Clock-Driven Scheduling (in-depth) Cyclic Schedules: General Structure
|
|
- Augustus Cook
- 5 years ago
- Views:
Transcription
1 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 has entres o type t k, Jt k, where t k : decson tme Jt k : job to start at tme t k Input: Schedule t k, Jt k k 0,,,N- ask Scheduler: : 0; k : 0; <set tmer to expre at tme t 0 > BEGIN LOOP <wat or tmer nterrupt> : +; k: mod N; <set tmer to expre at tme DIV N*H + t k > IF Jt k- s empty HEN wakeupaperodc ELSE wakeupjt k- END LOOP END Scheduler; Cyclc Schedules: General Structure Schedulng decson s made perodcally: rame Schedulng decson s made perodcally: choose whch job to execute perorm montorng and enorcement operatons decson ponts Major Cycle: Frames n a hyperperod. major cycle hyperperod H
2 CPSC-663: Real-me Systems Frame Sze Constrants Frames must be sucently long so that every job can start and complete wthn a sngle rame: max e he hyperperod must have an nteger number o rames: 2 H " dvdes" H For montorng purposes, rames must be sucently small that between release tme and deadlne o every job there s at least one rame: t t t+ t+2 t +D t +p t t' t D t' t gcd p, 2 gcd p, D Frame Szes: Example ask set: p e D 5, 22,, 2, 3, H : H e : 2 gcd p, D 3 2,3,4,5,6,0,.. 2,3,4,5,6 possble values or :3,4,5,6 2
3 CPSC-663: Real-me Systems Slcng and Schedulng Blocks Slcng 2 3 p e D 4,, 4 5, 2, 5 5, ?! slce , 5,, 2,, 3,, schedulng block H Cyclc Executve Input: Stored schedule: Lk or k 0,,,F-; Aperodc job queue. ASK CYCLIC_EXECUIVE: k 0; /* current rame */ BEGIN LOOP accept clock nterrupt at tme k*; IF <the last job s not completed> take acton; CurrentBlock : Lk; k : k+ mod F; IF <any slce n CurrentBlock s not released> take acton; WHILE <CurrentBlock s not empty> execute the rst slce n t; remove the rst slce rom CurrentBlock; END WHILE; WHILE <the aperodc job queue s not empty> execute the rst job n the queue; remove the just completed job; END WHILE; END LOOP; END CYCLIC_EXECUIVE; 3
4 CPSC-663: Real-me Systems What About Aperodc Jobs? ypcally: Scheduled n the background. her executon may be delayed. But: Aperodc jobs are typcally results o external events. hereore: he sooner the completon tme, the more responsve the system Mnmzng response tme o aperodc jobs becomes a desgn ssue. Approach: Execute aperodc jobs ahead o perodc jobs whenever possble. hs s called Slack Stealng. Slack Stealng Lehoczky et al., RSS 87 x k Amount o tme allocated to slces executed durng rame F k. s k Slack durng rame F k : s k : - x k. he cyclc executve can execute aperodc jobs or s k amount o tme wthout causng jobs to mss deadlnes. Example:
5 CPSC-663: Real-me Systems Sporadc Jobs Remnder: Sporadc jobs have hard deadlnes; the release tme and the executon tme are not known a pror. Worst-case executon tme known when job s released. Need acceptance test: Jd,e s c s c+ s l d F c- F c F c+ F l F l+ S c, l : otal amount o slack n Frames F c,, F l. l s c Acceptance est: IF Sc,l < e HEN reject job; ELSE accept job; schedule executon; END; how?! Statc schedulng: Schedulng o Accepted Jobs Schedule as large a slce o the accepted job as possble n the current rame. Schedule remanng portons as late as possble. Mechansm: Append slces o accepted job to lst o perodc-task slces n rames where they are scheduled. Problem: Early commt. Alternatves: Reschedulng upon arrval. Prorty-drven schedulng o sporadc jobs. 5
6 CPSC-663: Real-me Systems EDF-Schedulng o Accepted Jobs perodc tasks 2 3 N... acceptance test reject prorty queue aperodc processor Acceptance est or EDF-Scheduled Sporadc Jobs Sporadc Job J wth deadlne d arrves: est : est whether current amount o slack beore d s enough to accommodate J. I not, reject! est 2: est whether sporadc jobs stll n system wth deadlnes ater d wll mss deadlne J s accepted. I yes, reject! Accept! * Dene SJ : Amount o slack up to tme d ater J has been scheduled. ** Update all SJ wth d > d, that s, such that d > d : S J S J e 6
