Chapter 5, CPU Scheduling
1
5.1 Basic Concepts
• The goal of multi-programming is to maximize
the utilization of the CPU as a system resource
by having a process running on it at all times
• Supporting multi-programming means
encoding the ability in the O/S to switch
between currently running jobs
• Switching between jobs can be nonpreemptive or preemptive
2
• Simple, non-preemptive scheduling means that a
new process can be scheduled on the CPU only
when the current job has begun waiting, for I/O,
for example
• Non-preemptive means that the O/S will not
preempt the currently running job in favor of
another one
• I/O is the classic case of waiting, and it is the
scenario that is customarily used to explain
scheduling concepts
3
The CPU-I/O Burst Cycle
• A CPU burst refers to the period of time when
a given process occupies the CPU before
making an I/O request or taking some other
action which causes it to wait
• CPU bursts are of varying length and can be
plotted in a distribution by length
4
• Overall system activity can also be plotted as a
distribution of CPU and other activity bursts
by processes
• The distribution of CPU burst lengths tends to
be exponential or hyperexponential
5
6
The CPU scheduler = the short term
scheduler
• Under non-preemptive scheduling, when the
processor becomes idle, a new process has to be
picked from the ready queue and have the CPU
allocated to it
• Note that the ready queue doesn’t have to be
FIFO, although that is a simple, initial assumption
• It does tend to be some sort of linked data
structure with a queuing discipline which
implements the scheduling algorithm
7
Preemptive scheduling
• Preemptive scheduling is more advanced than
non-preemptive scheduling.
• Preemptive scheduling can take into account
factors besides I/O waiting when deciding
which job should be given the CPU.
• A list of scheduling points will be given next.
• It is worthwhile to understand what it means.
8
• Scheduling decisions can be made at these
points:
1. A process goes from the run state to the wait
state (e.g., I/O wait, wait for a child process to
terminate)
2. A process goes from the run state to the ready
state (e.g., as the result of an interrupt)
3. A process goes from the wait state to the ready
state (e.g., I/O completes)
4. A process terminates
9
• Scheduling has to occur at points 1 and 4.
• If it only occurs then, this is non-preemptive
or cooperative scheduling
• If scheduling is also done at points 2 and 3,
this is preemptive scheduling
10
• Points 1 and 4 are given in terms of the job
that will give up the CPU.
• Points 2 and 3 seem to relate to which process
might become available to run that could
preempt the currently running process.
11
• Historically, simple systems existed without
timers, just like they existed without mode
bits, for example
• It is possible to write a simple, nonpreemptive operating system for multiprogramming without multi-tasking
• Without a timer or other signaling, jobs could
only be switched when one was waiting for
I/O
12
• However, recall that much of the discussion in
the previous chapters assumed the use of
interrupts, timers, etc., to trigger a context
switch
• This implies preemptive scheduling
• Preemptive schedulers are more difficult to
write than non-preemptive schedulers, and
they raise complex technical questions
13
• The problem with preemption comes from
data sharing between processes
• If two concurrent processes share data,
preemption of one or the other can lead to
inconsistent data, lost updates in the shared
data, etc.
14
• Note that kernel data structures hold state for
user processes.
• The user processes do not directly dictate
what the kernel data structures contain, but
by definition, the kernel loads the state of >1
user process
15
• This means that the kernel data structures
themselves have the characteristic of data
shared between processes
• As a consequence, in order to be correctly
implemented, preemptive scheduling has to
prevent inconsistent state in the kernel data
structures
16
• Concurrency is rearing its ugly head again,
even though it still hasn’t been thoroughly
explained.
• The point is that it will become apparent that
concurrency is a condition that is inherent to a
preemptive scheduler.
• Therefore, a complete explanation of
operating systems eventually requires a
complete explanation of concurrency issues.
17
• The idea that the O/S is based on shared data
about processes can be explained concretely by
considering the movement of PCB’s from one
queue to another
• If an interrupt occurs while one system process is
moving a PCB, and the PCB has been removed
from one queue, but not yet added to another,
this is an error state
• In other words, the data maintained internally by
the O/S is now wrong/broken/incorrect…
18
Possible solutions to the problem
• So the question becomes, can the scheduler
be coded so that inconsistent queue state
couldn’t occur?
• One solution would be to only allow switching
on I/O blocks.
