dataprocess using different technology.ppt

COMPARATIVE STUDY OF CPU
SCHEDULING ALGORITHMS
PRESENTED BY
PRADEEP KUMAR RATH
ASST. PROFESSOR,MCA
1

CPU Scheduling
• How is the OS to decide which of several tasks to take off a
queue?
• Scheduling: deciding which threads are given access to
resources from moment to moment.
. Operating Systems 2

Assumptions about Scheduling
• CPU scheduling big area of research in early ‘70s
• Many implicit assumptions for CPU scheduling:
– One program per user
– One thread per program
– Programs are independent
• These are unrealistic but simplify the problem
• Does “fair” mean fairness among users or programs?
– If I run one compilation job and you run five, do you get five times as
much CPU?
• Often times, yes!
• Goal: dole out CPU time to optimize some desired
parameters of the system.
– What parameters?

Assumption: CPU Bursts

Assumption: CPU Bursts
• Execution model: programs alternate between bursts of CPU
and I/O
– Program typically uses the CPU for some period of time, then does
I/O, then uses CPU again
– Each scheduling decision is about which job to give to the CPU for
use by its next CPU burst
– With timeslicing, thread may be forced to give up CPU before
finishing current CPU burst.

What is Important in a Scheduling
Algorithm?

What is Important in a Scheduling
Algorithm?
• Minimize Response Time
– Elapsed time to do an operation (job)
– Response time is what the user sees
• Time to echo keystroke in editor
• Time to compile a program
• Real-time Tasks: Must meet deadlines imposed by World
• Maximize Throughput
– Jobs per second
– Throughput related to response time, but not identical
• Minimizing response time will lead to more context switching than if you
maximized only throughput
– Minimize overhead (context switch time) as well as efficient use of
resources (CPU, disk, memory, etc.)
• Fairness
– Share CPU among users in some equitable way
– Not just minimizing average response time

Scheduling Algorithms: First-Come,
First-Served (FCFS)
• “Run until Done:” FIFO algorithm
• In the beginning, this meant one program runs non-
preemtively until it is finished (including any blocking for I/O
operations)
• Now, FCFS means that a process keeps the CPU until one or
more threads block
• Example: Three processes arrive in order P1, P2, P3.
– P1 burst time: 24
• Draw the Gantt Chart and compute Average Waiting Time
and Average Completion Time.

First-Served (FCFS)
• Example: Three processes arrive in order P1, P2, P3.
• Waiting Time
– P1: 0
– P2: 24
– P3: 27
• Completion Time:
– P1: 24
– P2: 27
– P3: 30
• Average Waiting Time: (0+24+27)/3 = 17
• Average Completion Time: (24+27+30)/3 = 27
P1 P2 P3
0 24 27 30

First-Served (FCFS)
• What if their order had been P2, P3, P1?

First-Served (FCFS)
• What if their order had been P2, P3, P1?
• Waiting Time
– P1: 0
– P2: 3
– P3: 6
– P1: 3
– P2: 6
– P3: 30
• Average Waiting Time: (0+3+6)/3 = 3 (compared to 17)
• Average Completion Time: (3+6+30)/3 = 13 (compared to 27)
P1
P2 P3
0 3 6 30

First-Served (FCFS)
• Average Waiting Time: (0+3+6)/3 = 3 (compared to 17)
• Average Completion Time: (3+6+30)/3 = 13 (compared to 27)
• FIFO Pros and Cons:
– Simple (+)
– Short jobs get stuck behind long ones (-)
• If all you’re buying is milk, doesn’t it always seem like you are stuck behind
a cart full of many items
– Performance is highly dependent on the order in which jobs arrive (-)

How Can We Improve on This?

Round Robin (RR) Scheduling
• FCFS Scheme: Potentially bad for short jobs!
– Depends on submit order
– If you are first in line at the supermarket with milk, you don’t care who
is behind you; on the other hand…
• Round Robin Scheme
– Each process gets a small unit of CPU time (time quantum)
• Usually 10-100 ms
– After quantum expires, the process is preempted and added to the
end of the ready queue
– Suppose N processes in ready queue and time quantum is Q ms:
• Each process gets 1/N of the CPU time
• In chunks of at most Q ms
• What is the maximum wait time for each process?

• FCFS Scheme: Potentially bad for short jobs!
– Depends on submit order
– If you are first in line at the supermarket with milk, you don’t care who
is behind you; on the other hand…
– No process waits more than (n-1)q time units

• Performance Depends on Size of Q
– Small Q => interleaved
– Large Q is like…
– Q must be large with respect to context switch time, otherwise
overhead is too high (spending most of your time context switching!)

• Performance Depends on Size of Q
– Small Q => interleaved
– Large Q is like FCFS
– Q must be large with respect to context switch time, otherwise
overhead is too high (spending most of your time context switching!)