7 CPSC-663: Real-me Systems Pros and Cons o Pros: Conceptual smplcty mng constrants can be checked and enorced at rame boundares. Preempton cost can be kept small by havng approprate rame szes. Easy to valdate: Executon tmes o slces known a pror. Cons: Dcult to mantan. Does not allow to ntegrate hard and sot deadlnes. 7
Cyclic Schedules: General Structure. Frame Size Constraints
CPSC-663: Real-ime Systems Cyclic Schedules: General Structure Scheduling decision is made periodically: Frame Scheduling decision is made periodically: choose which job to execute perorm monitoring and
More informationLast 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
More informationEmbedded 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
More informationReal-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
More informationReal-Time Operating Systems M. 11. Real-Time: Periodic Task Scheduling
Real-Tme Operatng Systems M 11. Real-Tme: Perodc Task Schedulng Notce The course materal ncludes sldes downloaded from:! http://codex.cs.yale.edu/av/os-book/! and! (sldes by Slberschatz, Galvn, and Gagne,
More informationAN 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
More informationClock-driven scheduling
Clock-driven scheduling Also known as static or off-line scheduling Michal Sojka Czech Technical University in Prague, Faculty of Electrical Engineering, Department of Control Engineering November 8, 2017
More informationSynchronization 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
More informationTwo 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
More informationThere are three priority driven approaches that we will look at
Priority Driven Approaches There are three priority driven approaches that we will look at Earliest-Deadline-First (EDF) Least-Slack-Time-first (LST) Latest-Release-Time-first (LRT) 1 EDF Earliest deadline
More informationOverhead-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
More informationEDF 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
More informationFinite Difference Method
7/0/07 Instructor r. Ramond Rump (9) 747 698 rcrump@utep.edu EE 337 Computatonal Electromagnetcs (CEM) Lecture #0 Fnte erence Method Lecture 0 These notes ma contan coprghted materal obtaned under ar use
More informationSingle-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
More informationFixed-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
More informationFixed-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
More informationAnalysis of Discrete Time Queues (Section 4.6)
Analyss of Dscrete Tme Queues (Secton 4.6) Copyrght 2002, Sanjay K. Bose Tme axs dvded nto slots slot slot boundares Arrvals can only occur at slot boundares Servce to a job can only start at a slot boundary
More informationScheduling 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.
More informationGlobal 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
More informationReal-Time and Embedded Systems (M) Lecture 5
Priority-driven Scheduling of Periodic Tasks (1) Real-Time and Embedded Systems (M) Lecture 5 Lecture Outline Assumptions Fixed-priority algorithms Rate monotonic Deadline monotonic Dynamic-priority algorithms
More informationOffline 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
More informationmultiprogrammed, hard real-time environments Giuseppe Lipari John Carpenter Sanjoy Baruah particular server.