• The idea is that interrupts will be queued
rather than instantaneous (a queuing
mechanism will be needed)
19
• This means that processes will run to a point
where they can be moved to an I/O queue and
the next process will not be scheduled until
that happens
• This solves the problem of concurrency in
preemptive scheduling in a mindless way
• This solution basically means backing off to
non-preemptive scheduling
20
Other solutions to the problem
• 1. Only allow switching after a system call
runs to completion.
• In other words, make kernel processes
uninterruptible.
• If the code that moves PCB’s around can’t be
interrupted, inconsistent state can’t result.
• This solution also assumes a queuing system
for interrupts.
21
• 2. Make certain code segments in the O/S
uninterruptible.
• This is the same idea as the previous one, but
with finer granularity.
• It increases concurrency because interrupts
can at least occur in parts of kernel code, not
just at the ends of kernel code calls.
22
• Note that interruptibility of the kernel is
related to the problem of real time operating
systems
• If certain code blocks are not interruptible,
you are not guaranteed a fixed, maximum
response time to any particular system
request or interrupt that you generate
23
• You may have to wait an indeterminate
amount of time while the uninterruptible
code finishes processing
• This violates the requirement for a hard realtime system
24
Scheduling and the dispatcher
• The dispatcher = the module called by the
short term scheduler which
– Switches context
– Switches to user mode
– Jumps to the location in user code to run
• Speed is desirable.
• Dispatch latency refers to time lost in the
switching process
25
Scheduling criteria
• There are various algorithms for scheduling
• There are also various criteria for evaluating
them
• Performance is always a trade-off
• You can never maximize all of the criteria with
one scheduling algorithm
26
Criteria
• CPU utilization. The higher, the better. 40%-90%
is realistic
• Throughput = processes completed / unit time
• Turnaround time = total time for any single
process to complete
• Waiting time = total time spent waiting in O/S
queues
• Response time = time between submission and
first visible sign of response to the request—
important in interactive systems
27
• Depending on the criterion, you may want to:
• Strive to attain an absolute maximum or
minimum (utilization, throughput)
• Minimize or maximize the average
(turnaround, waiting)
• Minimize or maximize the variance (for timesharing, minimize the variance, for example)
28
5.3 Scheduling Algorithms
•
•
•
•
•
•
5.3.1
5.3.2
5.3.3
5.3.4
5.3.5
5.3.6
First-Come, First-Served (FCFS)
Shortest-Job-First (SJF)
Priority
Round Robin (RR)
Multilevel Queue
Multilevel Feedback Queue
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• Reality involves a steady stream of many, many CPU
bursts
• Reality involves balancing a number of different
performance criteria or measures
• Examples of the different scheduling algorithms will be
given below based on a very few processes and a
limited number of bursts
• The examples will be illustrated using Gantt charts
• The scheduling algorithms will be evaluated and
compared based on a simple measure of average
waiting time
30
FCFS Scheduling
• The name, first-come, first-served, should be
self-explanatory
• This is an older, simpler scheduling algorithm
• It is non-preemptive
• It is not suitable for interactive time sharing
• It can be implemented with a simple FIFO
queue of PCB’s
31
•
•
•
•
•
Consider the following scenario
Process
Burst length
P1
24 ms.
P2
3 ms.
P3
3 ms.
32
Avg. wait time = (0 + 24 + 27) / 3 =
17 ms.
33
• Compare with a different arrival order:
• P2, P3, P1
34
Avg. wait time = (0 + 3 + 6) / 3 =
3 ms.
35
• Additional comments on performance analysis
• It is clear that average wait time varies greatly
depending on the arrival order of processes
and their varying burst lengths
• As a consequence, it is also possible to
conclude that for any given set of processes
and burst lengths, arbitrary FCFS scheduling
does not result in a minimal or optimal
average wait time
36
• FCFS scheduling is subject to the convoy effect
• There is the initial arrival order of process
bursts
• After that, the processes enter the ready
queue after I/O waits, etc.