Example of RR with Time Quantum = 4
Process Burst Time
P1 24
P2 3
P3 3
• The Gantt chart is:
P1 P2 P3 P1 P1 P1 P1 P1
0 4 7 10 14 1822 26 30

Process Burst Time
P1 24
P2 3
P3 3
• Waiting Time:
– P1: (10-4) = 6
– P2: (4-0) = 4
– P3: (7-0) = 7
– P1: 30
– P2: 7
– P3: 10
• Average Waiting Time: (6 + 4 + 7)/3= 5.67
• Average Completion Time: (30+7+10)/3=15.67
P1 P2 P3 P1 P1 P1 P1 P1
0 4 7 10 14 1822 26 30

Turnaround Time Varies With The Time
Quantum

• Waiting Time:
– P1: (68-20)+(112-88) = 72
– P2: (20-0) = 20
– P3: (28-0)+(88-48)+(125-108) = 85
– P4: (48-0)+(108-68) = 88
– P1: 125
– P2: 28
– P3: 153
– P4: 112
• Average Waiting Time: (72+20+85+88)/4 = 66.25
• Average Completion Time: (125+28+153+112)/4 = 104.5
A process can finish before the time quantum expires, and release the CPU.

RR Summary
• Pros and Cons:
– Better for short jobs (+)
– Fair (+)
– Context-switching time adds up for long jobs (-)
• The previous examples assumed no additional time was needed for context
switching – in reality, this would add to wait and completion time without
actually progressing a process towards completion.
• Remember: the OS consumes resources, too!
• If the chosen quantum is
– too large, response time suffers
– infinite, performance is the same as FIFO
– too small, throughput suffers and percentage overhead grows
• Actual choices of timeslice:
– UNIX: initially 1 second:
• Worked when only 1-2 users
• If there were 3 compilations going on, it took 3
seconds to echo each keystroke!
– In practice, need to balance short-job
performance and long-job throughput:
• Typical timeslice 10ms-100ms
• Typical context-switch overhead 0.1ms – 1ms (about 1%)

Comparing FCFS and RR
• Assuming zero-cost context
switching time, is RR always
better than FCFS?
• Assume 10 jobs, all start at the
same time, and each require
100 seconds of CPU time
• RR scheduler quantum of 1
second
• Completion Times (CT)
– Both FCFS and RR finish at the same time
– But average response time is much worse under RR!
• Bad when all jobs are same length
• Also: cache state must be shared between all jobs with RR
but can be devoted to each job with FIFO
– Total time for RR longer even for zero-cost context switch!
Job # FCFS CT RR CT
1 100 991
2 200 992
… … …
9 900 999
10 1000 1000

Comparing FCFS and RR

Scheduling
• The performance we get is somewhat dependent on what
“kind” of jobs we are running (short jobs, long jobs, etc.)
• If we could “see the future,” we could mirror best FCFS
• Shortest Job First (SJF) a.k.a. Shortest Time to Completion
First (STCF):
– Run whatever job has the least amount of computation to do
• Shortest Remaining Time First (SRTF) a.k.a. Shortest
Remaining Time to Completion First (SRTCF):
– Preemptive version of SJF: if a job arrives and has a shorter time to
completion than the remaining time on the current job, immediately
preempt CPU
• These can be applied either to a whole program or the
current CPU burst of each program
– Idea: get short jobs out of the system
– Big effect on short jobs, only small effect on long ones
– Result: better average response time

Scheduling
• But, this is hard to estimate
• We could get feedback from the program or the user, but
they have incentive to lie!
• SJF/SRTF are the best you can do at minimizing average
response time
– Provably optimal (SJF among non-preemptive, SRTF among
preemptive)
– Since SRTF is always at least as good as SJF, focus on SRTF
• Comparison of SRTF with FCFS and RR
– What if all jobs are the same length?
– What if all jobs have varying length?

Scheduling
• But, this is hard to estimate
• We could get feedback from the program or the user, but
they have incentive to lie!
• SJF/SRTF are the best you can do at minimizing average
response time
– Provably optimal (SJF among non-preemptive, SRTF among
preemptive)
– Since SRTF is always at least as good as SJF, focus on SRTF
• Comparison of SRTF with FCFS and RR
– What if all jobs are the same length?
• SRTF becomes the same as FCFS (i.e. FCFS is the best we can do)
– What if all jobs have varying length?
• SRTF (and RR): short jobs are not stuck behind long ones

Example: SRTF
• A,B: both CPU bound, run for a week
• C: I/O bound, loop 1ms CPU, 9ms disk I/O
• If only one at a time, C uses 90% of the disk, A or B could
use 100% of the CPU
• With FIFO: Once A and B get in, the CPU is held for two
weeks
• What about RR or SRTF?
– Easier to see with a timeline
A or B C C I/O

