Real-Time Operating Systems M. 11. Real-Time: Periodic Task Scheduling
|
|
|
- Irma Spencer
- 5 years ago
- Views:
Transcription
1 Real-Tme Operatng Systems M 11. Real-Tme: Perodc Task Schedulng
2 Notce The course materal ncludes sldes downloaded from:! and! (sldes by Slberschatz, Galvn, and Gagne, assocated wth Operatng System oncepts, 9th Edton, Wley, 2013)! (sldes by Buttazzo, assocated wth Hard Real-Tme omputng Systems, 3rd Edton, Sprnger, 2011)! whch has been edted to sut the needs of ths course.! The sldes are authorzed for personal use only.! Any other use, redstrbuton, and any for proft sale of the sldes (n any form) requres the consent of the copyrght owners.! 11.2! Torron, Real-Tme Operatng Systems M 2013!
3 Problem Formulaton (, T ) job k r k d k For each perodc task, guarantee that:! Each job τ k s actvated at r j = (k-1)t! Each job τ k completes wthn d k = r j + D! 11.3! Buttazzo, Hard Real-Tme omputng Systems 2013!
4 A Farm Schedulng Problem Feed cow for 25 mn / 50 mn Feed pg for 10 mn / 20 mn 11.4! Buttazzo, Hard Real-Tme omputng Systems 2013!
5 Frst Algorthm Alternate pg wth cow! Pg ow Evaluaton:! Pg gets hungry! ow gets fat! 11.5! Buttazzo, Hard Real-Tme omputng Systems 2013!
6 Second Algorthm Feed pg and cow 10 mn each! Pg ow Evaluaton:! Pg s OK! ow s not happy! 11.6! Buttazzo, Hard Real-Tme omputng Systems 2013!
7 Thrd Algorthm Feed pg and cow 5 mn each! Pg ow Evaluaton:! Pg s OK, ow s OK! Farmer s tred! 11.7! Buttazzo, Hard Real-Tme omputng Systems 2013!
8 Optmal Algorthm Feed the most starvng anmal! Pg ow Evaluaton:! Everybody s happy! 11.8! Buttazzo, Hard Real-Tme omputng Systems 2013!
9 What Do We Learn? Reducng the executon tme wndow, we get closer to a feasble soluton! The tme s splt proportonally between the anmals! In the example, each anmal requred food for 50% of the tme! How can we generalze the soluton f the anmals requre dfferent fractons of tme?! 11.9! Buttazzo, Hard Real-Tme omputng Systems 2013!
10 A New Schedulng Problem Feed cow for 4 mn / 16 mn Feed pg for 20 mn / 40 mn 11.10! Buttazzo, Hard Real-Tme omputng Systems 2013!
11 Proportonal Share Proportonal Share algorthm:! 1. Dvde the tmelne nto slots of equal length! 2. Wthn each slot serve each task for a tme proportonal to ts utlzaton:! ow utlzaton factor = 4/16 = ¼! Pg utlzaton factor = 20/40 = ½! Pg 4/16 ow 20/ ! Buttazzo, Hard Real-Tme omputng Systems 2013!
12 Proportonal Share In general:! Let: U = requred feedng fracton! Δ = GD(T 1,T 2 ) = 8! Execute each task for δ = U Δ n each slot Δ! Pg 4/16 ow 20/ Note: U Δ ensures n T, n fact: δ(t /Δ)=! Feasblty test: Σδ Δ,.e., ΣU 1! 11.12! Buttazzo, Hard Real-Tme omputng Systems 2013!
13 Tmelne Schedulng (yclc Executve) It has been used for 30 years n mltary systems, navgaton, and montorng systems.! Examples:! Ar traffc control! Space Shuttle! Boeng 777! Idea:! Dvde tme axs n slots of equal length! Desgn statc schedulng (by hand)! Allocate each task n a slot, so as to meet the desred request rate! Actvate executon of each slot by a tmer! 11.13! Buttazzo, Hard Real-Tme omputng Systems 2013!
14 Example task A B f T 40 Hz 25 ms 20 Hz 50 ms 10 Hz 100 ms = GD (mnor cycle) T = lcm (major cycle) T Guarantee s very smple (wthn each mnor cycle):! A + B Δ = 25 ms! A + c Δ = 25 ms! 11.14! Buttazzo, Hard Real-Tme omputng Systems 2013!
15 yclc Executve: Implementaton The task sequence s not decded by a schedulng algorthm n the kernel, but t s trggered by calls made by the man program (no context swtches)! A B A A B tmer tmer tmer mnor cycle major cycle A tmer ! Buttazzo, Hard Real-Tme omputng Systems 2013!
16 yclc Executve: PROs and ONs Advantages: lghtweghtness, regularty! Smple mplementaton (does not requre RTOS)! Low run-tme overhead! Allows jtter control! Dsadvantages: rgdty! Fragle durng overloads! Dffcult to expand the schedule! Dffcult to handle aperodc actvtes! 11.16! Buttazzo, Hard Real-Tme omputng Systems 2013!
17 Problems Durng Overloads What do we do durng task overruns?! Let the task contnue! May have domno effect on all the other tasks (tmelne break)! Abort the task! The system can reman n nconsstent states! 11.17! Buttazzo, Hard Real-Tme omputng Systems 2013!
18 Expandblty If one or more tasks need to be upgraded, we! may have to re-desgn the whole schedule agan! task A B f T 40 Hz 25 ms 20 Hz 50 ms 10 Hz 100 ms Example:! Stuaton: B s updated but A + B > Δ! (prevous schedule) A B 0 25 Acton: splt B n two subtasks, B 1 and B 2, and re-buld the schedule! A B 1 A B 2 A B 1 A B ! Buttazzo, Hard Real-Tme omputng Systems 2013!
19 Expandblty task A B f T 40 Hz 25 ms 20 Hz 50 ms If the frequency of some task s changed, the! mpact can even be more sgnfcant:! 10 Hz 100 ms Example:! Stuaton: B s cycle changes from 50 ms to 40 ms! (prevous schedule) task A B T 25 ms 50 ms T 25 ms 40 ms 100 ms 100 ms before after mnor cycle: major cycle: = 25 = 5 T = 100 T = sync. per cycle! Acton: re-buld the schedule usng dfferent major/mnor cycle length! 11.19! Buttazzo, Hard Real-Tme omputng Systems 2013!
20 Expandblty task A B f T 40 Hz 25 ms 20 Hz 50 ms task A B T T 25 ms 25 ms 50 ms 40 ms 100 ms 100 ms before after mnor cycle: = 25 = 5 major cycle: T = 100 T = sync. per cycle! 10 Hz 100 ms (prevous schedule) T T 11.20! Buttazzo, Hard Real-Tme omputng Systems 2013!