• Let there be one CPU bound job (long CPU
burst)
• Let there be many I/O bound jobs (short CPU
bursts)
37
• Scenario:
• The CPU bound job holds the CPU
• The other jobs finish their I/O waits and enter
the ready queue
• Each of the other jobs is scheduled, FCFS, and
is quickly finished with the CPU due to an I/O
request
• The CPU bound job then takes the CPU again
38
• CPU utilization may be high (good) under this
scheme
• The CPU bound job is a hog
• The I/O bound jobs spend a lot of their time
waiting
• Therefore, the average wait time will tend to be
high
• Recall that FCFS is not preemptive, so once the
jobs have entered, scheduling only occurs when a
job voluntarily enters a wait state due to an I/O
request or some other condition
39
SJF Scheduling
• The name, shortest-job-first, is not quite selfexplanatory
• Various ideas involved deserve explanation
• Recall that these thumbnail examples of
scheduling are based on bursts, not the overall
job time
• For scheduling purposes, it is the length of the
next burst that is important
• There is no perfect way of predicting the length
of the next burst
40
• Implementing SJF in reality involves devising
formulas for predicting the next burst length
based on past performance
• SJF can be a non-preemptive algorithm. The
assumption now is that all processes are
available at time 0 for scheduling and the
shortest is chosen
• A more descriptive name for the algorithm is
“shortest next CPU burst” scheduling
41
• SJF can also be implemented as a preemptive
algorithm. The assumption is that jobs enter
the ready queue at different times. If a job
with a shorter burst enters the queue when a
job with a longer burst is running, the shorter
job preempts the longer one
• Under the preemptive scenario a more
descriptive name for the algorithm would be
“shortest remaining time first” scheduling
42
Non-preemptive Example
•
•
•
•
•
•
Consider the following scenario:
Process
burst length
P1
6 ms.
P2
8 ms.
P3
7 ms.
P4
3 ms.
43
SJF order: P4, P1, P3, P2
average wait time = (0 + 3 + 9 + 16) / 4 =
7 ms.
44
SJF average wait time is lower than the average wait
time for FCFS scheduling of the same processes: FCFS
average wait time = (0 + 6 + 14 + 21) / 4 =
10.25 ms.
45
• In theory, SJF is optimal for average wait time
performance
• Always doing the shortest burst first minimizes
the aggregate wait time for all processes
• This is only theoretical because burst length can’t
be known
• In a batch system user estimates might be used
• In an interactive system user estimates make no
sense
46
• Devising a formula for predicting burst time
• The only basis for such a formula is past performance
• What follows is the definition of an exponential
average function for this purpose
• Let tn = actual, observed length of nth CPU burst for a
given process
• Let Tn+1 = predicted value of next burst
• Let a be given such that 0 <= a < 1
• Then define Tn+1 as follows:
• Tn+1 = atn + (1 – a)Tn
47
• Explanation:
• a is a weighting factor. How important is the
most recent actual performance vs.
performance before that
• To get an idea of the function it serves,
consider a = 0, a = ½, a = 1
48
• Tn appears in the formula. It is the previous
prediction.
• It includes real past performance because
• Tn = atn-1 + (1 – a)Tn-1
• Ultimately this expansion depends on the
initial predicted value, T0
• Some arbitrary constant can be used, a system
average can be used, etc.
49
• Expanding the formula
• This illustrates how come it is known as an
exponential average
• It gives a better feel for the role of the
components in the formula
• Tn+1 = atn + (1-a)(atn-1 + (1-a)(…at0 + (1-a)T0)…)
• = atn + (1-a)atn-1 + (1-a)2atn-2 + … + (1-a)nat0 +
(1-a)n+1T0
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•
•
•
•
•
•
The first term is:
atn
The general term is:
(1 – a)jatn-j
The last term is:
(1 – a)n+1T0
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• In words
• The most recent actual performance, tn, gets
weight a
• All previous performances, ti, are multiplied by a
and by a factor of (1 – a)j, where the value of j is
determined by how far back in time t occurred
• Since (1 – a) < 1, as you go back in time, the
weight of a given term on the current prediction
is exponentially reduced
52
• The following graph illustrates the results of
applying the formula with T0 = 10 and a = ½
• With a = ½, the exponential coefficients on the
terms of the prediction are ½, (½)2, (½)3, …
• Note that the formula tends to produce a
lagging, not a leading indicator
• In other words, as the actual values shift up or
down, the prediction gradually approaches
the new reality, whatever it might be
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14
12
10
8
6
4
actual burst length
2
predicted burst
0
54
Preemptive SJF
• If a waiting job enters the ready queue with an
estimated burst length shorter than the time
remaining of the burst length of the currently
running job, then the shorter job preempts
the one on the CPU.
• This can be called “shortest remaining time
first” scheduling.
• Unlike in the previous examples, the arrival
time of a process now makes a difference
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•
•
•
•
•
•
Consider the following scenario:
Process
arrival time
burst length
P1
0
8 ms.
P2
1
4 ms.
P3
2
9 ms.
P4
3
5 ms.
56
Preemptive SJF average wait time =
(0 + 9 + 0 + 15 + 2) / 4 =
6.5 ms.