A framework for achevng nter-applcaton solaton n multprogrammed, hard real-tme envronments Guseppe Lpar John Carpenter Sanjoy Baruah Abstract A framework for schedulng a number of derent real-tme applcatons
More informationProblem Set 9 - Solutions Due: April 27, 2005
Problem Set - Solutons Due: Aprl 27, 2005. (a) Frst note that spam messages, nvtatons and other e-mal are all ndependent Posson processes, at rates pλ, qλ, and ( p q)λ. The event of the tme T at whch you
More informationLimited 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
More informationImproved 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
More informationTwo-Phase Low-Energy N-Modular Redundancy for Hard Real-Time Multi-Core Systems
1 Two-Phase Low-Energy N-Modular Redundancy for Hard Real-Tme Mult-Core Systems Mohammad Saleh, Alreza Ejlal, and Bashr M. Al-Hashm, Fellow, IEEE Abstract Ths paper proposes an N-modular redundancy (NMR)
More informationPartitioned Mixed-Criticality Scheduling on Multiprocessor Platforms
Parttoned Mxed-Crtcalty Schedulng on Multprocessor Platforms Chuanca Gu 1, Nan Guan 1,2, Qngxu Deng 1 and Wang Y 1,2 1 Northeastern Unversty, Chna 2 Uppsala Unversty, Sweden Abstract Schedulng mxed-crtcalty
More informationLecture 2 Solution of Nonlinear Equations ( Root Finding Problems )
Lecture Soluton o Nonlnear Equatons Root Fndng Problems Dentons Classcaton o Methods Analytcal Solutons Graphcal Methods Numercal Methods Bracketng Methods Open Methods Convergence Notatons Root Fndng
More informationEEL 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-
More informationA NEW HYBRID RESCHEDULING POLICY BASED ON CUMULATIVE DELAY
A NEW HYBRID RESCHEDULING POLICY BASED ON CUULATIVE DELAY Haruhko Suwa, Toshhsa Fujwara Department of Industral and Systems Engneerng, Setsunan Unversty 17-8, Ikedanaka-mach, Neyagawa, Osaka 5728508, JAPAN
More information3. Scheduling issues. Common approaches 3. Common approaches 1. Preemption vs. non preemption. Common approaches 2. Further definitions
Common approaches 3 3. Scheduling issues Priority-driven (event-driven) scheduling This class of algorithms is greedy They never leave available processing resources unutilized An available resource may
More informationComputer 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
More informationCritical 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
More informationOn the Throughput of Clustered Photolithography Tools:
On the hroughput of lustered Photolthography ools: Wafer Advancement and Intrnsc Equpment Loss Maruth Kumar Mutnur James R. Morrson, Ph.D. September 23, 2007 Presentaton Outlne Motvaton Model : Synchronous
More informationCS 331 DESIGN AND ANALYSIS OF ALGORITHMS DYNAMIC PROGRAMMING. Dr. Daisy Tang
CS DESIGN ND NLYSIS OF LGORITHMS DYNMIC PROGRMMING Dr. Dasy Tang Dynamc Programmng Idea: Problems can be dvded nto stages Soluton s a sequence o decsons and the decson at the current stage s based on the
More informationLecture 6. Real-Time Systems. Dynamic Priority Scheduling
Real-Time Systems Lecture 6 Dynamic Priority Scheduling Online scheduling with dynamic priorities: Earliest Deadline First scheduling CPU utilization bound Optimality and comparison with RM: Schedulability
More information: Numerical Analysis Topic 2: Solution of Nonlinear Equations Lectures 5-11:
764: Numercal Analyss Topc : Soluton o Nonlnear Equatons Lectures 5-: UIN Malang Read Chapters 5 and 6 o the tetbook 764_Topc Lecture 5 Soluton o Nonlnear Equatons Root Fndng Problems Dentons Classcaton
More informationKeynote: 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,
More informationLecture 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
More informationCalculation 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
More informationAppendix 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
More informationQuantifying 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,
More informationPriority-driven Scheduling of Periodic Tasks (1) Advanced Operating Systems (M) Lecture 4