Example: SRTF
• A,B: both CPU bound, run for a week
• C: I/O bound, loop 1ms CPU, 9ms disk I/O
A or B C C I/O

Last Word on SRTF
• Starvation
– SRTF can lead to starvation if many small jobs!
– Large jobs never get to run
• Somehow need to predict future
– How can we do this?
– Some systems ask the user
• When you submit a job, you have to say how long it will take
• To stop cheating, system kills job if it takes too long
– But even non-malicious users have trouble predicting runtime of their
jobs
• Bottom line, can’t really tell how long job will take
– However, can use SRTF as a yardstick for measuring other policies,
since it is optimal
• SRTF Pros and Cons
– Optimal (average response time) (+)
– Hard to predict future (-)
– Unfair, even though we minimized average response time! (-)

Predicting the Future
• Back to predicting the future… perhaps we can predict the
next CPU burst length?
• Iff programs are generally repetitive, then they may be
predictable
• Create an adaptive policy that changes based on past
behavior
– CPU scheduling, virtual memory, file systems, etc.
– If program was I/O bound in the past, likely in the future
• Example: SRTF with estimated burst length
– Use an estimator function on previous bursts
– Let T(n-1), T(n-2), T(n-3), …, be previous burst lengths. Estimate next
burst T(n) = f(T(n-1), T(n-2), T(n-3),…)
– Function f can be one of many different time series estimation
schemes (Kalman filters, etc.)

Determining Length of Next CPU Burst
• Can only estimate the length
• Can be done by using the length of previous CPU bursts,
using exponential averaging
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32

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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

Priority Scheduling
• A priority number (integer) is associated with each process
• The CPU is allocated to the process with the highest priority
(smallest integer  highest priority)
– Preemptive (if a higher priority process enters, it receives the CPU
immediately)
– Nonpreemptive (higher priority processes must wait until the current
process finishes; then, the highest priority ready process is selected)
• SJF is a priority scheduling where priority is the predicted
next CPU burst time
• Problem  Starvation – low priority processes may never
execute
• Solution  Aging – as time progresses increase the priority
of the process

Priority Inversion
• Consider a scenario in which there are three processes, one
with high priority (H), one with medium priority (M), and one
with low priority (L).
• Process L is running and successfully acquires a resource,
such as a lock or semaphore.
• Process H begins; since we are using a preemptive priority
scheduler, process L is preempted for process H.
• Process H tries to acquire L’s resource, and blocks
(because it is held by L).
• Process M begins running, and, since it has a higher priority
than L, it is the highest priority ready process. It preempts L
and runs, thus starving high priority process H.
• This is known as priority inversion.
• What can we do?

Priority Inversion
• Process L should, in fact, be temporarily of “higher priority”
than process M, on behalf of process H.
• Process H can donate its priority to process L, which, in this
case, would make it higher priority than process M.
• This enables process L to preempt process M and run.
• When process L is finished, process H becomes unblocked.
• Process H, now being the highest priority ready process,
runs, and process M must wait until it is finished.
• Note that if process M’s priority is actually higher than
process H, priority donation won’t be sufficient to increase
process L’s priority above process M. This is expected
behavior (after all, process M would be “more important” in
this case than process H).

Multi-level Feedback Scheduling
• Another method for exploiting past behavior
– Multiple queues, each with different priority
• Higher priority queues often considered “foreground” tasks
– Each queue has its own scheduling algorithm
• E.g. foreground  RR, background  FCFS
• Sometimes multiple RR priorities with quantum increasing exponentially
(highest queue: 1ms, next: 2ms, next: 4ms, etc.)
– Adjust each job’s priority as follows (details vary)
• Job starts in highest priority queue
• If entire CPU time quantum expires, drop one level
• If CPU is yielded during the quantum, push up one level (or to top)

Scheduling Details
• Result approximates SRTF
– CPU bound jobs drop rapidly to lower queues
– Short-running I/O bound jobs stay near the top
• Scheduling must be done between the queues
– Fixed priority scheduling: serve all from the highest priority, then the
next priority, etc.
– Time slice: each queue gets a certain amount of CPU time (e.g., 70%
to the highest, 20% next, 10% lowest)
• Countermeasure: user action that can foil intent of the OS
designer
– For multilevel feedback, put in a bunch of meaningless I/O to keep
job’s priority high
– But if everyone does this, it won’t work!
– Consider an Othello program, playing against a competitor. Key was
to compute at a higher priority than the competitors.
• Put in printf’s, run much faster!

Scheduling Details
• It is apparent that scheduling is facilitated by having a
“good mix” of I/O bound and CPU bound programs, so that
there are long and short CPU bursts to prioritize around.
• There is typically a long-term and a short-term scheduler in
the OS.
• We have been discussing the design of the short-term
scheduler.
• The long-term scheduler decides what processes should be
put into the ready queue in the first place for the short-term
scheduler, so that the short-term scheduler can make fast
decisions on a good mix of a subset of ready processes.
• The rest are held in memory or disk
– Why else is this helpful?

