Uniprocessor Scheduling Basic Concepts Scheduling Criteria - - PowerPoint PPT Presentation

Uniprocessor Scheduling

• Basic Concepts
• Scheduling Criteria
• Scheduling Algorithms

Three level scheduling

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Types of Scheduling yp g

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Long- and Medium-Term Schedulers g

Long-term scheduler

• Determines which programs are admitted to the system

(ie to become processes) b d d f h h l d

• requests can be denied if e.g. thrashing or overload

Medium-term scheduler

• decides when/which processes to suspend/resume
• Both control the degree of multiprogramming

– More processes, smaller percentage of time each process is executed executed

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Short-Term Scheduler

• Decides which process will be dispatched; invoked

upon

– Interrupts, Operating system calls, Signals, ..

• Dispatch latency – time it takes for the dispatcher

to stop one process and start another running; the to stop one process and start another running; the dominating factors involve:

– switching context g – selecting the new process to dispatch

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CPU–I/O Burst Cycle y

• Process execution consists of a

cycle of cycle of

–

CPU execution and – I/O wait.

• A process may be

– CPU-bound – IO-bound

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Scheduling Criteria- Optimization goals g p g

CPU utilization – keep CPU as busy as possible Throughput – # of processes that complete their execution per time unit Response time – amount of time it takes from when a request was Response time amount of time it takes from when a request was submitted until the first response is produced (execution + waiting time in ready queue)

T d ti t f ti t t ti l – Turnaround time – amount of time to execute a particular process (execution + all the waiting); involves IO schedulers also

Fairness - watch priorities, avoid starvation, ... Scheduler Efficiency - overhead (e.g. context switching, computing priorities, …)

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Decision Mode

Nonpreemptive O i i th i t t it ill ti til it

• Once a process is in the running state, it will continue until it

terminates or blocks itself for I/O Preemptive

• Currently running process may be interrupted and moved to the

R d t t b th ti t Ready state by the operating system

• Allows for better service since any one process cannot monopolize

the processor for very long p y g

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First-Come-First-Served (FCFS) (FCFS)

5 10 15 20 5 10 15 20 A B C E D

• non-preemptive
• Favors CPU bound processes
• Favors CPU-bound processes
• A short process may have to

wait very long before it can

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wait very long before it can execute (convoy effect)

Round-Robin

5 5 10 15 20 A B C D E

ti b d l k (i t t ti

• preemption based on clock (interrupts on time

slice or quantum -q- usually 10-100 msec)

• fairness: for n processes, each gets 1/n of the

CPU time in chunks of at most q time units CPU time in chunks of at most q time units

• Performance

– q large ⇒ FIFO ll h d b hi h d t

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– q small ⇒ overhead can be high due to context switches

Shortest Process First

5 10 15 20 10 15 20 A B C D E

• Non-preemptive
• Short process jumps ahead of

l longer processes

• Avoid convoy effect

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Shortest Remaining Time First g

5 10 15 20 5 10 15 20 A B C D E

• Preemptive (at arrival)

version of shortest process next next

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On SPF Scheduling

• gives high throughput

g s h gh throughput

• gives minimum (optimal) average response (waiting) time

for a given set of processes g p

– Proof (non-preemptive): analyze the summation giving the waiting time

• must estimate processing time (next cpu burst)

– Can be done automatically (exponential averaging) – If estimated time for process (given by the user in a batch system) not correct the operating system may abort it system) not correct, the operating system may abort it

• possibility of starvation for longer processes

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Determining Length of Next CPU Burst g g

• Can be done by using the length of previous CPU bursts, using

exponential averaging exponential averaging.

burst CPU next the for value predicted 2 burst CPU

• f

lenght actual 1. = = τ

1 th n

n t : Define 4. 1 , 3. burst CPU next the for value predicted 2. ≤ ≤

+

α α τ

1 n

( )

. t

n n n

τ α α τ − + =

=

1

1

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On Exponential Averaging p g g

• α =0

– τn+1 = τn – history does not count, only initial estimation counts

• α =1

α 1 – τn+1 = tn – Only the actual last CPU burst counts.

