Chapter 5, CPU Scheduling

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 non-preemptive or preemptive

• 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

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

• 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

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

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.

• 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

• 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

• 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.

• Historically, simple systems existed without timers, just like they existed without mode bits, for example

• It is possible to write a simple, non-preemptive operating system for multi-programming without multi-tasking

• Without a timer or other signaling, jobs could only be switched when one was waiting for I/O

• 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

• 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.

• 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

• 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

• 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.

• 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…

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)

• 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

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.

• 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.

• 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

• You may have to wait an indeterminate amount of time while the uninterruptible code finishes processing

• This violates the requirement for a hard real-time system

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

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

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– This is important in interactive systems

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 time-sharing, minimize the variance, for example)

5.3 Scheduling Algorithms

• 5.3.1 First-Come, First-Served (FCFS)• 5.3.2 Shortest-Job-First (SJF)• 5.3.3 Priority• 5.3.4 Round Robin (RR)• 5.3.5 Multilevel Queue• 5.3.6 Multilevel Feedback Queue

• Reality involves a steady stream of many, many CPU bursts

• Reality involves balancing a number of different performance criteria or measures

• It is worth keeping these facts in the back of your mind

• Examples of the different scheduling algorithms will be given below but these examples will be based on a very few processes and a limited number of bursts

• The examples will be given as a short list of processes and their burst times.

• This information will be turned into simple Gantt charts which make it possible to visualize the situation.

• The scheduling algorithms will be evaluated and compared based on a simple measure of average waiting time.

• It is relatively simple to see the values needed for the calculation using a Gantt chart.

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

(ready, or scheduling) queue of PCB’s

• Consider the following scenario• Process Burst length• P1 24 ms.• P2 3 ms.• P3 3 ms.

Avg. wait time = (0 + 24 + 27) / 3 =17 ms.

• Compare with a different arrival order:• P2, P3, P1

Avg. wait time = (0 + 3 + 6) / 3 =3 ms.

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

• 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)

• 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

• 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

SJF Scheduling

• The name, shortest-job-first, is not quite self-explanatory

• 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

• In a sense, SJF is really shortest-next-CPU burst-first scheduling

• There is no perfect way of predicting the length of the next burst

• Implementing SJF in practice involves devising formulas for predicting the next burst length based on past performance

• SJF can be a non-preemptive algorithm. • When one process relinquishes the CPU for an

I/O wait, for example, all other processes are available for scheduling at this time t = 0

• The job with the shortest predicted next CPU burst would be chosen for scheduling

• SJF can also be implemented as a preemptive algorithm.

• Jobs enter the ready queue at different times.• These may be new jobs that have just entered

the system or jobs that have finished waiting because the system has handled their I/O request

• Let a job enter the ready queue while another job is running.

• Let the newly ready job have a shorter predicted CPU burst time than the predicted remaining CPU burst time of the currently running job.

• Then the newly ready job preempts the current job.• Under the preemptive scenario a more descriptive

name for the algorithm would be “shortest remaining time first” scheduling.

• A non-preemptive example will be given first• Its performance characteristics will be

compared with FCFS scheduling• Then there will be a discussion of how to

predict burst times• This will be followed by a preemptive example

Non-preemptive Example

• Consider the following scenario:• Process burst length• P1 6 ms.• P2 8 ms.• P3 7 ms.• P4 3 ms.• Recall that for a miniature scenario like this, the

assumption is that all jobs (bursts) are available for scheduling at time t = 0.

SJF order: P4, P1, P3, P2average wait time = (0 + 3 + 9 + 16) / 4 =

• In general, the average wait time for SJF scheduling is lower than the average wait time for FCFS scheduling of the same processes

• This is illustrated by scheduling the jobs of this example in FCFS order and comparing the average wait time

• Although the assumption is that all bursts are available at time t = 0, for the comparison with FCFS, the arrival order in the ready queue makes a difference

• Let the arrival order be represented by the subscripts• The example using FCFS scheduling is given next

FCFS average wait time = (0 + 6 + 14 + 21) / 4 =10.25 ms.

