gather statistics indicating algorithm performance
 Data to drive simulation gathered via
 Random number generator according to probabilities
 Distributions defined mathematically or empirically
 Trace tapes record sequences of real events in real systems

Evaluation of CPU Schedulers
by Simulation

Implementation
 Simulation can be expensive and limited accuracy
 A more detailed simulation provides more accurate
results, but it also takes more computer time.
 Only completely accurate way to implement new
scheduler and test in real systems
 Cost of coding the algorithm and risk of users reaction
 Changing the environment in which algorithm is used
 Most flexible scheduling algorithms that can be
altered by system managers and tuned for a
specific set of applications
 Use APIs that can modify priority of a process or thread
 But again environments vary

Operating System Concepts – 9th Edit9on
End of Chapter 6

W-ch06.pdfcentral processing unit scheduling

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