21 Prorty Schedulng Idea:! Assgn each task a prorty based on ts tmng constrants! Verfy the feasblty of the schedule usng analyss technques! Execute tasks on a prorty-based kernel! Prortes could be statc or dynamc! Examples:! RM: assgn fxed prorty to tasks, proportonal to task rate! EDF: at all tmes, assgn top prorty to job wth earlest absolute deadlne! 11.21! Buttazzo, Hard Real-Tme omputng Systems 2013!
22 Assumptons The nstances of a perodc task are regularly actvated at a constant rate wth perod T.! All tasks are released as soon as they arrve.! All nstances of a perodc task have:! the same worst-case executon tme! the same relatve deadlne D = T! Independent tasks (no precedence relatons, no resource constrants)! No task can suspend tself (trap)! Neglgble kernel overheads! T 11.22! Buttazzo, Hard Real-Tme omputng Systems 2013!
23 Statc Prorty Schedulng
24 How to assgn prortes? Typcally, task prortes are assgned based on the tasks relatve mportance! Example: Solars schedulng! hghest global prorty nterrupt threads schedulng order frst However, dfferent assgnments can lead to dfferent utlzaton bounds! realtme (RT) threads system (SYS) threads far share (FSS) threads fxed prorty (FX) threads tmeshare (TS) threads lowest 0 nteractve (IA) threads last 11.24! Buttazzo, Hard Real-Tme omputng Systems 2013!
25 Prorty Importance If τ 2 s more mportant than τ 1 and s assgned a hgher prorty!! the schedule may not be feaslble...! 1 P 1 > P 2 2 deadlne mss 1 P 2 > P ! Buttazzo, Hard Real-Tme omputng Systems 2013!
26 Prorty Importance!!!!!!! whle the utlzaton upper bound can be arbtrarly small.! P 2 > P deadlne mss U = ε/ /! 0! An applcaton can be unfeasble even f the processor s almost empty!! 11.26! Buttazzo, Hard Real-Tme omputng Systems 2013!
27 Rate Monotonc (RM)! Assgn each task a fxed prorty proportonal to ts request rate! (Lu & Layland 73)! A B ! Buttazzo, Hard Real-Tme omputng Systems 2013!
28 Rate Monotonc s Optmal RM s optmal among all fxed prorty algorthms (f D = T )! RM optmalty (n the sense of feasblty):! If there exsts a fxed prorty assgnment whch leads to a feaslbe schedule for Γ, then the RM assgnment s feasble for Γ.! If Γ s not schedulable by RM, then t cannot be scheduled by any fxed prorty assgnment.! 11.28! Buttazzo, Hard Real-Tme omputng Systems 2013!
29 An RM-Unfeasble Schedule D = T U pp deadlne mss 11.29! Buttazzo, Hard Real-Tme omputng Systems 2013!
30 Identfyng the Worst ase Feasblty may depend on the ntal actvatons (phases):! U p deadlne mss ! Buttazzo, Hard Real-Tme omputng Systems 2013!
31 rtcal Instant The longest response tme occurs when a task arrves together wth all hgher prorty tasks! 1 2 R R ! Buttazzo, Hard Real-Tme omputng Systems 2013!
32 rtcal Instant For ndependent preemptve tasks under fxed prortes, the crtcal nstant of τ occurs when t arrves together wth all hgher prorty tasks.! 1 1/6 2 2/8 3 2/12 Idle tme 2/ ! Buttazzo, Hard Real-Tme omputng Systems 2013!
33 How can we verfy feasblty? Each task uses the processor for a fracton of tme:! U T Hence the total processor utlzaton s:! U p n 1 T U p s a measure of the processor load! 11.33! Buttazzo, Hard Real-Tme omputng Systems 2013!
34 A Necessary ondton If U p > 1 the processor s overloaded hence the task set cannot be schedulable! However, there are cases where:! U p < 1! but the task set s not schedulable by RM! U p U p deadlne mss Utlzaton upper bound: f 1 or 2 s ncreased, τ 2 wll mss ts deadlne!! 11.34! Buttazzo, Hard Real-Tme omputng Systems 2013!
35 and a Dfferent Upper Bound 2 4 U ub The upper bound U ub depends on the specfc task set.! 11.35! Buttazzo, Hard Real-Tme omputng Systems 2013!
36 and Yet Another One 2 4 U 1 4 p The upper bound U ub depends on the specfc task set.! In these examples: U ub = 0.833, 0.9, 1,.! Is there anythng more we can tell about U ub?! 11.36! Buttazzo, Hard Real-Tme omputng Systems 2013!
37 The Least Upper Bound U ub 1 U lub ! Buttazzo, Hard Real-Tme omputng Systems 2013!
38 A Suffcent ondton Gven a task set Γ:! If U p U lub, Γ s certanly schedulable wth RM! If U lub < U p 1, we cannot say anythng about Γ s feasblty! 11.38! Buttazzo, Hard Real-Tme omputng Systems 2013!
39 Least Upper Bound for RM Lu & Layland, 1973! Gven a set of n perodc tasks:! U RM lub n 2 1/ n 1 for n U lub ln 2 Used for RM guarantee test:! ompute processor utlzaton! Verfy that t does not exceed the least upper bound! 11.39! Buttazzo, Hard Real-Tme omputng Systems 2013!
40 RM Schedulablty U lub RM for n tasks! n! n(2 1/n -1)! 1! 1! 2! 0.828! 3! 0.780! 4! 0.757! 5! 0.743! 10! 0.718! 20! 0.705! 50! 0.698! 100! 0.696! 1000! 0.693! PU% n 69% 11.40! Buttazzo, Hard Real-Tme omputng Systems 2013!
41 RM Schedulablty U ub RM for 2 tasks s a functon of! F =!" T 2 T 1 #$ n! n(2 1/n -1)! 1! 1! 2! 0.828! 3! 0.780! 4! 0.757! 5! 0.743! 10! 0.718! 20! 0.705! 50! 0.698! 100! 0.696! 1000! 0.693! F U* 1! 0.828! 2! 0.899! 3! 0.928! 4! 0.944! 5! 0.954! 10! 0.976! 20! 0.988! 50! 0.995! 100! 1.000! 1000! 1.000! U * = 2( F(F +1) F) 11.41! Buttazzo, Hard Real-Tme omputng Systems 2013!
42 A Tghter Least Upper Bound for RM Bn & Buttazzo 2, 2000! Hyperbolc Bound! A set of n perodc tasks s schedulable wth RM f:! n 1 (U 1) 2 It s a tght bound: gven any set of utlzatons that volate the HB, t s always possble to produce an unfeasble task set wth those utlzaton.! 11.42! Buttazzo, Hard Real-Tme omputng Systems 2013!