• In theory, SJF is actually 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

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 = the actual observed length of the nth CPU burst

for a given process• Let Tn+1 = the predicted value of the next burst• Let α be given such that 0 <= α < 1• Then define Tn+1 as follows:• Tn+1 = α tn + (1 – α)Tn

Explanation

• Consider the coefficient α • It is a weighting factor. • It weights the most recent actual performance

vs. the performance before that• To get an idea of the purpose α serves,

consider the results of the three choices given below

• If α = 0, no weight is given to the most recent burst time in predicting the next

• If α = ½, one half weight is given to the most recent burst time and one half weight to all burst times before that in predicting the next

• If α = 1, the prediction of the next burst time is based completely on the most recent burst time

• tn appears in the formula.• This was the most recent actual burst time.• Tn also appears in the formula. • It is the previous prediction.• Although it is a prediction, it includes real past

performance information because• Tn = α tn-1 + (1 – α)Tn-1

• In other words, Tn includes tn-1

• Ultimately this formula expands to include all previous actual performance figures

• It regresses back to the initial predicted value, T0

• T0 has to be some sort of guess because it is not based on any immediately preceding value for that process

• Some arbitrary constant can be used, a system average can be used, perhaps a value from a previous run of the same process was kept, etc.

Expanding the formula

• Expanding the formula illustrates how come it is known as an exponential average

• The expansion gives a better feel for the role of the components in the formula

• Tn = α tn-1 + (1 – α)Tn-1

• Tn-1 = α tn-2 + (1 – α)Tn-2

• Tn-2 = α tn-3 + (1 – α)Tn-3

• …• T2 = α t1 + (1 – α)T1

• T1 = α t0 + (1 – α)T0

• T0 is an estimate

• The expansion looks like this:• Tn+1 = α tn + (1- α)(α tn-1 + (1- α)(… α t0 + (1- α)T0)…)

• = α tn + (1- α) α tn-1 + (1- α)2 α tn-2 + … + (1- α)n α t0 + (1- α)n+1T0

• The first term is:• α tn

• The general term is:• (1 – α)jαtn-j

• The last term is:• (1 – α)n+1T0

In words

• The most recent actual performance, tn, gets weight α

• All previous performances, ti, are multiplied by α and by a factor of (1 – α)j, where the value of j is determined by how far back in time t occurred

• Since (1 – α) < 1, as you go back in time, the weight of a given term on the current prediction is exponentially reduced

• Incidentally, if α = 1 – α = ½, in the formula you can just combine the coefficients for α and 1 – α, giving a single coefficient of α (or 1 – α with 1 added to the power on it.

• The following graph illustrates the results of applying the formula with T0 = 10 and α = 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, but the prediction lags reality

prediction in

dex 70

actual burst lengthpredicted burst

Preemptive SJF

• This was mentioned previously.• 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

• An example of preemptive SJF scheduling will now be given.

• For the purposes of comparison the performance of the same set of jobs will then be given with non-preemptive scheduling

• 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.

Preemptive SJF average wait time =(0 + 9 + 0 + 15 + 2) / 4 =

6.5 ms.

Walking through the example

• P1 arrives at t = 0 and starts• P2 arrives 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

• 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

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

• Total wait time = sum of waits for P1 through P4

• = 0 + 9 + 0 + 15 + 2 = 26• Average wait time = 26 / 4 = 6.5

The same processes under non-preemptive SJF

• Notice how it doesn’t really look like SJF until after P1 is finished

• At that point the other processes have arrived and are available to be scheduled

• Time t = 8 is like time t = 0 and from there it is clearly SJF• Of course, it would be more complicated if some jobs

arrived even later• The average wait time is longer than for the preemptive

example due to the behavior of P1 at the beginning

Average wait time for this non-preemptive SJF example

• 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

• Notice that the wait time without preemption is higher than the wait time with preemption

• This is not surprising• P1 burns up 8 time units at the beginning,

adding to the overall wait time of all of the remaining processes

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

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

Average wait time = (0 + 1 + 6 + 16 + 18) / 5 = 41 / 5 =8.2 ms.