57
Walking through the example
• P1 arrives at t = 0 and starts
• P2 arrivess at t = 1
– P2’s burst length = 4
– P1’s remaining burst length = 8 – 1 = 7
– P2 preempts
• P3 arrives at t = 2
–
–
–
–
P3’s burst length burst length = 9
P2’s remaining burst length = 4 – 1 = 3
P1’s remaining burst length = 7
No preemption
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• P4 arrives at t = 3
–
–
–
–
–
•
•
•
•
P4’s burst length = 5
P3’s remaining burst length = 9
P2’s remaining burst length = 3 – 1 = 2
P1’s remaining burst length = 7
No preemption
P2 runs to completion at t = 5
P4 is scheduled. It runs to completion at t = 10
P1 is rescheduled. It runs to completion at 17
P3 is scheduled. It runs to completion at 26
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Calculating the wait times for the
example
• P1 has 2 episodes
– 1st, enters at t = 0, starts at t = 0, wait time = 0
– 2nd, waits from t = 1 to t = 10, wait time = 10 – 1 = 9
– Total P1 wait time = 0 + 9
• P2 has 1 episode
– Enters at t = 1, starts at t = 1, wait time = 1 – 1 = 0
• P3 has 1 episode
– Enters at t = 2, starts at t = 17, wait time = 17 – 2 = 15
• P4 has 1 episode
– Enters at t = 3, starts at t = 5, wait time = 5 – 3 = 2
• Total wait time = 0 + 9 + 0 + 15 + 2 = 26
• Average wait time = 26 / 4 = 6.5
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The same processes under nonpreemptive SJF
61
•
•
•
•
•
•
P1 wait = 0 – 0 = 0
P2 wait = 8 – 1 = 7
P3 wait = 17 – 2 = 15
P4 wait = 12 – 3 = 9
Total wait time = 0 + 7 + 15 + 9 = 31
Average wait time = 31 / 4 = 7.75
62
Priority Scheduling
• A priority is assigned to each process
• High priority processes are scheduled before low
priority ones
• Processes of equal priority are handled in FCFS
order
• In the textbook a high priority process is given a
low number and a low priority process is given a
high number, e.g., 0-7, 0-4095
• Note that SJF is a type of priority scheduling
where the priority is inversely proportional to the
predicted length of the next burst
63
Priority Example
•
•
•
•
•
•
•
Consider the following scenario:
Process
burst length
priority
P1
10 ms.
3
P2
1 ms.
1
P3
2 ms.
4
P4
1 ms.
5
P5
5 ms.
2
64
Average wait time = (0 + 1 + 6 + 16 + 18) / 5 = 41 / 5 =
8.2 ms.
65
• Internal priority setting
• SJF is an example
• Other criteria that have been used:
– Time limits
– Memory requirements
– (I/O burst) / (CPU burst)
66
• External priority setting:
– Importance of process
– Type or amount of funding
– Sponsoring department
– politics
67
• Priority scheduling can be either preemptive or
non-preemptive
• Priority scheduling can lead to indefinite blocking
= process starvation
• Low priority jobs may be delayed until low load
times
• Low priority jobs might be lost (in system crashes,
e.g.) before they’re finished
• Solution to starvation: aging. Raise a process’s
priority by n units for every m time units it’s been
in the system
68
Round Robin Scheduling
• This is the time-sharing scheduling algorithm
• It is FCFS with fixed time-slice preemption
• The time slice, or time quantum, is in the range of 10ms.100ms.
• The ready queue is a circularly linked list
• The scheduler goes around the list allocating 1 quantum
per process
• A process may block (I/O, e.g.) before the quantum is over
• When an unfinished process leaves the CPU, it is added to
the “tail” of the circularly linked list
• The tail “moves”. It is the point behind the currently
scheduled process
69
70
• RR scheduling depends on a hardware timer
• The tradeoff in RR scheduling is fairness in
dividing up the CPU as a shared resource
• Vs. long average waiting times for all
processes contending for it
• If this is interactive time-sharing, the waiting
for human I/O will far outweigh the waiting
time for access to the CPU
71
RR Example
•
•
•
•
•
•
Consider the following scenario:
Let the time slice be 4 ms.
Process
burst length
P1
24 ms.
P2
3 ms.
P3
3 ms.