43 omparson The hyperbolc bound can be compared wth the Lu-Layland bound n the task utlzaton space (U-space)! U HB vs. LL n 1/ n LL U n(2 1) 1 n HB ( 1) 2 1 U U The gan acheved by HB over LL ncreases wth n (t tends to 2)! ! Buttazzo, Hard Real-Tme omputng Systems 2013!
44 Exercse 4.1! T! τ 1! 2! 6! τ 2! 2! 8! τ 3! 2! 12! Verfy the schedulablty and construct the RM schedule.! 11.44! Buttazzo, Hard Real-Tme omputng Systems 2013!
45 Exercse 4.2! T! τ 1! 3! 5! τ 2! 1! 8! τ 3! 1! 10! Verfy the schedulablty and construct the RM schedule.! 11.45! Buttazzo, Hard Real-Tme omputng Systems 2013!
46 Extenson to Tasks wth D < T More modelng possbltes. For nstance:! Tasks wth jtter constrants;! Actvtes wth shorter response tme wth respect to ther perod.! T D r,k d,k r,k+1 t Deadlne Monotonc (DM):! P 1/D (statc)! Earlest Deadlne Frst (EDF):! P 1/d,k (dynamc)! 11.46! Buttazzo, Hard Real-Tme omputng Systems 2013!
47 Deadlne Monotonc! Assgn each task a fxed prorty nversely proportonal to ts relatve deadlne! (Leung & Whtehead 1982)!! ! Buttazzo, Hard Real-Tme omputng Systems 2013!
48 Deadlne Monotonc s Optmal If D < T, f a task set s schedulable by some fxed prorty assgnment, then t s also schedulable by DM.! DM P 2 > P RM P 1 > P ! Buttazzo, Hard Real-Tme omputng Systems 2013!
49 Good News, Bad News Good news! DM gves optmal prorty assgnment.! Bad news! problem wth the utlzaton bound:!! U p n 1 D but the task set s schedulable! PU workload overestmated! RM guarantee test too pessmstc for DM! 11.49! Buttazzo, Hard Real-Tme omputng Systems 2013!
50 Seen so far Problem defnton (perodc task schedulng)! oncepts (processor utlzaton, crtcal nstant, upper bound)! Schedulng Algorthms! Theoretcal (Proportonal Share)! Paper & pencl (Tmelne Schedulng)! Fxed Prorty (optmal)! Rate Monotonc f D=T! Deadlne Monotonc f D<T! Shedulablty Analyss! Least Upper Bound! Lu-Layland! Hyperbolc Bound! Next?! 11.50! Torron, Real-Tme Operatng Systems M 2013!
51 Response Tme Analyss A suffcent and necessary schedulablty test for DM (Audsley et al., 1990)!! For each task τ, compute the nterference (preempton) due to hgher prorty tasks:! I k D k D ompute ts response tme as:! Verfy whether! R D R = + I! 11.51! Buttazzo, Hard Real-Tme omputng Systems 2013!
52 omputng the Interference Assume tasks are ordered by ncreasng relatve deadlnes! < j f and only f D D j! k 0 R Interference of k on n the nterval [0, [, R ] ]: Interference of hgh prorty tasks on : I I k 1 k 1 R T T k R T k k k 11.52! Buttazzo, Hard Real-Tme omputng Systems 2013!
53 11.53! Buttazzo, Hard Real-Tme omputng Systems 2013! omputng the Response Tme R R 1 k k k T R 1 Iteratve soluton: R 0 67 k k s k s T R R 1) ( 1 1 R terate whle ( 1) s s R R k 1 1 k k s k s T R R 1) ( 1 1 R terate whle ( 1) s s R R omputng Response Tme R R 1 k k k T R 1 Iteratve soluton: R 0 67 k k s k s T R R 1) ( 1 1 R terate whle ( 1) s s R R 14/11/2012 omputng Response Tme R R 1 k k k T R 1 Iteratve soluton: R 0 67 k k s k s T R R 1) ( 1 1 R terate whle ( 1) s s R R
54 Exercse 4.7! D! T! τ 1! 2! 5! 6! τ 2! 2! 4! 8! τ 3! 4! 8! 12! Verfy the schedulablty and construct the DM schedule.! 11.54! Buttazzo, Hard Real-Tme omputng Systems 2013!
55 Exercse 4.3! T! τ 1! 1! 4! τ 2! 2! 6! τ 3! 3! 10! Verfy the schedulablty and construct the RM schedule.! 11.55! Buttazzo, Hard Real-Tme omputng Systems 2013!
56 Exercse 4.4! T! τ 1! 1! 4! τ 2! 2! 6! τ 3! 3! 8! Verfy the schedulablty and construct the RM schedule.! 11.56! Buttazzo, Hard Real-Tme omputng Systems 2013!
57 Dynamc Prorty Schedulng
58 EDF D = T! No constrants! U p RM: unfeasble.! EDF: feasble!! Wth EDF, any task set can utlze the processor up to 100%! 1 2 D = T U p deadlne mss 11.58! Buttazzo, Hard Real-Tme omputng Systems 2013!
59 EDF Optmalty & Schedulablty Optmalty. EDF s optmal among all algorthms (Dertouzos 1974)! If there exsts a feasble schedule for Γ, then EDF wll fnd a feasble schedule! If Γ s not schedulable by EDF, then t cannot be scheduled by any algorthm! (result ndependent of perodcty)! Schedulablty. For a set of n perodc tasks,! U EDF lub 1!(Lu & Layland 1973)! In other words, a task set Γ s EDF-schedulable f and only f U p 1! 11.59! Buttazzo, Hard Real-Tme omputng Systems 2013!
60 EDF Schedulablty Necessty: schedulable! U p 1 s trval! To prove suffcency: U p 1! schedulable! 1. We fnd any algorthm for whch the above condton holds! 2. Then, for the EDF optmalty, we can say that the above condton also holds for EDF.! t onsder the algorthm whch schedules n every nterval of length Δ a fracton of task: δ = U t Δ! Proportonal Share Algorthm! n Feasblty s ensured f, that s, f U p 1.! ! Buttazzo, Hard Real-Tme omputng Systems 2013!
61 Exercse 4.5! T! τ 1! 1! 4! τ 2! 2! 6! τ 3! 3! 8! Verfy the schedulablty and construct the EDF schedule.! 11.61! Buttazzo, Hard Real-Tme omputng Systems 2013!
62 Schedulablty wth Dynamc Prorty What f D T?! Processor Demand rteron! n any nterval, the computaton demanded by the task! must be no greater than the avalable tme! (Baruah, Roser & Howell 1990)! t 1 t 2 Demand of a task τ n [t 1,t 2 ]: amount of processng tme g (t 1,t 2 ) of all nstances of τ that are actvated n [t 1,t 2 ] and must be completed n [t 1,t 2 ].! For the whole task set: g(t 1,t 2 )! g( t1, t2) d t r t ! Buttazzo, Hard Real-Tme omputng Systems 2013!
