Lecture 7 Real Time Task Scheduling. Forrest Brewer

Transcription

1 Lecture 7 Real Tme Task Schedulng Forrest Brewer

2 Real Tme ANSI defnes real tme as A Real tme process s a process whch delvers the results of processng n a gven tme span A data may requre processng at a pror known pont n tme, or t may be demanded wthout any pror knowledge Correctness of computaton Deadlnes (latest acceptable tme) Soft deadlne Dmnshed functonalty as deadlnes are mssed System does not fal Hard deadlne System fals (X-29 wngs fall off )

3 Real Tme Processng guarantees for tme-crtcal applcatons: Predctably fast response to tme-crtcal events & accurate tmng nformaton Jtter ssues Hgh degree of schedulablty Hgh degree of resource utlzaton below whch the processng guarantee s a queston Stablty under transent overload Under system overload, crtcal jobs processng of must be ensured Prorty Scheme, process preempton Sharng of Resources Management of Arbtraton Low Overhead

4 Fve Characterstcs of Real-Tme Operatng Systems Determnsm: concerned wth how long an operatng system delays before acknowledgng an event Responsveness: concerned wth how long after acknowledgment, t takes an operatng system to fnsh the event (nterrupt) servce Determnsm and responsveness together make up the response tme to external events whch are crucal for realtme systems User control: allow the user (dynamc?) fne-graned control over task prorty Relablty: a transent falure may cause fnancal loss or major equpment damage or even loss of lfe. Fal-soft operaton: durng overload, contnued operaton at a reduced level of servce

5 System Modelng n RT Schedulng Tasks are the schedulable unt of the system. A task s characterzed by tmng constrants and resource requrements. Perodc task (T) processng tme deadlne perod Perod of T Deadlne of T 0 Perodc task T Processng tme of T

6 Real tme schedulng: Perodc system model Task: schedulable entty Processng of separate tasks are assumed mutually ndependent Tmng constrants of a perodc task τ s specfed by (s, e, D, p) s -(scheduled) Startng Tme of Task e -Processng tme of f -Fnsh tme of D -Deadlne of p -Perod of r -Rate of = (1/p )

7 Real tme schedulng: Perodc system model Tasks can be Preemptve Nonpreemptve Guarantee rato Processng tme used by guaranteed tasks versus total processng tme Utlzaton: U = n = 1 e p

8 System Model - Assumptons and Notaton Assumptons: Perodc tasks wthout precedence relatons Amed at vertcal system decomposton No OS overhead tme added to every task nvocaton ths s a problem for preemptve task models Tme Constrants (non-perodc): C = {t1=(s 1, e 1, D 1 ), t3=(s 3, e 3, D 3 ), t2=(s 2, e 2, D 2 ), }

9 Non-Repeatng Schedule Non-Repeatng Schedule k D f s S k t s f f s n t f s A = < = = + & } 1,..., ),, {( 1 A schedule s a set of executon ntervals s=start tme of nterval, f=fnsh tme of nterval, t=the task executed durng the nterval A schedule s feasble f every task ح k receves at least e k seconds of CPU executon n the schedule k A k f s k e s f Feasble Schedule k t A a t f s a A k = = = ) ( ),, ( } & ),, ( { ) ( τ τ Note: a task may be segmented nto several executon ntervals

10 Schedule Example C={t1=(0,8,13), t2=(3,5,10), t3=(4,7,20)} A={(0,3,t1),(3,8,t2),(8,13,t1),(13,7,t3)} s a feasble schedule for t1, (3-0) + (13-8) = = 8 C={t1=(1,8,12), t2=(3,5,10), t3=(4,7,14)} No feasble schedule

11 Real-Tme Schedulng Polces Statc table-drven Sutable for perodc tasks/earlest-deadlne frst schedulng Requres Statc analyss of feasble schedule Statc prorty-drven preemptve rate monotonc algorthm Statc analyss to determne prorty Tradtonal prorty-drven scheduler s used Dynamc plannng-based (evaluate prortes on the fly) Create a schedule contanng the prevously scheduled tasks and the new arrval f all tasks meets ther constrants, the new one s accepted Dynamc best effort No feasblty analyss s performed Assgned a prorty to the new arrval then apply earlest deadlne frst System tres to meet all deadlnes and aborts any started process whose deadlne s mssed

12 Perodc tasks: Example Suppose the tasks tsk 1 tsk 3 have the followng propertes name executon tme [msec] perod [msec] Deadlne [msec] tsk tsk tsk The tasks get assgned prortes Once assgned, these prortes do not change; The tasks are scheduled accordng to ther prortes,.e. a ready task wth hghest prorty s executed untl a hgher prorty task becomes ready. Such hgher prorty task then pre-empts the lower prorty task.