72
Average wait time = (0 + 6 + 4 + 7) / 3 =
17 / 3 = 5 2/3
73
• Wait time for P1 = 0 initially
• Wait time for P1 = 10 – 4 = 6 when scheduled
again
• Wait time for P2 = 4
• Wait time for P3 = 7
74
• The performance of round robin depends on
the length of the time slice
• If the length of the slice is > any single process
burst, then RR = FCFS
• If the slice is short, then in theory a machine
with n users behaves like n machines, each
1/nth as fast as the actual machine
• This is the ideal, which ignores the overhead
from switching between jobs
75
• A simple measure to gauge overhead cost is: (context
switch time) / (time slice length)
• In order for time sharing to be practical, this ratio has
to be relatively small
• The size of the ratio is dependent on hardware speed
and O/S code efficiency (speed)
• Note that even if the ratio is acceptable, the number of
users determines 1/n. The actual system speed
determines how small you can make a time slice (slices
per unit time) and how many users you can practically
support at one time
76
• Round robin scheduling conveniently
illustrates other performance parameters
besides average waiting time
• Consider overall average process turnaround
time as a function of time slice size
• Smaller time slices mean more context
switching overhead on a percentage basis
• They also mean longer delays as each process
has to wait for multiple slices
77
• On the other hand, if time slices are long,
scheduling can degenerate into FCFS
• FCFS doesn’t fairly allocate the CPU in a time
sharing environment
• The rule of thumb for system design and
tuning is that 80% of all process CPU bursts
should finish within 1 time slice
• Empirically, this shares the CPU while still
achieving reasonable performance
78
RR time slice size variations
•
•
•
•
•
•
Consider the following scenario:
Process
burst length
P1
6 ms.
P2
3 ms.
P3
1 ms.
P4
7 ms.
79
Average turnaround time = (14 + 7 + 8
+ 17) / 4 = 46 / 4 = 11 ½
80
Average wait time = ((0 + 8) + 4 + 7 + (8
+ 2)) / 4 = 29 / 4 = 7 ¼
81
• Average waiting time and average turnaround
time ultimately measure the same thing
• Average turnaround time varies as the time
slice size varies
• However, it doesn’t vary in a regular fashion
• Depending on the relative length of process
bursts and time slice size, a larger slice may
lead to slower turnaround
82
14
12
10
8
6
average turnaround time
4
average waiting time
2
0
time time time time time time time
slice slice slice slice slice slice slice
size 1 size 2 size 3 size 4 size 5 size 6 size 7
83
• Keep in mind that all of these examples are
thumbnails
• They are designed to give some idea of what’s
going on, but they are not realistic in size
• In real life design and tuning would be based
on an analysis of a statistically significant mass
(relatively large) of historical or ongoing data
84
Multi-level Queue Scheduling
•
•
•
•
A simple example
Let interactive jobs be foreground jobs
Let batch jobs be background jobs
Let foreground and background be distinguished
by keeping the jobs in separate queues where the
queues have separate queuing
disciplines/scheduling algorithms
• For example, use RR scheduling for foreground
jobs
• Use FCFS for batch jobs
85
• The follow-up question becomes, how do you
coordinate scheduling between the two queues?
• One possibility: Fixed priority preemptive
scheduling. Batch jobs only run if the interactive
queue is empty
• Another possibility: Time slicing. For example,
the interactive queue is given 80% of the time
slices and the batch queue is given 20%
86
• Let different classes of jobs be permanently
assigned to different queues
• Let the queues have priorities relative to each
other
• Let each queue implement its own scheduling
algorithm for the processes in it, which are of
equal priority
87
An Example
88
• The coordination between queues would be
similar to the interactive/batch example
• Fixed priority preemptive scheduling would mean
that any time a job entered a queue of a higher
priority, any currently running job would have to
step aside
• Lower priority jobs could only run if all higher
priority queues were empty
• You could time slice between the queues, giving a
certain percent of CPU time to each one
89
Multi-level Feedback Queue
Scheduling
• This introduces the possibility that processes
move between queues
• This may be based on characteristics such as CPU
or I/O usage or time spent in system
• In general, CPU greedy processes can be moved
to a lower queue
• This gives interactive jobs and I/O bound jobs
with shorter CPU bursts higher priority
• It can also handle ageing. If a job is in a lower
priority queue too long, it can be moved to a
higher one, preventing starvation
90
An Example
91
Queuing Discipline
• 1. The relative priority of the queues is fixed.
– Jobs in queue 1 execute only if queue 0 is empty.
– Jobs in queue 2 execute only if queue 1 is empty.
• 2. Every new job enters queue 0.