63 Processor Demand Test 0 L Processor Demand n [0,L] aka Demand Bound Functon, dbf(l)! g(0, L) n 1 L D T T Demand Test! L 0, g(0, L) L How can we bound the number of ntervals n whch the test has to be performed?! 11.63! Buttazzo, Hard Real-Tme omputng Systems 2013!
64 Example g(0, L) L 2 0 L ! Buttazzo, Hard Real-Tme omputng Systems 2013!
65 11.65! Buttazzo, Hard Real-Tme omputng Systems 2013! Boundng omplexty Some consderatons:! Snce g(0,l) s a step functon, t suffces to check feasblty only at deadlne ponts (d k )! If tasks are synchronous and U p < 1, t suffces to check feasblty only up to the hyperperod H = lcm(t 1,, T n )! g(0,l) G(0,L) and, f U < 1, there exsts an L* for whch G(0,L*) = L*.! n T D T L L G 1 ) (0, n n D T L ) ( T D T T L 1 1 ) ( n U D T LU 1 ) ( n U D T LU L G 1 ) ( ) (0, L g(0, L) G(0, L) 1 f L>L * U U D T L n 1 ) ( 1 * L L * for L > L * g(0,l) G(0,L) < L
66 Processor Demand Test A set of synchronous perodc tasks wth relatve deadlnes less than or equal to perods can be scheduled by EDF f and only f! U < 1, and!! L D, g (0, L ) L D = {d k d k mn (H, L * )} H = lcm(t 1,, T n n) L * n 1 ( T D ) U 1U g(0, L) n 1 L D T T 11.66! Buttazzo, Hard Real-Tme omputng Systems 2013!
67 Exercse 4.6! D! T! τ 1! 2! 5! 6! τ 2! 2! 4! 8! τ 3! 4! 8! 12! Verfy the schedulablty and construct the EDF schedule.! 11.67! Buttazzo, Hard Real-Tme omputng Systems 2013!
68 Quzzes True or False?! If a task set s DM-schedulable, t s EDF-schedulable! If a task set s EDF-schedulable, ts processor utlzaton U p s below the HB! If a task set s processor utlzaton U p s below the Lu-Layand bound, then U p s also below the HB! A task set consstng of two tasks, τ 1 and τ 2, wth D =T and T 1 =2T 2, s RM-feasble f and only f the total processor utlzaton s at most 1! Response Tme Analyss can be used to study schedulablty, even f relatve deadlne and pedod concde (for all τ, D =T )! The Processor Demand Test can be used to study schedulablty, even f relatve deadlne and pedod concde! If for all τ, D =T, the Processor Demand Test and Response Tme Analyss of a gven task set gve the same schedulablty result!! 11.68! Torron, Real-Tme Operatng Systems M 2013!
69 Summary
70 Perodc Task Schedulng Three schedulng approaches! Off-lne constructon (tmelne)! Fxed prorty (RM, DM)! P 1/T " P 1/D! Dynamc prorty (EDF)! P 1/d,k, d,k = r,k + D! Three analyss technques:! Processor Utlzaton Bound!!U U lub!!!o(n)! Response Tme Analyss!!for all, R D!!*! Process Demand rteron!!for all L, g(0,l) L!*!!* pseudo-polynomal complexty! 11.70! Buttazzo, Hard Real-Tme omputng Systems 2013!
71 RM vs. EDF RM! Smpler to mplement n commercal operatng systems! fxed prortes! More predctable durng! EDF! overloads! hghest prorty tasks are!! known! More effcent! Reduces context swtches! RM Better responsveness n handlng aperodc tasks! Perod rescalng durng permanent overloads! EDF deadlne mss ! Buttazzo, Hard Real-Tme omputng Systems 2013!
Real-Time Systems. Multiprocessor scheduling. Multiprocessor scheduling. Multiprocessor scheduling
Real-Tme Systems Multprocessor schedulng Specfcaton Implementaton Verfcaton Multprocessor schedulng -- -- Global schedulng How are tasks assgned to processors? Statc assgnment The processor(s) used for
Embedded Systems. 4. Aperiodic and Periodic Tasks
Embedded Systems 4. Aperodc and Perodc Tasks Lothar Thele 4-1 Contents of Course 1. Embedded Systems Introducton 2. Software Introducton 7. System Components 10. Models 3. Real-Tme Models 4. Perodc/Aperodc
Last Time. Priority-based scheduling. Schedulable utilization Rate monotonic rule: Keep utilization below 69% Static priorities Dynamic priorities
Last Tme Prorty-based schedulng Statc prortes Dynamc prortes Schedulable utlzaton Rate monotonc rule: Keep utlzaton below 69% Today Response tme analyss Blockng terms Prorty nverson And solutons Release
Handling Overload (G. Buttazzo, Hard Real-Time Systems, Ch. 9) Causes for Overload
PS-663: Real-Te Systes Handlng Overloads Handlng Overload (G Buttazzo, Hard Real-Te Systes, h 9) auses for Overload Bad syste desgn eg poor estaton of worst-case executon tes Sultaneous arrval of unexpected
Clock-Driven Scheduling (in-depth) Cyclic Schedules: General Structure
CPSC-663: Real-me Systems n-depth Precompute statc schedule o-lne e.g. at desgn tme: can aord expensve algorthms. Idle tmes can be used or aperodc jobs. Possble mplementaton: able-drven Schedulng table
Limited Preemptive Scheduling for Real-Time Systems: a Survey
Lmted Preemptve Schedulng for Real-Tme Systems: a Survey Gorgo C. Buttazzo, Fellow Member, IEEE, Marko Bertogna, Senor Member, IEEE, and Gang Yao Abstract The queston whether preemptve algorthms are better
Improved Worst-Case Response-Time Calculations by Upper-Bound Conditions
Improved Worst-Case Response-Tme Calculatons by Upper-Bound Condtons Vctor Pollex, Steffen Kollmann, Karsten Albers and Frank Slomka Ulm Unversty Insttute of Embedded Systems/Real-Tme Systems {frstname.lastname}@un-ulm.de
Improving the Sensitivity of Deadlines with a Specific Asynchronous Scenario for Harmonic Periodic Tasks scheduled by FP
Improvng the Senstvty of Deadlnes wth a Specfc Asynchronous Scenaro for Harmonc Perodc Tasks scheduled by FP P. Meumeu Yoms, Y. Sorel, D. de Rauglaudre AOSTE Project-team INRIA Pars-Rocquencourt Le Chesnay,