Priority-driven Scheduling of Periodic Tasks (1) Advanced Operating Systems (M) Lecture 4 Priority-driven Scheduling Assign priorities to jobs, based on their deadline or other timing constraint Make scheduling
More informationUncertain Models for Bed Allocation
www.ccsenet.org/ghs Global Journal of Health Scence Vol., No. ; October 00 Uncertan Models for Bed Allocaton Lng Gao (Correspondng author) College of Scence, Guln Unversty of Technology Box 733, Guln 54004,
More informationQuantifying 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
More informationWorst Case Interrupt Response Time Draft, Fall 2007
Worst Case Interrupt esponse Te Draft, Fall 7 Phlp Koopan Carnege Mellon Unversty Copyrght 7, Phlp Koopan eproducton and dssenaton beyond students of CMU ECE 8-348 s prohbted.. Overvew: Interrupt Servce
More informationHandling 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
More informationPartitioned 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
More informationLinear Momentum. Equation 1
Lnear Momentum OBJECTIVE Obsere collsons between two carts, testng or the conseraton o momentum. Measure energy changes durng derent types o collsons. Classy collsons as elastc, nelastc, or completely
More informationReal-time Scheduling of Periodic Tasks (2) Advanced Operating Systems Lecture 3
Real-time Scheduling of Periodic Tasks (2) Advanced Operating Systems Lecture 3 Lecture Outline The rate monotonic algorithm (cont d) Maximum utilisation test The deadline monotonic algorithm The earliest
More informationCHAPTER 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
More informationResource 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
More informationModeling motion with VPython Every program that models the motion of physical objects has two main parts:
1 Modelng moton wth VPython Eery program that models the moton o physcal objects has two man parts: 1. Beore the loop: The rst part o the program tells the computer to: a. Create numercal alues or constants
More informationPartitioned 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
More information= z 20 z n. (k 20) + 4 z k = 4
Problem Set #7 solutons 7.2.. (a Fnd the coeffcent of z k n (z + z 5 + z 6 + z 7 + 5, k 20. We use the known seres expanson ( n+l ( z l l z n below: (z + z 5 + z 6 + z 7 + 5 (z 5 ( + z + z 2 + z + 5 5
More informationNP-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
More informationTOPICS MULTIPLIERLESS FILTER DESIGN ELEMENTARY SCHOOL ALGORITHM MULTIPLICATION
1 2 MULTIPLIERLESS FILTER DESIGN Realzaton of flters wthout full-fledged multplers Some sldes based on support materal by W. Wolf for hs book Modern VLSI Desgn, 3 rd edton. Partly based on followng papers:
More informationReal-Time Scheduling
1 Real-Time Scheduling Formal Model [Some parts of this lecture are based on a real-time systems course of Colin Perkins http://csperkins.org/teaching/rtes/index.html] Real-Time Scheduling Formal Model
More informationA FAST HEURISTIC FOR TASKS ASSIGNMENT IN MANYCORE SYSTEMS WITH VOLTAGE-FREQUENCY ISLANDS
Shervn Haamn A FAST HEURISTIC FOR TASKS ASSIGNMENT IN MANYCORE SYSTEMS WITH VOLTAGE-FREQUENCY ISLANDS INTRODUCTION Increasng computatons n applcatons has led to faster processng. o Use more cores n a chp
More informationPHYS 1101 Practice problem set 12, Chapter 32: 21, 22, 24, 57, 61, 83 Chapter 33: 7, 12, 32, 38, 44, 49, 76
PHYS 1101 Practce problem set 1, Chapter 3: 1,, 4, 57, 61, 83 Chapter 33: 7, 1, 3, 38, 44, 49, 76 3.1. Vsualze: Please reer to Fgure Ex3.1. Solve: Because B s n the same drecton as the ntegraton path s
More informationQueuing system theory
Elements of queung system: Queung system theory Every queung system conssts of three elements: An arrval process: s characterzed by the dstrbuton of tme between the arrval of successve customers, the mean
More informationResource 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
More informationEnergy 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
More informationInternational Mathematical Olympiad. Preliminary Selection Contest 2012 Hong Kong. Outline of Solutions
Internatonal Mathematcal Olympad Prelmnary Selecton ontest Hong Kong Outlne of Solutons nswers: 7 4 7 4 6 5 9 6 99 7 6 6 9 5544 49 5 7 4 6765 5 6 6 7 6 944 9 Solutons: Snce n s a two-dgt number, we have