– If its burst is <= 8, it stays there.
– Otherwise, it’s moved to queue 1.
• 3. When a job in queue 1 is scheduled
– If it has a burst length > 16, it’s preempted and
moved to queue 2.
92
• 4. Jobs can move back up to a different queue
if their burst lengths are within the quantum
of the higher priority queue.
• 5. Note that in a sense, this queuing scheme
predicts future performance on the basis of
the most recent burst length.
93
Defining Characteristics of a General MultiLevel Feedback Queue Scheduling System
• 1. The number of queues.
• 2. The scheduling algorithm for each queue.
• 3. The method used to determine when to
upgrade a process.
• 4. The method used to determine when to
downgrade a process.
• 5. The method used to determine which
queue a job will enter when it needs service
(initially).
94
• Multi-level feedback queue systems are the most
general and the most complex.
• The example given was simply that, an example.
• In theory, such a system can be configured to
perform well for a particular hardware
environment and job mix.
• In reality, there are no ways of setting the
scheduling parameters except for experience,
analysis, and trial and error.
95
5.4 Multiple Processor Scheduling
• Load sharing = the possibility of spreading
work among >1 processor, assuming you can
come up with a scheduling algorithm.
• Homogeneous systems = each processor is the
same. Any process can be assigned to any
processor in the system.
• Even in homogeneous systems, a process may
be limited to a certain processor if a needed
peripheral is attached to that processor
96
Approaches to Multiple Processor
Scheduling
• Asymmetric multi-processing = master-slave architecture.
The scheduling code runs on one processor only.
• Symmetric Multi-Processing (SMP) = each processor is selfscheduling.
• There is still the question of whether the ready queue is
local or global.
• To maximize concurrency, you need a global ready queue.
• Maintaining a global ready queue requires cooperation
(concurrency control).
• This is a difficult problem, so most systems maintain local
ready queues for each processor.
• Most modern O/S’s support SMP: Windows, Solaris, Linux,
Max OS X.
97
Processor Affinity
• This term refers to trying to keep the same job
on the same processor.
• Moving jobs between processors is expensive.
• Everything that might have been cached
would be lost unless explicitly recovered.
• Soft affinity = not guaranteed to stay on the
same processor.
• Hard affinity = guaranteed to stay on the same
processor.
98
Load Balancing
• This term refers to trying to keep all
processors busy at all times.
• This is an issue if there are at least as many
jobs as there are processors.
• If a global ready queue is implemented, load
balancing would naturally be part of the
algorithm.
99
• If a system only maintains local ready queues and
there is not hard affinity, there are two
approaches to moving jobs among processors:
• Push migration = a single system process
regularly checks processor utilization and pushes
processes from busy processors to idle ones.
• Pull migration = an idle processor reaches into
the ready queue of a busy processor and extracts
a process for itself.
100
• Both kinds of migration can be built into a
system (Linux for example).
• By definition, migration and affinity are in
opposition.
• There is a performance trade-off.
• Some systems try to gauge imbalance in load
and only do migration if the imbalance rises
above a certain threshold.
101
• Symmetric multi-threading = SMT
• Definition: Provide multiple logical processors
rather than multiple physical processors.
• This is known as hyperthreading on Intel chips.
• At a hardware level:
– Each logical processor has its own architecture state
(register values).
– Each logical processor receives and handles its own
interrupts.
– All hardware resources are shared.
102
• An O/S doesn’t have to be designed specifically
for SMT.
• SMT should be transparent—the machine “looks
like” an SMP machine.
• A system may combine SMT and SMP—I.e., there
would be >1 logical processor on each of >1
physical processor.
• If the O/S is system aware, it could be written to
avoid this scheduling case: >1 process on >1 local
processor of 1 physical processor while another
physical processor is idle.
103
Thread Scheduling
• This is essentially an expansion on ideas raised in
the last chapter.
• The term “contention scope” refers to the level at
which scheduling is occurring.
• Process Contention Scope (PCS) = the scheduling
of threads on lightweight processes.
• In many-to-one or many-to-many schemes,
threads of one or more user processes contend
with each other to be scheduled.
• This is usually priority based, but not necessarily
preemptive.
104
• System contention scope (SCS) = the
scheduling of kernel level threads on the
actual machine.
• In a one-to-one mapping scheme, these kernel
threads happen to represent user threads
belonging to one or more processes.
105
Operating System Examples
• In most previous chapters, the O/S example
sections have been skipped because they
involve needless detail.