13 Tme Lne Schedulng (Cyclc Schedulng) Tme Lne Schedulng (Off-lne schedulng strategy) Dvde the tme lne nto tme slces for schedulng tasks, e.g. use the Greatest Common Dvsor of the Task Perods as the tme slce:

14 Executon tme based prorty Suppose we assgn the prortes dependng on ther (worst) computaton tme, I.e. the longer the computaton tme the hgher prorty L M H name executon tme [msec] perod [msec] Deadlne [msec] tsk tsk tsk What wll be then the executon? Tsk 1 Tsk2 100 Deadlne s mssed 150 Tsk3 350

15 Executon tme based prorty Suppose we assgn the prortes dependng on ther (worst) computaton tme, I.e. the shortest the computaton tme the hgher prorty M H L name executon tme [msec] perod [msec] Deadlne [msec] tsk tsk tsk Tsk Tsk Tsk3 350

16 Questons In ths specfc case, ths prorty assgnment works Does t always work? If t does not work n ths specfc case s there an assgnment that always works? Is there a better way (than trace analyss) to decde whether an assgnment works?

17 Rate monotonc schedulng Classc paper, Lu & Layland, JACM 1973 m tasks, wth perodctes (P ), deadlnes (D = P ) and computaton tme (C ) Monotone Prorty: task frequency = f = task prorty = 1/ P ), Always Scheduable f (but not only-f): U n C = P = 1 n( n 2 1) Smple, elegant result No tght upper bound on the Utlzaton metrc s avalable a trval upper bound can be summaton of the Task Utlzaton <= 1 n Note: lm n( 2 1) = ln(2) = n

18 Rate Monotonc Schedulng Assumptons Tasks are perodc Tasks do not communcate wth each other Tasks are scheduled accordng to prorty, and task prortes are fxed (statc prorty schedulng) Note A task set may have feasble schedule, but not by usng any statc prorty schedule Feasble statc prorty assgnment Rate Monotonc Schedulng (RMS) Assgns prortes to tasks on the bass of ther perods Hghest-prorty task s the one wth the shortest perod If p h < p l, then Prorty h > Prorty l

19 C = { τ = ( C, P ) 1,..., n} = Perodc Real-tme task set P=perod The start tme of a new nstance of a job s the deadlne of the last nstance

20 Rate Monotonc Schedulng Process Prorty determned by arrval rate (snce rate = 1/perod) Process 1 : Hgh Prorty Process 2 : Lower Prorty Preemptve Nonpreemptve

21 Example of Rate Monotonc Schedulng P1: C1 = 1; T1= 2; C1/T1= 0.5 P2: C2 = 1; T2= 3; C2/T2= P3: C3= 1; T3= 6; C3/T3= Total utlzaton = 1.0 Snce: 1.0 <= 1.0 < 3 (2 1/3 1) = May or may not be schedulable However f C1 = ½ the total utlzaton would be 0.75 and the system wll always be schedulable.

22 Crtcal Instant of J3 J1 Arrve at 0, 2, 4, 6 J2 Arrve at 0, 3, 6, 9 J3 Arrve at 0, 6, 12, 18 C={(1,2),(1,3),(1,6)}

23 Release J1 Earler J1: -0.5, 1.5, 3.5, J2: 0, 3, 6, J3: 0, 6, 12, 18 C={(1,2),(1,3),(1,6)}

24 Release J1 Later J1 J1: 2, 4, 6, J2 J2: 0, 3, 6, 9 J3 J3: 0, 6, 12, 18 C={(1,2),(1,3),(1,6)}

25 RMS s Optmal If a set of perodc tasks has a feasble statc prorty assgnment, RMS s a feasble statc prorty assgnment Outlne of Proof: Hnt: f there s a non-rms feasble statc prorty assgnment Lst the tasks n decremented order of prorty Because non-rms, there must be ح and ح +1 T > T +1 Prove exchange ح and ح +1 Repeat the prorty exchange such that and the schedule s feasble

26 Value of the threshold factor m m*(2 1/m -1)

27 Prorty Inverson RMA assumpton: the processes are ndependent Issue: real RT-processes often are requred to share resources that are unque to the system under such crcumstances processes can block each other. In partcular: executon of hgh prorty task can be blocked by executon of a low prorty task whch has locked a requred resource In other word: prortes are effectvely nverted

28 Prorty nverson: Example tasks 1 and 3 share a resource (S1) task 1 (hgh) ncsp CS1 pro(task1) >pro(task2) >pro(task3) task 2 (med) ncsp Z1,1 Task 2 can run for any amount of tme t blocks task 3 (low) CS1 CS1 CS1 Task 3 from fnshng and Z3,1 unlockng resource needed by task 1. task 3 locks S1 Infamous Mars pathfnder Prorty Inverson Bug task 1 attempts to lock S1 (blocked) task 3 unlocks S1 (task 1 unblocked) task 1 locks S1 task 1 unlocks S1

29 Example Contnued Concluson Hgh prorty task (task 1) s blocked by low prorty task (task 3) the blockng perod can be arbtrarly long Possble solutons: no preempton durng crtcal secton (Interrupt-Maskng Protocol) good for short crtcal sectons, otherwse bad: unnecessary CS blockng n crtcal secton (CS), rase the task's prorty to a level hgher than all tasks ever usng that CS (Prorty nhertance protocol) In example, ths prohbts Task 2 from preemptng Task 3 dsadvantage: unnecessary (prorty) blockng, possblty of deadlock