EDF Scheduling for Identical Multiprocessor Systems
EDF Schedulng for dentcal Multprocessor Systems Maro Bertogna Unversty of Modena, taly As Moore s law goes on Number of transstor/chp doubles every 18 to 24 mm heatng becomes a problem Power densty (W/cm
Problem Set 9 Solutions
Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem
Improving the Quality of Control of Periodic Tasks Scheduled by FP with an Asynchronous Approach
Improvng the Qualty of Control of Perodc Tasks Scheduled by FP wth an Asynchronous Approach P. Meumeu Yoms, L. George, Y. Sorel, D. de Rauglaudre AOSTE Project-team INRIA Pars-Rocquencourt Le Chesnay,
Two Methods to Release a New Real-time Task
Two Methods to Release a New Real-tme Task Abstract Guangmng Qan 1, Xanghua Chen 2 College of Mathematcs and Computer Scence Hunan Normal Unversty Changsha, 410081, Chna qqyy@hunnu.edu.cn Gang Yao 3 Sebel
Critical sections. Using semaphores. Using semaphores. Using semaphores. How long is blocking time? 17/10/2016. Problems caused by mutual exclusion
rtcal sectons Problems caused by mutual excluson crtcal secton wat(s) x = ; y = 5; sgnal(s) wrte global memory buffer nt x; nt y; read wat(s) a = x+; b = y+; c = x+y; crtcal secton sgnal(s) Usng semaphores
Overhead-Aware Compositional Analysis of Real-Time Systems
Overhead-Aware ompostonal Analyss of Real-Tme Systems Lnh T.X. Phan, Meng Xu, Jaewoo Lee, nsup Lee, Oleg Sokolsky PRESE enter Department of omputer and nformaton Scence Unversty of Pennsylvana ompostonal
AN EXTENDIBLE APPROACH FOR ANALYSING FIXED PRIORITY HARD REAL-TIME TASKS
AN EXENDIBLE APPROACH FOR ANALYSING FIXED PRIORIY HARD REAL-IME ASKS K. W. ndell 1 Department of Computer Scence, Unversty of York, England YO1 5DD ABSRAC As the real-tme computng ndustry moves away from
Quantifying the Sub-optimality of Uniprocessor Fixed Priority Non-Pre-emptive Scheduling
Quantfyng the Sub-optmalty of Unprocessor Fxed Prorty Non-Pre-emptve Schedulng Robert I Davs Real-Tme Systems Research Group, Department of Computer Scence, Unversty of York, York, UK robdavs@csyorkacuk
Quantifying the Sub-optimality of Uniprocessor Fixed Priority Pre-emptive Scheduling for Sporadic Tasksets with Arbitrary Deadlines
Quantfyng the Sub-optmalty of Unprocessor Fxed Prorty Pre-emptve Schedulng for Sporadc Tasksets wth Arbtrary Deadlnes Robert Davs, Sanjoy Baruah, Thomas Rothvoss, Alan Burns To cte ths verson: Robert Davs,
EEL 6266 Power System Operation and Control. Chapter 3 Economic Dispatch Using Dynamic Programming
EEL 6266 Power System Operaton and Control Chapter 3 Economc Dspatch Usng Dynamc Programmng Pecewse Lnear Cost Functons Common practce many utltes prefer to represent ther generator cost functons as sngle-
Lecture 4. Instructor: Haipeng Luo
Lecture 4 Instructor: Hapeng Luo In the followng lectures, we focus on the expert problem and study more adaptve algorthms. Although Hedge s proven to be worst-case optmal, one may wonder how well t would
Fixed-Priority Multiprocessor Scheduling with Liu & Layland s Utilization Bound
Fxed-Prorty Multprocessor Schedulng wth Lu & Layland s Utlzaton Bound Nan Guan, Martn Stgge, Wang Y and Ge Yu Department of Informaton Technology, Uppsala Unversty, Sweden Department of Computer Scence
Scheduling Motivation
76 eal-me & Embedded Systems 7 Uwe. Zmmer - he Australan Natonal Unversty 78 Motvaton n eal-me Systems Concurrency may lead to non-determnsm. Non-determnsm may make t harder to predct the tmng behavour.
Parametric Utilization Bounds for Fixed-Priority Multiprocessor Scheduling
2012 IEEE 26th Internatonal Parallel and Dstrbuted Processng Symposum Parametrc Utlzaton Bounds for Fxed-Prorty Multprocessor Schedulng Nan Guan 1,2, Martn Stgge 1, Wang Y 1,2 and Ge Yu 2 1 Uppsala Unversty,
Procrastination Scheduling for Fixed-Priority Tasks with Preemption Thresholds
Procrastnaton Schedulng for Fxed-Prorty Tasks wth Preempton Thresholds XaoChuan He, Yan Ja Insttute of Network Technology and Informaton Securty School of Computer Scence Natonal Unversty of Defense Technology
Energy-Efficient Scheduling Fixed-Priority tasks with Preemption Thresholds on Variable Voltage Processors
Energy-Effcent Schedulng Fxed-Prorty tasks wth Preempton Thresholds on Varable Voltage Processors XaoChuan He, Yan Ja Insttute of Network Technology and Informaton Securty School of Computer Scence Natonal
Structure and Drive Paul A. Jensen Copyright July 20, 2003
Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.
Winter 2008 CS567 Stochastic Linear/Integer Programming Guest Lecturer: Xu, Huan
Wnter 2008 CS567 Stochastc Lnear/Integer Programmng Guest Lecturer: Xu, Huan Class 2: More Modelng Examples 1 Capacty Expanson Capacty expanson models optmal choces of the tmng and levels of nvestments
Schedulability Analysis of Task Sets with Upper- and Lower-Bound Temporal Constraints
Schedulablty Analyss of Task Sets wth Upper- and Lower-Bound Temporal Constrants The MIT Faculty has made ths artcle openly avalable. Please share how ths access benefts you. Your story matters. Ctaton
Assortment Optimization under MNL
Assortment Optmzaton under MNL Haotan Song Aprl 30, 2017 1 Introducton The assortment optmzaton problem ams to fnd the revenue-maxmzng assortment of products to offer when the prces of products are fxed.