• Concrete examples will be covered here for
two reasons:
– To give an idea of how complex real system are.
– To show that if you know the basic principles, you
can tease apart the different pieces of an actual
implementation.
106
Solaris Scheduling
• Solaris scheduling is based on four priority
classes:
– Real time
– System
– Time sharing
– Interactive
107
• Practical points of Solaris scheduling:
– High numbers = high priority, range of values: 059
– The four different priority classes are
implemented in three queues (3 and 4 are
together).
– The distinction between 3 and 4 is that if a
process requires the generation of windows, it is
given a higher priority.
108
• There is an inverse relationship between
priority and time slice size.
– A small time slice = quick response for high
priority (interactive type) jobs.
– A large time slice = good throughput for low
priority (CPU bound) jobs.
109
Solaris Scheduling Queue—Notice that
Jobs Don’t Move Between Queues
110
Solaris Dispatch Table for Interactive and Time-sharing Threads
Starting Priority
Allocated Time Quantum
200
New Priority after
Quantum Expiration
0
New Priority after Return
from Sleep
50
0
5
200
0
50
10
160
0
51
15
160
5
51
20
120
10
52
25
120
15
52
30
80
20
53
35
80
25
54
40
40
30
55
45
40
35
56
50
40
40
58
55
40
45
58
59
20
49
59
111
Later Versions of Solaris Add these
Details
•
•
•
•
Fixed priority threads
Fair share priority threads
System processes don’t change priorities
Real-time processes have the absolutely highest
priorities
• Each scheduling class has a set of priorities.
These are translated into global priorities and the
schedule uses the global priorities to schedule
• Among threads of equal priority, the scheduler
does RR scheduling
112
Windows XP Scheduling
• XP (kernel) thread scheduling is priority based
preemptive
• This supports soft real-time applications
• There are 32 priorities, 0-31
• A high number = a high priority
• There is a separate queue for each priority
• Priority 0 is used for memory management
and will not come up further
113
• There is a relationship between priorities in
the dispatcher and classes of jobs defined in
the Win32 API
• There are 6 API classes divided into 2 groups
according to the priorities they have
114
• Class: Real time. Priorities: 16-31
• Variable (priority) classes:
– High priority
– Above normal priority
– Normal priority
– Below normal priority
– Idle priority
• The priorities of these classes can vary from 115
115
• Within each class there are 7 additional
subdivisions:
– Time critical
– Highest
– Above normal
– Below normal
– Lowest
– idle
116
• Each thread has a base priority
• This corresponds to the relative priority it’s
given within its class
• The default base value would be the “normal”
relative priority for the class
• The distribution of values among classes and
relative priorities is shown in the following
table
117
Columns = Priority Classes, Rows = Relative Priorities within Classes
The ‘Normal’ row contains the base priorities for the classes.
Real-time
High
Above
normal
Normal
Below
normal
Idle
priority
Timecritical
31
15
15
15
15
15
Highest
26
15
13
10
8
6
Above
normal
25
14
11
9
7
5
Normal
24
13
10
8
6
4
Below
normal
23
13
9
7
5
3
Lowest
22
11
8
6
4
2
Idle
16
1
1
1
1
1
118
• The scheduling algorithm dynamically changes a
thread’s priority if it’s in the variable group
• If a thread’s time quantum expires, it’s priority is
lowered, but not below its base priority
• When a thread is released from waiting, it’s
priority is raised.
• How much it’s raised depends on what it was
waiting for. For example:
– Waiting for keyboard I/O—large raise
– Waiting for disk I/O—smaller raise
119
• For an interactive process, if the user thread is given a
raise, the windowing process it’s running in is also
given a raise
• These policies favor interactive and I/O bound jobs and
attempt to control threads that are CPU hogs
• XP has another feature that aids windowing
performance
• If several process windows are on the screen and one is
brought to the foreground, it’s time quantum is
increased by a factor such as 3 so that it can get
something done before being preempted.