How process priorities are set

• Internal priority setting• SJF is an example• Other criteria that have been used:– Time limits– Memory requirements– (I/O burst) / (CPU burst)

• External priority setting:– Importance of process– Type or amount of funding– Sponsoring department– politics

• 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

Round Robin (RR) 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 use a whole time slice• If it is unfinished when the timer goes off, it is

taken off the CPU and added to the “tail” of the circularly linked list

• A process may also block (I/O, e.g.) before the quantum is over

• If so, it goes into a waiting queue• When it is ready again it is added to the tail of

the circularly linked ready queue

• The so-called tail of the queue moves• It is the point behind the currently scheduled

process

• RR scheduling depends on a hardware timer• RR scheduling provides for fairness in dividing

up the CPU as a shared resource• It has the disadvantage of long average

waiting times for all processes contending for it

• If this is interactive time-sharing, the time a job spends waiting for human I/O (blocked) will far outweigh the time spent waiting for access to the CPU while in the circularly linked ready queue

• Also, human expectations for finishing a job will be relatively long compared to the sum of all waiting times for the job.

• However, human expectations of a short response time may be demanding.

• Like essentially everything in CS, RR scheduling involves a trade-off, in particular, fairness vs. waiting time

• Everything has a price• The question of whether the trade-off in performance

is worth it in this case depends in large part on human factors

• Empirically, it’s been shown that the advantages of time-slicing in this way outweigh the disadvantages

• Essentially every modern operating system does something that is an elaboration on the RR idea

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.

Average wait time = (0 + 6 + 4 + 7) / 3 = 17 / 3 = 5 2/3

• 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

• 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

• 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 context switch time is dependent on

hardware speed and O/S code efficiency (speed)

• It is interesting to consider the parameters involved and their effects on what is possible

• Historically, there was a time when time-sharing was simply not possible

• Device speeds were so slow that to make the ratio acceptable, the time slice had to be impractically large.

• What is the point of time-sharing if the time slice is one second, for example?

• Moore’s Law solved this problem

• In modern micro-devices, like handhelds, multi-tasking may still be impractical

• The devices are much faster than older devices so the cost may be more evident when considering the amount of code run for various purposes rather than time

• Consider this ratio: (number of lines of machine code run for context switch) / (number of lines of code run for some lightweight application)

• The context switch is still pure overhead which eats into the number of cycles available for useful work, eats into battery life directly with computational costs, eats into battery life if the display remains on while O/S code runs, etc.

• In any case, for larger-scale systems, multi-tasking is supportable, but the question then becomes one of performance tuning

• System speed ultimately determines the answers to these questions:

• How small can a time slice be, and still consist of a useful number of cycles?

• How small can a time slice be, and still consist of a useful number of cycles compared to the overhead cost of switching?

• How many slices of a given size should be given per unit time to a single process in order to satisfy its needs?

• The total number of slices per unit time divided by the number needed per process gives n, the total number of users or processes that can be supported at any given time

• Once all of the parameters are settled, you can then determine what percent of your processing cost is pure overhead in order to support that number of users

• In general, system administrators have some control over the multi-tasking level

• An administrator may have the ability to limit the number of jobs or change the time slice size in order to improve performance for those jobs that enter or in order to reduce cycles wasted on context switching

• Round robin scheduling illustrates other performance issues besides just average waiting time and overall efficiency of CPU resource utilization

• As pointed out earlier, if time slices are long, scheduling can degenerate into FCFS

• FCFS doesn’t fairly allocate the CPU in a time sharing environment

• On the other hand, if the system speed supports very small time slices, another factor can come into play

• Consider overall average process turnaround time as a function of time slice size

• It’s not just a question of how many time slices should be allocated among jobs at the macro level.

• It’s what the relationship should be between time slices and the CPU bursts of jobs at the micro level

• Delays will increase if individual process bursts have to wait for multiple slices

• The typical size of process bursts is work-load dependent and is something that an operating system can monitor and report to the administrator.

• 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

• Digression on the 80-20 rule…

• Just as there are trade-offs in CS, lots of things can be viewed on a spectrum.

• The whole topic of time slices nicely illustrates a spectrum.

• At one extreme, in a very old system, overhead costs might be so high that time slices would be so long that you were just accomplishing multi-programming, not multi-tasking

• In other words, whole jobs completed within their time slice, more or less.

• At the next step down, slices might still be so large that under multi-tasking users were not satisfied with response times

• From the point of view of dividing up the processor, you might simply be getting FCFS of CPU bursts among various jobs.