Fixed-Priority Multiprocessor Scheduling with Liu & Layland s Utilization Bound
Fxed-Prorty Multprocessor Schedulng wth Lu & Layland s Utlzaton Bound Nan Guan, Martn Stgge, Wang Y and Ge Yu Department of Informaton Technology, Uppsala Unversty, Sweden Department of Computer Scence
Resource Sharing. CSCE 990: Real-Time Systems. Steve Goddard. Resources & Resource Access Control (Chapter 8 of Liu)
CSCE 990: Real-Tme Systems Resource Sharng Steve Goddard goddard@cse.unl.edu http://www.cse.unl.edu/~goddard/courses/realtmesystems Resources & Resource Access Control (Chapter 8 of Lu) Real-Tme Systems
Keynote: RTNS Getting ones priorities right
Keynote: RTNS 2012 Gettng ones prortes rght Robert Davs Real-Tme Systems Research Group, Unversty of York rob.davs@york.ac.uk What s ths talk about? Fxed Prorty schedulng n all ts guses Pre-emptve, non-pre-emptve,
Module 9. Lecture 6. Duality in Assignment Problems
Module 9 1 Lecture 6 Dualty n Assgnment Problems In ths lecture we attempt to answer few other mportant questons posed n earler lecture for (AP) and see how some of them can be explaned through the concept
Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]
![Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]](/thumbs/81/84170417.jpg "Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]")
DYNAMIC SHORTEST PATH SEARCH AND SYNCHRONIZED TASK SWITCHING Jay Wagenpfel, Adran Trachte 2 Outlne Shortest Communcaton Path Searchng Bellmann Ford algorthm Algorthm for dynamc case Modfcatons to our algorthm
Efficient Feasibility Analysis for Real-Time Systems with EDF scheduling*
Effcent Feasblty Analyss for Real-Tme Systems wth EF schedulng* Karsten Albers, Frank Slomka epartment of omputer Scence Unversty of Oldenburg Ammerländer Heerstraße 114-118 26111 Oldenburg, Germany {albers,
Lecture 4: November 17, Part 1 Single Buffer Management
Lecturer: Ad Rosén Algorthms for the anagement of Networs Fall 2003-2004 Lecture 4: November 7, 2003 Scrbe: Guy Grebla Part Sngle Buffer anagement In the prevous lecture we taled about the Combned Input
find (x): given element x, return the canonical element of the set containing x;
COS 43 Sprng, 009 Dsjont Set Unon Problem: Mantan a collecton of dsjont sets. Two operatons: fnd the set contanng a gven element; unte two sets nto one (destructvely). Approach: Canoncal element method:
NP-Completeness : Proofs
NP-Completeness : Proofs Proof Methods A method to show a decson problem Π NP-complete s as follows. (1) Show Π NP. (2) Choose an NP-complete problem Π. (3) Show Π Π. A method to show an optmzaton problem
Worst-case response time analysis of real-time tasks under fixed-priority scheduling with deferred preemption
Real-Tme Syst (2009) 42: 63 119 DOI 10.1007/s11241-009-9071-z Worst-case response tme analyss of real-tme tasks under fxed-prorty schedulng wth deferred preempton Render J. Brl Johan J. Lukken Wm F.J.
On the Scheduling of Mixed-Criticality Real-Time Task Sets
On the Schedulng of Mxed-Crtcalty Real-Tme Task Sets Donso de Nz, Karthk Lakshmanan, and Ragunathan (Raj) Rajkumar Carnege Mellon Unversty, Pttsburgh, PA - 15232 Abstract The functonal consoldaton nduced
The Schedulability Region of Two-Level Mixed-Criticality Systems based on EDF-VD
The Schedulablty Regon of Two-Level Mxed-Crtcalty Systems based on EDF-VD Drk Müller and Alejandro Masrur Department of Computer Scence TU Chemntz, Germany Abstract The algorthm Earlest Deadlne Frst wth
Single-Facility Scheduling over Long Time Horizons by Logic-based Benders Decomposition
Sngle-Faclty Schedulng over Long Tme Horzons by Logc-based Benders Decomposton Elvn Coban and J. N. Hooker Tepper School of Busness, Carnege Mellon Unversty ecoban@andrew.cmu.edu, john@hooker.tepper.cmu.edu
CHAPTER 17 Amortized Analysis
CHAPTER 7 Amortzed Analyss In an amortzed analyss, the tme requred to perform a sequence of data structure operatons s averaged over all the operatons performed. It can be used to show that the average
Improvements in the configuration of Posix b scheduling
Improvements n the confguraton of Posx 1003.1b schedulng Matheu Grener, Ncolas Navet To cte ths verson: Matheu Grener, Ncolas Navet. Improvements n the confguraton of Posx 1003.1b schedulng. Ncolas Navet
Resource Allocation with a Budget Constraint for Computing Independent Tasks in the Cloud
Resource Allocaton wth a Budget Constrant for Computng Independent Tasks n the Cloud Wemng Sh and Bo Hong School of Electrcal and Computer Engneerng Georga Insttute of Technology, USA 2nd IEEE Internatonal
On Energy-Optimal Voltage Scheduling for Fixed-Priority Hard Real-Time Systems
On Energy-Optmal Voltage Schedulng for Fxed-Prorty Hard Real-Tme Systems HAN-SAEM YUN and JIHONG KIM Seoul Natonal Unversty We address the problem of energy-optmal voltage schedulng for fxed-prorty hard
Computer Control: Task Synchronisation in Dynamic Priority Scheduling
Computer Control: Task Synchronsaton n Dynamc Prorty Schedulng Sérgo Adrano Fernandes Lopes Department of Industral Electroncs Engneerng School Unversty of Mnho Campus de Azurém 4800 Gumarães - PORTUGAL
Simultaneous Optimization of Berth Allocation, Quay Crane Assignment and Quay Crane Scheduling Problems in Container Terminals
Smultaneous Optmzaton of Berth Allocaton, Quay Crane Assgnment and Quay Crane Schedulng Problems n Contaner Termnals Necat Aras, Yavuz Türkoğulları, Z. Caner Taşkın, Kuban Altınel Abstract In ths work,
Kernel Methods and SVMs Extension
Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general
Appendix B: Resampling Algorithms
407 Appendx B: Resamplng Algorthms A common problem of all partcle flters s the degeneracy of weghts, whch conssts of the unbounded ncrease of the varance of the mportance weghts ω [ ] of the partcles
Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems
Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons
princeton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg
prnceton unv. F 17 cos 521: Advanced Algorthm Desgn Lecture 7: LP Dualty Lecturer: Matt Wenberg Scrbe: LP Dualty s an extremely useful tool for analyzng structural propertes of lnear programs. Whle there
Synchronization Protocols. Task Allocation Bin-Packing Heuristics: First-Fit Subtasks assigned in arbitrary order To allocate a new subtask T i,j
End-to-End Schedulng Framework 1. Tak allocaton: bnd tak to proceor 2. Synchronzaton protocol: enforce precedence contrant 3. Subdeadlne agnment 4. Schedulablty analy Tak Allocaton Bn-Packng eurtc: Frt-Ft
Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity
Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle
Partitioned EDF Scheduling in Multicore systems with Quality of Service constraints
Parttoned EDF Schedulng n Multcore systems wth Qualty of Servce constrants Nadne Abdallah, Audrey Queudet, Marylne Chetto, Rafc Hage Chehade To cte ths verson: Nadne Abdallah, Audrey Queudet, Marylne Chetto,