• If we expand the formula, we get:

τn+1 = α tn+(1 - α) α tn -1 + … +(1 α )j α t + +(1 - α )j α tn -i + … +(1 - α )n τ0

• Since both α and (1 - α) are less than or equal to 1, each successive

term has less weight than its predecessor.

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Prediction of the Length of the Next CPU Burst

Priority Scheduling: General Rules y g

• Scheduler can choose a process of higher priority

f l i it

• ver one of lower priority

– can be preemptive or non-preemptive – can have multiple ready queues to represent multiple level of – can have multiple ready queues to represent multiple level of priority

• Example Priority Scheduling: SPF, where priority is

p y g , p y the predicted next CPU burst time.

• Problem ≡ Starvation – low priority processes may

p y p y never execute.

• A solution ≡ Aging – as time progresses increase the

f h priority of the process.

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Priority Scheduling Cont. : Multilevel Queue y g Q

• Ready queue is partitioned into separate

queues eg queues, eg

foreground (interactive) background (batch)

• Each queue has its own scheduling
• Each queue has its own scheduling

algorithm, eg

foreground – RR b k d FCFS background – FCFS

• Scheduling must be done between the

queues.

– Fixed eg., serve all from foreground then from background. Possible starvation. – Another solution: Time slice – each queue t f ti f CPU ti t di id gets a fraction of CPU time to divide amongst its processes, eg. 80% to foreground in RR 20% t b k d i FCFS

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20% to background in FCFS

Multilevel Queue Scheduling Q g

Multilevel Feedback Queue Q

• A process can move

b t th i between the various queues; aging can be implemented this way implemented this way.

• scheduler parameters:
• scheduler parameters:

– number of queues – scheduling algorithm for each g g m f queue – method to upgrade a process th d t d t – method to demote a process – method to determine which queue a process will enter

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q p first

Multilevel Feedback Queues Q

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Thread Scheduling

M t d t d l th d

• Many-to-one and many-to-many models, thread

library schedules user-level threads to run on LWP LWP

– Known as process-contention scope (PCS) since scheduling competition is within the process

• Kernel thread scheduled onto available CPU is

system-contention scope (SCS) – competition among all threads in system

• E.g. Pthreads scheduling API allows specifying

ith PCS SCS d i th d ti either PCS or SCS during thread creation

Fair-Share Scheduling

• extention of multi-level queues with feedback +

t nt on of mu t qu u s w th f ac priority recomputation

– application runs as a collection of processes (threads) – concern: the performance of the application, user-groups, … (ie. group of processes/threads) – scheduling decisions based on process sets rather than scheduling decisions based on process sets rather than individual processes

• eg. “traditional” unix sheduling

g g

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Real-Time Scheduling

Real-Time Systems y

• Tasks or processes attempt to interact with outside-world events ,

which occur in “real time”; process must be able to keep up e g which occur in real time ; process must be able to keep up, e.g.

– Control of laboratory experiments, Robotics, Air traffic control, Drive-by- wire systems, Tele/Data-communications, Military command and control systems y

• Correctness of the RT system depends not only on the logical result of

the computation but also on the time at which the results are produced i.e. Tasks or processes come with a deadline (for starting or completion) Requirements may be hard or soft

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Periodic Real-TimeTasks: Timing Diagram Timing Diagram

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E.g. Multimedia Process Scheduling

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A movie may consist of several files

E.g. Multimedia Process Scheduling (cont) g g ( )

• Periodic processes displaying a movie
• Frame rates and processing requirements may be

different for each movie (or other process that i i )

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requires time guarantees)

Scheduling in Real-Time Systems Schedulable real-time system

• Given

– m periodic events m per od c events – event i occurs within period Pi and requires Ci seconds

• Then the load can only be handled if

Then the load can only be handled if

1

m i

C P ≤

∑

Utilization =

1 i i

P

=

∑

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Scheduling with deadlines: Earliest Deadline First Earliest Deadline First