• The sweet spot is at the 80-20 mark• There is an approximate match between time

slice size and burst length for 80% of the CPU bursts for all processes

• 20% of CPU bursts are exceptionally large and it’s a performance benefit to break them into separate slices, allowing other process bursts to be scheduled in between

• At the other end of the spectrum, if you make the time slice too small, you break too many bursts, and again the overhead cost of switching can begin to eat away at the benefit of sharing

• Next, the effect of time slice size variation is examined with an example

• The example calculates both turnaround time and average wait time

RR time slice size variations

• Consider the following scenario with a time slice size of 4:

• Process burst length• P1 6 ms.• P2 3 ms.• P3 1 ms.• P4 7 ms.

Average turnaround time = (14 + 7 + 8 + 17) / 4 = 46 / 4 = 11 ½

Average wait time = ((0 + 8) + 4 + 7 + (8 + 2)) / 4 = 29 / 4 = 7 ¼

• 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

• In the following chart, the middle two bars represent the case just presented, of a time slice of 4

• The surrounding bars show what happens with smaller and larger time slice sizes

• Some are higher and some are lower

time slice size 1

time slice size 2

time slice size 3

time slice size 4

time slice size 5

time slice size 6

time slice size 7

average turnaround timeaverage waiting time

• 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

Multi-level Queue Scheduling

• You could argue whether this statement is literally correct, but it may have some explanatory value:

• Round robin scheduling is the classic way of dividing processor time among processes

• In a sense, queue based scheduling systems are just complex elaborations of round robin scheduling

• They are ways of dividing processor time with various bells and whistles to achieve certain desirable effects

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

• 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%

• Consider the following set of scheduling design decisions, for example:

• Different classes of jobs are permanently assigned to different queues

• The queues have priorities relative to each other• Each queue implements its own scheduling algorithm

for the processes in it, which are of equal priority• Consider the set of queues shown on the next

overhead in a system with such a design:

An Example

• 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

• It would also be possible to time slice between the queues, giving a certain percent of CPU time to each one

Multi-level Feedback Queue Scheduling

• The next elaboration is to allow processes to change priority—and change between queues accordingly

• The change may be based on characteristics such as CPU or I/O usage or time spent in the 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

• Such a system 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

• A diagram of a simple system is given on the next overhead.

• This is followed by explanations

An Example

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.

• 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.

Defining Characteristics of a General Multi-Level 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).

• 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.

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

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 self-scheduling.

• Under SMP 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.

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.

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.

• 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.

• 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.

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.

• 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.

(Hyperthreading)

• Sometimes the question arises, what does or did the term hyperthreading mean?

• In previous editions of the book, this was explained in some detail.

• It has been replaced by a discussion of scheduling in multicore processors

• For the sake of completeness, a little bit of information on hyperthreading is given before taking up multicore processors

• 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.

Multicore Processors

• Multicore processors basically consist of >1 CPU paired with a register set on a single chip

• Each core appears as a separate CPU to the operating system

• One of the key things to note about this architecture is the separate cores still share access to a single main memory

• In classical systems (one CPU, many processes), the need to access shared secondary storage (do I/O) is a source of significant delay

• This affects scheduling decisions/algorithms• In multicore systems, it turns out that at the

micro-level, the need to access shared memory is a significant source of delay

• This delay is known as memory stall, and it affects multicore scheduling decisions/algorithms

• Memory stall at the core/thread level is analogous to I/O wait at the process level

• One solution to the delay is implement multi-threaded multicore systems

• The idea is that 2 (or more) hardware threads are assigned to each core

• If the application process running on one of the hardware threads assigned to the core stalls, then that thread is switched out and the other one takes the core

• The thing to note about the term “hardware thread” is that to the O/S, such a thread appears as a separate processor

• In other words, hardware threads truly are a hardware construct, which is implemented below the O/S level

• The engineers who design the chip accomplish switching between hardware threads using microcode and silicon solutions

• From the software person’s point of view, it’s necessary to write a multi-processing O/S, but it doesn’t matter whether the multiple physical processors are virtual or real

• All that matters is the multi-processor interface presented by the hardware to the O/S