Lecture 12: Classification
Lecture : Classfcaton g Dscrmnant functons g The optmal Bayes classfer g Quadratc classfers g Eucldean and Mahalanobs metrcs g K Nearest Neghbor Classfers Intellgent Sensor Systems Rcardo Guterrez-Osuna
Randomness and Computation
Randomness and Computaton or, Randomzed Algorthms Mary Cryan School of Informatcs Unversty of Ednburgh RC 208/9) Lecture 0 slde Balls n Bns m balls, n bns, and balls thrown unformly at random nto bns usually
Amiri s Supply Chain Model. System Engineering b Department of Mathematics and Statistics c Odette School of Business
Amr s Supply Chan Model by S. Ashtab a,, R.J. Caron b E. Selvarajah c a Department of Industral Manufacturng System Engneerng b Department of Mathematcs Statstcs c Odette School of Busness Unversty of
THE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens
THE CHINESE REMAINDER THEOREM KEITH CONRAD We should thank the Chnese for ther wonderful remander theorem. Glenn Stevens 1. Introducton The Chnese remander theorem says we can unquely solve any par of
Utilization-Based Scheduling of Flexible Mixed-Criticality Real-Time Tasks
1 Utlzaton-Based Schedulng of Flexble Mxed-Crtcalty Real-Tme Tasks Gang Chen, Nan Guan, D Lu, Qngqang He, Ka Huang, Todor Stefanov, Wang Y arxv:1711.00100v1 [cs.dc] 29 Sep 2017 Abstract Mxed-crtcalty models
Combining Constraint Programming and Integer Programming
Combnng Constrant Programmng and Integer Programmng GLOBAL CONSTRAINT OPTIMIZATION COMPONENT Specal Purpose Algorthm mn c T x +(x- 0 ) x( + ()) =1 x( - ()) =1 FILTERING ALGORITHM COST-BASED FILTERING ALGORITHM
Resource-Efficient Scheduling Of Multiprocessor Mixed-Criticality Real-Time Systems
Unversty of Pennsylvana ScholarlyCommons Publcly Accessble Penn Dssertatons 2017 Resource-Effcent Schedulng Of Multprocessor Mxed-Crtcalty Real-Tme Systems Jaewoo Lee Unversty of Pennsylvana, jaewoo@cs.upenn.edu
Parallel Real-Time Scheduling of DAGs
Washngton Unversty n St. Lous Washngton Unversty Open Scholarshp All Computer Scence and Engneerng Research Computer Scence and Engneerng Report Number: WUCSE-013-5 013 Parallel Real-Tme Schedulng of DAGs
Global EDF Scheduling for Parallel Real-Time Tasks
Washngton Unversty n St. Lous Washngton Unversty Open Scholarshp Engneerng and Appled Scence Theses & Dssertatons Engneerng and Appled Scence Sprng 5-15-2014 Global EDF Schedulng for Parallel Real-Tme
Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system
Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng
Calculation of time complexity (3%)
Problem 1. (30%) Calculaton of tme complexty (3%) Gven n ctes, usng exhaust search to see every result takes O(n!). Calculaton of tme needed to solve the problem (2%) 40 ctes:40! dfferent tours 40 add
Markov Chain Monte Carlo (MCMC), Gibbs Sampling, Metropolis Algorithms, and Simulated Annealing Bioinformatics Course Supplement
Markov Chan Monte Carlo MCMC, Gbbs Samplng, Metropols Algorthms, and Smulated Annealng 2001 Bonformatcs Course Supplement SNU Bontellgence Lab http://bsnuackr/ Outlne! Markov Chan Monte Carlo MCMC! Metropols-Hastngs
a b a In case b 0, a being divisible by b is the same as to say that
Secton 6.2 Dvsblty among the ntegers An nteger a ε s dvsble by b ε f there s an nteger c ε such that a = bc. Note that s dvsble by any nteger b, snce = b. On the other hand, a s dvsble by only f a = :
Instantaneous Utilization Based Scheduling Algorithms for Real Time Systems Radhakrishna Naik 1, R.R.Manthalkar 2 Pune University 1, SGGS Nanded 2
Radhakrshna Nak et al, / (IJSIT) Internatonal Journal of omputer Scence and Informaton Technologes, Vol. (),, 654-66 Instantaneous tlzaton Based Schedulng Algorthms for Real Tme Systems Radhakrshna Nak,
Delay-Aware Period Assignment in Control Systems
Delay-Aware Perod Assgnment n Control Systems Bn, Enrco; Cervn, Anton Publshed: 28-- Lnk to publcaton Ctaton for publshed verson (APA): Bn, E., & Cervn, A. (28). Delay-Aware Perod Assgnment n Control Systems.
Quantifying the Exact Sub-Optimality of Non-Preemptive Scheduling
Quantfyng the Exact Sub-Optmalty of Non-Preemptve Schedulng Robert I. Davs 1, Abhlash Thekklakattl 2, Olver Gettngs 1, Radu Dobrn 2, and Saskumar Punnekkat 2 1 Real-Tme Systems Research Group, Unversty
College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009
College of Computer & Informaton Scence Fall 2009 Northeastern Unversty 20 October 2009 CS7880: Algorthmc Power Tools Scrbe: Jan Wen and Laura Poplawsk Lecture Outlne: Prmal-dual schema Network Desgn:
Lecture Randomized Load Balancing strategies and their analysis. Probability concepts include, counting, the union bound, and Chernoff bounds.
U.C. Berkeley CS273: Parallel and Dstrbuted Theory Lecture 1 Professor Satsh Rao August 26, 2010 Lecturer: Satsh Rao Last revsed September 2, 2010 Lecture 1 1 Course Outlne We wll cover a samplng of the
Partitioned Scheduling of Multi-Modal Mixed- Criticality Real-Time Systems on Multiprocessor Platforms
Unversty of Pennsylvana ScholarlyCommons Departmental Papers (CIS) Department of Computer & Informaton Scence 4-2014 Parttoned Schedulng of Mult-Modal Mxed- Crtcalty Real-Tme Systems on Multprocessor Platforms
jitter Abstract Output jitter the variation in the inter-completion times of successive jobs of the same
Schedulng perodc task systems to mnmze output jtter Sanjoy Baruah Gorgo Buttazzo y Sergey Gornsky z Guseppe Lpar y Abstract Output jtter the varaton n the nter-completon tmes of successve jobs of the same
Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:
Computing Correlated Equilibria in Multi-Player Games
Computng Correlated Equlbra n Mult-Player Games Chrstos H. Papadmtrou Presented by Zhanxang Huang December 7th, 2005 1 The Author Dr. Chrstos H. Papadmtrou CS professor at UC Berkley (taught at Harvard,
Offline Equivalence: A Non-Preemptive Scheduling Technique for Resource-Constrained Embedded Real-Time Systems
Offlne Equvalence: A Non-Preemptve Schedulng Technque for Resource-Constraned Embedded Real-Tme Systems Mtra Nasr Björn B. Brandenburg Max Planck Insttute for Software Systems (MPI-SWS) Abstract We consder
Queueing Networks II Network Performance
Queueng Networks II Network Performance Davd Tpper Assocate Professor Graduate Telecommuncatons and Networkng Program Unversty of Pttsburgh Sldes 6 Networks of Queues Many communcaton systems must be modeled
PreDVS: Preemptive Dynamic Voltage Scaling for Real-time Systems using Approximation Scheme
PreDVS: Preemptve Dynamc Voltage Scalng for Real-tme Systems usng Approxmaton Scheme Wexun Wang and Prabhat Mshra Department of Computer and Informaton Scence and Engneerng Unversty of Florda, Ganesvlle,
4DVAR, according to the name, is a four-dimensional variational method.