30 Prorty Celng Protocol Each resource s assgned a prorty equal to that of the hghest prorty task that uses that resource. Tasks then nhert the prorty of the resource whle t s locked Tasks are not scheduled f any resource t may need t already locked by another task Ths scheme prevents mproper nestng of the prortes of crtcal secton and thus prevents deadlocks Ref: Lu Sha, Ragunathan Rajkumar, and John P. Lehoczky (September 1990). "Prorty Inhertance Protocols: An Approach to Real-Tme Synchronzaton". IEEE Transactons on Computers 39 (9): do: /

31 Deadlne Schedulng Deadlne Schedulng: the task whch has the earlest deadlne, wll be scheduled frst A system that collects and processes data from two sensors, A and B. The deadlne for collectng data from sensor A must be met every 20 ms, and that for B every 50 ms. It takes 10 ms to process each sample of data from A and 25 ms to process each sample of data from B.

32 Example of Deadlne Schedulng (cont.)

33 Earlest Deadlne Frst Algorthm Very well known for real-tme processng Optmal dynamc algorthm - produces a vald schedule whenever one exsts. If prortes are used, earlest deadlne gets the hghest prorty. Complexty of algorthm s O(n^2). Upper bound of process utlzaton s 100%. Tme Drven Scheduler - extenson of EDF handles overload stuaton by abortng tasks f overload occurs. It also removes tasks from the queue wth low prorty.

34 Earlest Deadlne Frst (EDF) Algorthm Best known algorthm for real tme processng At every new ready state, the scheduler selects the task wth earlest deadlne among the tasks that are ready & not fully processed The processng of the nterrupted task s done accordng to EDF algorthm later on Optmal algorthm Dynamc algorthm

35 Earlest Deadlne Frst (EDF) Algorthm Optmal Produces a vald schedule whenever exsts If a task can be scheduled usng any statc prorty assgnment, t can also be scheduled by EDF Dynamc Schedules every nstances of ncomng task accordng to ts specfc demands Each task s assgned a prorty accordng to ts deadlne Hghest prorty to the task wth earlest deadlne

36 Earlest Deadlne Frst (EDF) Algorthm Overhead n rearrangng prortes TDS-Tme drven Scheduler An extenson of EDF Handles overload Aborts all the tasks that cannot meet ther deadlnes anymore If there s stll overload, tasks wth low value denstes(mportance of a task for the system) are removed EDF

37 Earlest Deadlne Frst (EDF) Algorthm Another varaton handles every task as consstng of two parts, mandatory part and optonal part A task s scheduled for t s mandatory part Optonal part s processed, f the resource capacty s not fully utlzed A set of task s schedulable f all tasks can meet the deadlnes of ther mandatory part Improves the system performance at the expense of meda qualty

38 EDF Schedulng Process Streams scheduled accordng to ther deadlnes A1 A2 A Process j Aj1 1 D1 D2 D3 Aj2 Aj3 Aj4 Aj Dj1 Dj2 Dj3 Dj Both streams scheduled accordng to ther deadlnes

39 Comparson of EDF and Rate Monotonc Schedulng deadlne d1 d2 da d3 d4 db d5 d6 dc Hgh rate Low rate In terms of context swtchng, EDF s better s more than one stream s processed concurrently. EDF Rate Monotonc

40 Context swtches : EDF & Rate Monotonc Audo stream have the rate of 1/75 s/sample & vdeo stream have the rate of 1/25 s/frame Prorty assgned to an audo stream s then hgher Arrval of messages from audo stream wll nterrupt vdeo frame The context swtches wth rate monotonc algorthm wll be more than EDF n the presence of more than one stream

41 Processor Utlzatons : EDF & Rate Monotonc Processor utlzaton n rate monotonc Upper bound of processor utlzaton s determned by crtcal nstant For each number of n ndependent tasks t(j), a constellaton can be found where maxmum possble processor utlzaton s mnmal

42 Processor Utlzatons : EDF & Rate Monotonc

43 Schedulng of Perodc Dependant tasks The sharng of (data) resource, when the use of the resource must be atomc, and the tasks must realse deadlnes, necesstates choosng: a synchronsaton prmtve (semaphors, regons etc.) an allocaton polcy (what happens when request s made but the resource s taken); an executon prorty durng the use of the resource (change? of prorty whle usng of a resource); Defnton: Combned choce s called a synchronsaton protocol;

44 Examples of synchronsaton protocols: FIFO semaphores ; semaphores are used to mplement the crtcal secton f the resource s busy, queueng s performed n FIFO order; the task that s usng the resource does not adjust ts executon prorty; nterrupt maskng nterrupt maskng; dsable pre-empton set nterrupt level to the maxmum level;

45 Preemptve vs. Non preemptve schedulng The best schedulng algorthm maxmzes the number of completed tasks Tasks are usually treated as preemptve, to guarantee the processng of perodc processes Hgh preemtablty mnmzes prorty nverson There may not be any feasble schedule for non-preemptve schedule Schedulng of non-preemptve tasks s less favorable because number of schedulable task sets s smaller compared to preemptve tasks