• In summary, consider a system with dual cores and dual threads

• From the operating system point of view, such a system has four processors

• The book points out that the switching of hardware threads on an off of a core may be coarse-grained or fine-grained

• This describes, in essence, how large or small the time slices are for each hardware thread

• Each approach has its advantages and disadvantages, but the details are not important

• These are engineering questions, not O/S questions

• The book also points out that scheduling is now happening both in the O/S and on the chip

• The general scheduling ideas discussed earlier still apply to the implementation of scheduling in the O/S

• The came concepts also apply at the hardware level

• Engineers may choose to alternate hardware threads using round robin scheduling, for example, some other recognizable technique

Virtualization and Scheduling

• The main point of this section is that if you add a layer to system software, there will be effects on or interactions with other layers

• In a virtual machine environment, each virtual machine runs an O/S with a scheduling algorithm implemented in it

• Each virtual machine appears to be running on a separate processor, when in fact, it is effectively just getting time slices of an underlying processor

• The actual performance of processes scheduled by the user O/S on the virtual machine is at the mercy of how many real machine cycles the virtual machine is actually given

• The effort put into optimizing scheduling performance at the user level may be undone by the way the virtual machine layer works

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.

Solaris Scheduling

• Solaris scheduling is based on four priority classes:– Real time– System– Time sharing– Interactive

• Practical points of Solaris scheduling:– High numbers = high priority, range of values: 0-59– 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.

– It is apparent that with 60 priorities and 3 queues, more than one priority value applies to each queue

• 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.

Solaris Scheduling Queue—Notice that Jobs Don’t Move Between Queues

Solaris Dispatch Table for Interactive and Time-sharing Threads

Starting Priority Allocated Time Quantum New Priority after Quantum Expiration

New Priority after Return from Sleep

0 200 0 50

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

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 scheduler uses the global priorities to schedule

• Among threads of equal priority, the scheduler does RR scheduling

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

• 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

• 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 1-15

• Within each class there are 7 additional subdivisions:– Time critical– Highest– Above normal– Below normal– Lowest– idle

• 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

Columns = Priority Classes, Rows = Relative Priorities within ClassesThe ‘Normal’ row contains the base priorities for the classes.Real-time High Above

normalNormal Below

normalIdle priority

Time-critical

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

• 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

• 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.

Linux Scheduling

• Skip this• Two concrete examples are enough

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

• If a JVM implementation doesn’t have time-slicing 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

• 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

• 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

Scheduling Algorithm Evaluation

• In general, algorithm selection is based on multiple criteria.

• For example:• Maximize 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

• Various approaches can be used to try and predict performance for a given scheduling algorithm:

• Deterministic models• Queuing models• Simulation modeling• Implementation• Brief remarks will be made on each of these

approaches

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

Queuing Models

• These are basically statistical models where the statistical assumptions are based on past observation of real systems

• The first distribution of interest is the arrival of jobs into the system

• This is typically a Poisson distribution

• All of the rest of the system distributions tend to be exponential (or hyper-exponential—remember the graph given earlier of burst lengths)– CPU burst occurrence distribution– CPU burst length distribution– I/O burst occurrence distribution– I/O wait length distribution

• Given these distributions it is possible to calculate:

• Throughput• CPU utilization• Waiting time• A simple example of calculating a statistic

follows

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 or decrease in the queue length occurs when the system is not in steady state (when the arrival and departure processes are not equal)

• Queuing models are not perfect• They are limited by the match or mismatch

between the chosen 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

Simulation Modeling

• Basic elements of a simulation:– A clock (discrete event simulation)– Data structures modeling state– Modules which model activity which changes state

• Simulation input:• Option 1: Random number generation based on

statistical distributions for processes (again, mathematical simplification)

• Option 2: Trace tapes. – These are records of events in actual runs. – They provide an excellent basis for comparing two algorithms– Industrial strength operating system have hooks which allow

the collection of performance information down to the level of individual processes and bursts

• 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 an 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

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

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 change 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

• 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

• 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.• This goes back to the idea of the separation of

mechanism and policy • The system administrator will be able to set

scheduling algorithms and their parameters to meet the job mix at any given time

• It will not be necessary to re-code the operating system except in cases of major version changes

The End

Chapter 5, CPU Scheduling

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