More informationA 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
More informationProcrastination 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
More informationUsing non-preemptive regions and path modification to improve schedulability of real-time traffic over priority-based NoCs
Real-Tme Syst (2017) 53:886 915 DOI 10.1007/s11241-017-9276-5 Usng non-preemptve regons and path modfcaton to mprove schedulablty of real-tme traffc over prorty-based NoCs Meng Lu 1 Matthas Becker 1 Mors
More informationEnergy-Efficient Thermal-Aware Scheduling for RT Tasks Using T CP N
Energy-Effcent Thermal-Aware Schedulng for RT Tasks Usng T CP N L. Rubo-Anguano G. Desrena-López A. Ramírez-Trevño J.L. Brz CINVESTAV-IPN Undad Guadalajara, Av. del Bosque 11, CP 19, Zapopan, Jalsco, Mexco
More informationCS 3750 Machine Learning Lecture 6. Monte Carlo methods. CS 3750 Advanced Machine Learning. Markov chain Monte Carlo
CS 3750 Machne Learnng Lectre 6 Monte Carlo methods Mlos Haskrecht mlos@cs.ptt.ed 5329 Sennott Sqare Markov chan Monte Carlo Importance samplng: samples are generated accordng to Q and every sample from
More informationNonlinear Classifiers II
Nonlnear Classfers II Nonlnear Classfers: Introducton Classfers Supervsed Classfers Lnear Classfers Perceptron Least Squares Methods Lnear Support Vector Machne Nonlnear Classfers Part I: Mult Layer Neural
More informationVariability-Driven Module Selection with Joint Design Time Optimization and Post-Silicon Tuning
Asa and South Pacfc Desgn Automaton Conference 2008 Varablty-Drven Module Selecton wth Jont Desgn Tme Optmzaton and Post-Slcon Tunng Feng Wang, Xaoxa Wu, Yuan Xe The Pennsylvana State Unversty Department
More informationSchedulability 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
More informationKernel 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
More informationAlgorithm Design and Analysis
Algorithm Design and Analysis LECTURE 6 Greedy Algorithms Interval Scheduling Interval Partitioning Scheduling to Minimize Lateness Sofya Raskhodnikova S. Raskhodnikova; based on slides by E. Demaine,
More informationEnergy-Aware Fault Tolerance in Fixed-Priority Real-Time Embedded Systems*
Energy-Aware Fault Tolerance n Fxed-Prorty Real-Tme Embedded Systems Yng Zang, Krsnendu Cakrabarty and Vsnu Swamnatan Department of Electrcal & Computer Engneerng Duke Unversty, Duram, NC 778, USA Abstract
More informationfind (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:
More informationInstantaneous 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,
More informationTask Scheduling with Self-Suspensions in Soft Real-Time Multiprocessor Systems
ask Schedulng wth Self-Suspensons n Soft Real-me Multprocessor Systems Cong Lu and James H. Anderson Department of Computer Scence, Unversty of North Carolna at Chapel Hll Abstract Job release Job deadlne
More informationImproving 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,
More informationParametric 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,
More informationPredictable Execution Model: Concept and Implementation
Predctable Executon Model: Concept and Implementaton Rodolfo Pellzzon, Emlano Bett, Stanley Bak, Gang Yao, John Crswell and Marco Caccamo Unversty of Illnos at Urbana-Champagn, IL, USA, {rpellz2, ebett,
More informationNon-Preemptive and Limited Preemptive Scheduling. LS 12, TU Dortmund
Non-Preemptive and Limited Preemptive Scheduling LS 12, TU Dortmund 09 May 2017 (LS 12, TU Dortmund) 1 / 31 Outline Non-Preemptive Scheduling A General View Exact Schedulability Test Pessimistic Schedulability
More informationLecture 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
More informationCS : Algorithms and Uncertainty Lecture 17 Date: October 26, 2016
CS 29-128: Algorthms and Uncertanty Lecture 17 Date: October 26, 2016 Instructor: Nkhl Bansal Scrbe: Mchael Denns 1 Introducton In ths lecture we wll be lookng nto the secretary problem, and an nterestng
More informationCase A. P k = Ni ( 2L i k 1 ) + (# big cells) 10d 2 P k.