120
Linux Scheduling
• Skip this
• Two concrete examples are enough
121
Java Scheduling
• The JVM scheduling specification isn’t detailed
• Thread scheduling is supposed to be priority
based
• It does not have to be preemptive
• Round-robin scheduling is not required, but a
given implementation may have it
122
• If a JVM implementation doesn’t have timeslicing or preemption, the programmer can try
to devise cooperative multi-tasking in
application code
• The relevant Java API method call is
Thread.yield();
• This can be called in the run() method of a
thread at the point where it is willing to give
up the CPU to another thread
123
• Java Thread class name priorities:
– Thread.MIN_PRIORITY (value = 1)
– Thread.MAX_PRIORITY (value = 10)
– Thread.NORM_PRIORITY (value = 5)
• A new thread is given the priority of the
thread that created it
• The default priority is NORM
• The system doesn’t change a thread’s priority
124
• The programmer can assign a thread a priority value in the
range 1-10
• The relevant Java API method call is:
Thread.currentThread().setPriority(value);
• This is done in the thread’s run() method
• This specification isn’t foolproof though
• Java thread priorities have to be mapped to O/S kernel
thread priorities
• If the difference between Java priorities isn’t great enough,
they may be mapped to the same priority in the
implementing system
• The author gives Windows NT as an example where this can
happen
125
Algorithm Evaluation
• In general, algorithm selection is based on
multiple criteria. For example:
• Maximiz CPU utilization under the constraint
that maximum response time is <1 second
• Maximize throughput such that turnaround
time, on average, is linearly proportional to
total execution time
126
Deterministic Modeling
• This is a form of analytic evaluation
• For a given set of jobs, with all parameters
known, you can determine performance under
various scheduling scenarios
• This is OK for developing examples and exploring
possibilities
• It’s not generally a practical way to pick a
scheduling algorithm for a real system with an
unknown mix of jobs
• The thumbnail analyses with Gantt charts are a
simplified example of deterministic modeling
127
Queuing Models
• These are basically statistical models where
the statistical assumptions are based on past
observation of real systems
• The first distribution of interest
• Arrival of jobs into the system
• Typically Poisson
128
• All of the rest of the distributions tend to be
exponential
– CPU burst occurrence distribution
– CPU burst length distribution
– I/O burst occurrence distribution
– I/O wait length distribution
129
• Given these distributions it is possible to
calculate:
• Throughput
• CPU utilization
• Waiting time
130
A Simple Example of an Analysis
Formula
•
•
•
•
•
•
•
•
•
Let the following parameters be given:
N = number of processes in a queue
L = arrival rate of processes
W = average waiting time in queue
Then
N=L*W
This is known as Little’s formula
Given any two of the parameters, the third can be calculated
Note that the formula applies when the system is in a steady
state—the number of processes entering the queue = the number
of processes leaving the queue
• Increase of decrease in the queue length occurs when the system is
not in steady state
131
• Queuing models are not perfect
• They are limited by the match or mismatch between
the chose distributions and actual behavior
• They are simplifications because they aggregate
behavior and may overlook some factors
• They rely on mathematical assumptions (they treat
arrival, service, and waiting as mathematically
independent distributions, when for each
process/burst these successive events are related)
• They are useful for getting ideas, but they do not
perfectly match reality
132
Simulation Modeling
• Basic elements of a simulation:
– A clock (discrete event simulation)
– Data structures modeling state
– Modules which model activity which changes
state
133
• Simulation input:
• Random number generation based on
statistical distributions for processes (again,
mathematical simplification)
• Trace tapes. These are records of events in
actual runs. They provide an excellent basis
for comparing two algorithms
134
• Simulation models can generate statistics on all
aspects of performance under the simulated
workload
• Obtaining suitable input data and coding the
simulation are not trivial tasks.
• Coding and O/S and living with the
implementation choices it embodies are also not
trivial
• Making the model may be worth the cost if it aids
in developing the O/S
135
Implementation
• Implementation is the gold standard of
algorithm evaluation and system testing
• You code an algorithm, install it in the O/S,
and test it under real conditions
• Problems:
– Coding cost
– Installation cost
– User reactions to modifications
136
User Reactions
• Changes in O/S code result from perceived
shortcomings in performance
• Performance depends on the algorithm, the
mix of jobs, and the behavior of jobs
• If the algorithm is changed, users will changed
their code and behavior to adapt to the
altered O/S runtime environment
• This can cause the same performance problem
to recur, or a new problem to occur
137
• Examples:
• If job priority is gauged by size (smaller jobs
given higher priority), programmers may break
their applications into separate processes
• If job priority is gauged by frequency of I/O
(I/O bound processes are given higher
priority), programmers may introduce
(needless) I/O into their applications
138
• Without resorting to subterfuge, a Java
application programmer has some control
over behavior in a single application by
threading and using calls like yield() and
setPriority()
• A large scale O/S will be tunable. The system
administrator will be able to set scheduling
algorithms and their parameters to meet the
job mix at any given time
139
The End
140