Scheduling Details
• It is apparent that scheduling is facilitated by having a
“good mix” of I/O bound and CPU bound programs, so that
there are long and short CPU bursts to prioritize around.
• There is typically a long-term and a short-term scheduler in
the OS.
• We have been discussing the design of the short-term
scheduler.
• The long-term scheduler decides what processes should be
put into the ready queue in the first place for the short-term
scheduler, so that the short-term scheduler can make fast
decisions on a good mix of a subset of ready processes.
• The rest are held in memory or disk
– This also provides more free memory for the subset of ready
processes given to the short-term scheduler.

Fairness
• What about fairness?
– Strict fixed-policy scheduling between queues is unfair (run highest,
then next, etc.)
• Long running jobs may never get the CPU
• In Multics, admins shut down the machine and found a 10-year-old job
– Must give long-running jobs a fraction of the CPU even when there
are shorter jobs to run
• Tradeoff: fairness gained by hurting average response time!
• How to implement fairness?
– Could give each queue some fraction of the CPU
• i.e., for one long-running job and 100 short-running ones?
• Like express lanes in a supermarket – sometimes express lanes get so
long, one gets better service by going into one of the regular lines
– Could increase priority of jobs that don’t get service (as seen in the
multilevel feedback example)
• This was done in UNIX
• Ad hoc – with what rate should priorities be increased?
• As system gets overloaded, no job gets CPU time, so everyone increases in
priority
– Interactive processes suffer

Lottery Scheduling
• Yet another alternative: Lottery Scheduling
– Give each job some number of lottery tickets
– On each time slice, randomly pick a winning ticket
– On average, CPU time is proportional to number of tickets given to
each job over time
• How to assign tickets?
– To approximate SRTF, short-running jobs get more, long running jobs
get fewer
– To avoid starvation, every job gets at least one ticket (everyone
makes progress)
• Advantage over strict priority scheduling: behaves
gracefully as load changes
– Adding or deleting a job affects all jobs proportionally, independent of
how many tickets each job possesses

Example: Lottery Scheduling
• Assume short jobs get 10 tickets, long jobs get 1 ticket
• What percentage of time does each long job get? Each
short job?
• What if there are too many short jobs to give reasonable
response time
– In UNIX, if load average is 100%, it’s hard to make progress
– Log a user out or swap a process out of the ready queue (long term
scheduler)

Example: Lottery Scheduling
• Assume short jobs get 10 tickets, long jobs get 1 ticket
• What if there are too many short jobs to give reasonable
response time
– In UNIX, if load average is 100%, it’s hard to make progress
– Log a user out or swap a process out of the ready queue (long term
scheduler)
# short jobs /
# long jobs
% of CPU each
short job gets
% of CPU each
long job gets
1/1 91% 9%
0/2 N/A 50%
2/0 50% N/A
10/1 9.9% 0.99%
1/10 50% 5%

Scheduling Algorithm Evaluation
• Deterministic Modeling
– Takes a predetermined workload and compute the performance of
each algorithm for that workload
• Queuing Models
– Mathematical Approach for handling stochastic workloads
• Implementation / Simulation
– Build system which allows actual algorithms to be run against actual
data. Most flexible / general.

Conclusion
• Scheduling: selecting a waiting process
from the ready queue and allocating the
CPU to it
• When do the details of the scheduling
policy and fairness really matter?
– When there aren’t enough resources to go around
• When should you simply buy a faster computer?
– Or network link, expanded highway, etc.
– One approach: buy it when it will pay for itself in improved response
time
• Assuming you’re paying for worse response in reduced productivity,
customer angst, etc.
• Might think that you should buy a faster X when X is utilized 100%, but
usually, response time goes to infinite as utilization goes to 100%
– Most scheduling algorithms work fine in the “linear” portion of the
load curve, and fail otherwise
– Argues for buying a faster X when utilization is at the “knee” of the
curve

REFERENCES
• 1] H. Heidari dan A. Chalechale, “Scheduling in
Multiprocessor System Using Genetic Algorithm,”
International Journal ofAdvanced Science and Technology,
vol. 43, pp. 81-94, 2012.
• [2] C. Bajaj, A. Dogra dan G. Singh, “Review And Analysis Of
Task Scheduling Algorithms,” International Research
Journal of Engineering and Technology, vol. 2, no. 3, pp.
1449-1452, 2015.
• [3] N. Hamid, “Scheduling Policies,” [Online]. Available:
http://homepages.uel.ac.uk/u8902383/scheduling_policies.ht
m.
• A. Dusseau, R. H. dan A. C., Operating Systems: Three Easy
Pieces, Arpaci-Dusseau Books, 2014.

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