Set of tasks with deadlines is schedulable (i.e can be executed in a way that no process misses its deadline) iff EDF is a schedulable (aka feasible) no process misses its deadline) iff EDF is a schedulable (aka feasible)

• sequence. Example sequences:

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Rate Monotonic Scheduling

• Assigns priorities to tasks on the basis of their periods

Assigns priorities to tasks on the basis of their periods

• Highest-priority task is the one with the shortest period

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EDF or RMS? (1)

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EDF or RMS? (2) Another example of real time scheduling with RMS and EDF

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Another example of real-time scheduling with RMS and EDF

EDF or RMS? (3)

• RMS “accomodates” task set with less utilization

1

m i

C ≤

∑

0.7

1 i i

P

=

∑

– (recall: for EDF that is up to 1)

• RMS is often used in practice;

– main reason: stability is easier to meet with RMS; priorities are static hence under transient period with deadline misses are static, hence, under transient period with deadline-misses, critical tasks can be “saved” by being assigned higher (static) priorities

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– it is ok for combinations of hard and soft RT tasks

Multipr cess r Systems Multiprocessor Systems

Scheduling

Multiprocessors Definition: A computer system in which two or more CPUs share full access to a common RAM CPUs share full access to a common RAM

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Multiprocessor/Multicore Hardware (ex.1) p ( ) Bus-based multiprocessors

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Multiprocessor/Multicore Hardware (ex.2) UMA ( nif m m m ss) UMA (uniform memory access)

Not/hardly scalable

• Bus-based architectures -> saturation
• Crossbars too expensive (wiring constraints)

Possible solutions

• Reduce network traffic by caching

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Reduce network traffic by caching

• Clustering -> non-uniform memory latency

behaviour (NUMA.

Multiprocessor/Multicore Hardware (ex.3) NUMA (n n nif m m m ss) NUMA (non-uniform memory access)

• Single address space visible to all CPUs
• Access to remote memory slower than to local

C h t ll /MMU d t i h th f i l l

• Cache-controller/MMU determines whether a reference is local or

remote

• When caching is involved, it's called CC-NUMA (cache coherent

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g , ( NUMA)

Hyperthreading yp g

HW i i f lti l HW gives image of multiple processors per processor OS can be oblivious; but will benefit from knowing that it runs on such a HW it runs on such a HW

• logical CPU = state only (not execution unit)

On multicores

Reason for multicores: physical limitations can cause significant heat dissipation by high clock rate; instead, parallelize within the same chip! parallelize within the same chip! In addition to operating system (OS) In addition to operating system (OS) support, adjustments to existing software are required to maximize utilization of the computing resources provided by multi-core processors.

Intel Core 2 dual core processor, with CPU-local

Virtual machine approach again in focus

p Level 1 caches+ shared,

• n-die Level 2 cache.

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focus.

On multicores (cont) ( )

• Also possible (figure from

www.microsoft.com/licensing/highlights/multicore.mspx)

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OS Design issues (1): Who executes the OS/scheduler(s)? Who executes the OS/scheduler(s)?

• Master/slave architecture: Key kernel functions always run on a
• Master/slave architecture: Key kernel functions always run on a

particular processor

– Master is responsible for scheduling; slave sends service request to th t the master – Disadvantages

• Failure of master brings down whole system
• Master can become a performance bottleneck
• Peer architecture: Operating system can execute on any processor

– Each processor does self-scheduling Each processor does self scheduling – New issues for the operating system

• Make sure two processors do not choose the same process

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Master-Slave multiprocessor OS p

Bus

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Non-symmetric Peer Multiprocessor OS y p

Bus

Each CPU has its own operating system

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Symmetric Peer Multiprocessor OS y p

• Symmetric Multiprocessors

Bus

Symmetric Multiprocessors

– SMP multiprocessor model

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Scheduling in Multiprocessors g p

Recall: Tightly coupled multiprocessing (SMPs)