THE CELLULAR METHOD In ths lecture, we ntroduce the cellular method as an approach to ncdence geometry theorems lke the Szemeréd-Trotter theorem. The method was ntroduced n the paper Combnatoral complexty
More informationCOS 511: Theoretical Machine Learning. Lecturer: Rob Schapire Lecture # 15 Scribe: Jieming Mao April 1, 2013
COS 511: heoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 15 Scrbe: Jemng Mao Aprl 1, 013 1 Bref revew 1.1 Learnng wth expert advce Last tme, we started to talk about learnng wth expert advce.
More informationLecture 14: Bandits with Budget Constraints
IEOR 8100-001: Learnng and Optmzaton for Sequental Decson Makng 03/07/16 Lecture 14: andts wth udget Constrants Instructor: Shpra Agrawal Scrbed by: Zhpeng Lu 1 Problem defnton In the regular Mult-armed
More informationProf. Paolo Colantonio a.a
Pro. Paolo olantono a.a. 3 4 Let s consder a two ports network o Two ports Network o L For passve network (.e. wthout nternal sources or actve devces), a general representaton can be made by a sutable
More informationSimultaneous 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,
More informationONE-DIMENSIONAL COLLISIONS
Purpose Theory ONE-DIMENSIONAL COLLISIONS a. To very the law o conservaton o lnear momentum n one-dmensonal collsons. b. To study conservaton o energy and lnear momentum n both elastc and nelastc onedmensonal
More informationTowards Minimizing Processes Response Time in Interactive Systems
Internatonal Journal of Computer Scence and Informaton Technology Research (IJCSITR) Vol. 1, Issue 1, pp: (65-73), Month: October-December 2013, Avalable at: www.researchpublsh.com Towards Mnmzng Processes
More informationThis is the Pre-Published Version.
Ths s the Pre-Publshed Verson. Abstract In ths paper we consder the problem of schedulng obs wth equal processng tmes on a sngle batch processng machne so as to mnmze a prmary and a secondary crtera. We
More informationLoop-independent dependence: dependence exists within an iteration; i.e., if the loop is removed, the dependence still exists.
Loop-depedet vs. loop-carred depedeces [ 3.] Loop-carred depedece: depedece exsts across teratos;.e., f the loop s removed, the depedece o loger exsts. Loop-depedet depedece: depedece exsts wth a terato;.e.,
More informationModule 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
More informationA 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
More informationWorst-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.
More informationStructure 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.
More informationExperiment 5 Elastic and Inelastic Collisions
PHY191 Experment 5: Elastc and Inelastc Collsons 7/1/011 Page 1 Experment 5 Elastc and Inelastc Collsons Readng: Bauer&Westall: Chapter 7 (and 8, or center o mass deas) as needed Homework 5: turn n the
More informationImproving 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,
More informationExample: (13320, 22140) =? Solution #1: The divisors of are 1, 2, 3, 4, 5, 6, 9, 10, 12, 15, 18, 20, 27, 30, 36, 41,
The greatest common dvsor of two ntegers a and b (not both zero) s the largest nteger whch s a common factor of both a and b. We denote ths number by gcd(a, b), or smply (a, b) when there s no confuson
More information