4D-Varatonal Data Assmlaton (4D-Var) 4DVAR, accordng to the name, s a four-dmensonal varatonal method. 4D-Var s actually a drect generalzaton of 3D-Var to handle observatons that are dstrbuted n tme. The
Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law:
CE304, Sprng 2004 Lecture 4 Introducton to Vapor/Lqud Equlbrum, part 2 Raoult s Law: The smplest model that allows us do VLE calculatons s obtaned when we assume that the vapor phase s an deal gas, and
Energy and Feasibility Optimal Global Scheduling Framework on big.little platforms
Energy and Feasblty Optmal Global Schedulng Framework on bg.little platforms Hoon Sung Chwa, Jaebaek Seo, Hyuck Yoo Jnkyu Lee, Insk Shn Department of Computer Scence, KAIST, Republc of Korea Department
PRIME NUMBER GENERATION BASED ON POCKLINGTON S THEOREM
PRIME NUMBER GENERATION BASED ON POCKLINGTON S THEOREM Alexandros Papankolaou and Song Y. Yan Department of Computer Scence, Aston Unversty, Brmngham B4 7ET, UK 24 October 2000, Receved 26 June 2001 Abstract
MILP-based Deadline Assignment for End-to-End Flows in Distributed Real-Time Systems
MILP-based Deadlne Assgnment for End-to-End Flows n Dstrbuted Real-Tme Systems Bo Peng Department of Computer Scence Wayne State Unversty Detrot, Mchgan, USA et7889@wayne.edu Nathan Fsher Department of
Lab 2e Thermal System Response and Effective Heat Transfer Coefficient
58:080 Expermental Engneerng 1 OBJECTIVE Lab 2e Thermal System Response and Effectve Heat Transfer Coeffcent Warnng: though the experment has educatonal objectves (to learn about bolng heat transfer, etc.),
Parametric fractional imputation for missing data analysis. Jae Kwang Kim Survey Working Group Seminar March 29, 2010
Parametrc fractonal mputaton for mssng data analyss Jae Kwang Km Survey Workng Group Semnar March 29, 2010 1 Outlne Introducton Proposed method Fractonal mputaton Approxmaton Varance estmaton Multple mputaton
CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE
CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng
CHAPTER IV RESEARCH FINDING AND DISCUSSIONS
CHAPTER IV RESEARCH FINDING AND DISCUSSIONS A. Descrpton of Research Fndng. The Implementaton of Learnng Havng ganed the whole needed data, the researcher then dd analyss whch refers to the statstcal data
princeton univ. F 13 cos 521: Advanced Algorithm Design Lecture 3: Large deviations bounds and applications Lecturer: Sanjeev Arora
prnceton unv. F 13 cos 521: Advanced Algorthm Desgn Lecture 3: Large devatons bounds and applcatons Lecturer: Sanjeev Arora Scrbe: Today s topc s devaton bounds: what s the probablty that a random varable
Integrated Scheduling and Synthesis of Control Applications on Distributed Embedded Systems
Integrated Schedulng and Synthess of Control Applcatons on Dstrbuted Embedded Systems Sam, Sohel; Cervn, Anton; Eles, Petru; Peng, Zebo 9 Lnk to publcaton Ctaton for publshed verson (APA): Sam, S., Cervn,
Partitioned Scheduling of Multi-Modal Mixed-Criticality Real-Time Systems on Multiprocessor Platforms
Parttoned Schedulng of Mult-Modal Mxed-Crtcalty Real-Tme Systems on Multprocessor Platforms Donso de Nz SEI, Carnege Mellon Unversty Lnh T.X. Phan Unversty of Pennsylvana Abstract Real-tme systems are
Grover s Algorithm + Quantum Zeno Effect + Vaidman
Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the
A 2D Bounded Linear Program (H,c) 2D Linear Programming
A 2D Bounded Lnear Program (H,c) h 3 v h 8 h 5 c h 4 h h 6 h 7 h 2 2D Lnear Programmng C s a polygonal regon, the ntersecton of n halfplanes. (H, c) s nfeasble, as C s empty. Feasble regon C s unbounded
A Simple Inventory System
A Smple Inventory System Lawrence M. Leems and Stephen K. Park, Dscrete-Event Smulaton: A Frst Course, Prentce Hall, 2006 Hu Chen Computer Scence Vrgna State Unversty Petersburg, Vrgna February 8, 2017
Optimal Static Partition Configuration in ARINC653 System
JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 9, NO. 4, DECEMBER 7 Optmal Statc rtton Confguraton n ARINC6 System Sheng-Ln Gu, Le Luo, Sen-Sen Tang, and Yang Meng Abstract ARINC6 systems, whch have
Resource Reservation for Mixed Criticality Systems
Resource Reservaton for Mxed Crtcalty Systems Guseppe Lpar 1, Gorgo C. Buttazzo 2 1 LSV, ENS - Cachan, France 2 Scuola Superore Sant Anna, Italy Abstract. Ths paper presents a reservaton-based approach
Lecture 3 January 31, 2017
CS 224: Advanced Algorthms Sprng 207 Prof. Jelan Nelson Lecture 3 January 3, 207 Scrbe: Saketh Rama Overvew In the last lecture we covered Y-fast tres and Fuson Trees. In ths lecture we start our dscusson
CS-433: Simulation and Modeling Modeling and Probability Review
CS-433: Smulaton and Modelng Modelng and Probablty Revew Exercse 1. (Probablty of Smple Events) Exercse 1.1 The owner of a camera shop receves a shpment of fve cameras from a camera manufacturer. Unknown
Minimizing Energy Consumption of MPI Programs in Realistic Environment
Mnmzng Energy Consumpton of MPI Programs n Realstc Envronment Amna Guermouche, Ncolas Trquenaux, Benoît Pradelle and Wllam Jalby Unversté de Versalles Sant-Quentn-en-Yvelnes arxv:1502.06733v2 [cs.dc] 25
VQ widely used in coding speech, image, and video
at Scalar quantzers are specal cases of vector quantzers (VQ): they are constraned to look at one sample at a tme (memoryless) VQ does not have such constrant better RD perfomance expected Source codng