– Processors share main memory – Processors share main memory – Controlled by operating system

Different degrees of parallelism

– Independent and Coarse-Grained Parallelism

• no or very limited synchronization
• no or very limited synchronization
• can by supported on a multiprocessor with little change (and a bit
• f salt ☺)

Medium Grained Parallelism – Medium-Grained Parallelism

• collection of threads; usually interact frequently

– Fine-Grained Parallelism

• Highly parallel applications; specialized and fragmented area

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Design issues 2: Assignment of Processes to Processors Assignment of Processes to Processors

P d l b l d Per-processor ready-queues vs global ready-queue

• Permanently assign process to a processor;

– Less overhead – A processor could be idle while another processor has a backlog

• Have a global ready queue and schedule to any available processor

– can become a bottleneck can become a bottleneck – Task migration not cheap (cf. NUMA and scheduling)

• Processor affinity – process has affinity for processor on which it

Processor affinity process has affinity for processor on which it is currently running

– soft affinity – hard affinity

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Multiprocessor Scheduling: per processor or per partition RQ per processor or per-partition RQ

• Space sharing

– multiple threads at same time across multiple CPUs

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Multiprocessor Scheduling: Load sharing / Global ready queue Load sharing / Global ready queue

• Timesharing

– note use of single data structure for scheduling

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g g

Multiprocessor Scheduling Load Sharing: a problem Load Sharing: a problem P bl ith i ti b t t th d

• Problem with communication between two threads

– both belong to process A b th i t f h s

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– both running out of phase

Design issues 3: Multiprogramming on processors? Multiprogramming on processors?

Experience shows: Experience shows: – Threads running on separate processors (to the extend of dedicating a processor to a thread) yields dramatic gains in performance performance – Allocating processors to threads ~ allocating pages to ( ki d l?) processes (can use working set model?) – Specific scheduling discipline is less important with more than

• n processor; the decision of “distributing” tasks is more

important

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Gang Scheduling Gang Sch u ng

• Approach to address the prev. problem :

1 Groups of related threads scheduled as a unit (a gang) 1. Groups of related threads scheduled as a unit (a gang) 2. All members of gang run simultaneously

• n different timeshared CPUs

3. All gang members start and end time slices together

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Gang Scheduling: another option g g p

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Multiprocessor Thread Scheduling Dynamic Scheduling Dynamic Scheduling

• Number of threads in a process are altered dynamically by the

application application

• Programs (through thread libraries) give info to OS to manage

parallelism

OS adjusts the load to improve use – OS adjusts the load to improve use

• Or os gives info to run-time system about available processors, to

adjust # of threads. i d i i f i i i

• i.e dynamic vesion of partitioning:

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Multithreaded Multicore System y

Solution by architecture: hyperthreading Needs OS awareness though to get the corresponding efficiency

Summary: Multiprocessor Thread Scheduling y p g

Load sharing: processors/threads not assigned to particular Load sharing: processors/threads not assigned to particular processors

• load is distributed evenly across the processors;

n ds central queue; m b b ttl n ck

• needs central queue; may be a bottleneck
• preemptive threads are unlikely to resume execution on the same

processor; cache use is less efficient

G h d li A i th d t ti l Gang scheduling: Assigns threads to particular processors (simultaneous scheduling of threads that make up a process)

• Useful where performance severely degrades when any part of the

p y g y p application is not running (due to synchronization)

• Extreme version: Dedicated processor assignment (no

multiprogramming of processors) p g g p )

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Operating System Examples p g y p

• Solaris scheduling
• Windows XP scheduling
• Linux scheduling

Solaris Scheduling

Kernel preemptible A la Multilevel feedback p mp by RT tasks in multiprocessors ( l feedback queues (unless interrupts disabled) disabled)

Solaris Dispatch Table p

Windows XP Priorities

Linux Scheduling

• Two priority ranges: time-sharing and

Two priority ranges: time sharing and real-time

• One queue per processor/core

One queue per processor/core

List of Tasks Indexed According to Priorities