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Welcome!. PhD Dissertation Defense. PhD Candidate: Wenming Li Advisor: Dr. Krishna M. Kavi Committee: Dr. Krishna M. Kavi Dr. Robert Akl Dr. Phil Sweany. Group-EDF - A New Approach And An Efficient Non-Preemptive Algorithm for Soft Real-Time Systems. Contributions.

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Welcome!PhD Dissertation DefensePhD Candidate: Wenming Li Advisor: Dr. Krishna M. Kavi Committee: Dr. Krishna M. Kavi Dr. Robert Akl Dr. Phil SweanyGroup-EDF - A New Approach And An Efficient Non-Preemptive Algorithm for Soft Real-Time SystemsContributionsA new approach for soft real-time systems. A new scheduling algorithm for soft real-time systems and soft Real-Time Operating System (RTOS). Contributions (Cont’d)Our research work is a new approach for soft real-time systems. - First proposed the strategy of the dynamic grouping of tasks with deadlines.- First proposed a two-level scheduling scenario for real-time tasks.Contributions (Cont’d)Group-EDF is a new scheduling algorithm for soft RTOS and real-time systems. - First proposed to use Earliest Deadline First (EDF) for dynamic groups and Shortest Job First (SJF) within a group.FocusSoft real-time systems and soft RTOS. Non-preemptive scheduling. Real-time periodic, aperiodic, or sporadic tasks. The Taxonomy of Real-time Scheduling Our EDF/gEDF algorithm is applicable to the shaded regionTerminology of the Real-Time ModelHard Real-Time SystemsEvery resource management system must work in the correct order to meet time constraints. No deadline miss is allowed. Disadvantage - Low utilizationSoft Real-Time SystemsIt is similar to hard real-time systems. But it is not necessary that every time constraint be met. Some deadline miss is tolerated. Advantage - High utilizationNon-Preemptive SchedulingWhy non-preemptive? - non-preemptive scheduling is more efficient than preemptive scheduling since preemption incurs context switching overhead which can be significant in fine-grained multithreading systems.Basic Real-Time SchedulingFirst Come First Served (FCFS) Round Robin (RR) Shortest Job First (SJF) First Come First Served (FCFS)Simple “first in first out” queue Long average waiting time Negative for I/O bound processes Nonpreemptive Round Robin (RR)FCFS + preemption with time quantum Performance (average waiting time) is proportional to the size of the time quantum. Shortest Job First (SJF)Optimal with respect to average waiting time. Requires profiling of the execution times of tasks. Static Priority Scheduling – Rate-Monotonic (RM)The shorter the period of a task, the higher is its priority (relative deadline = period). A set of n independent, periodic jobs can be scheduled by the rate monotonic policy if e1/P1 + e2/P2 + … + en/Pnn (21/n - 1)- The upper bound on utilization is ln2 = 0.69 as n approaches infinity.Static Priority Scheduling – Deadline-Monotonic (DM)The shorter the relative deadline of a task, the higher is its priority. Suitable when relative deadline period For arbitrary relative deadlines, DM outperforms RM in terms of utilization. Dynamic Priority Scheduling – Earliest Deadline First (EDF)The first and the most effectively widely used dynamic priority-driven scheduling algorithm. Effective for both preemptive and non-preemptive scheduling periodic, aperiodic, and sporadic tasks. Preemptive EDFFor a set of preemptive periodic, aperiodic, and sporadic tasks, EDF is optimal in the sense that EDF will find a schedule if a schedule is possible for other algorithms. - Approach 100% utilization for periodic tasksNon-Preemptive EDFOptimal for sporadic non-preemptive tasks. Scheduling periodic and aperiodic non-preemptive tasks is NP-hard. - Approach near optimal for non-preemptive scheduling on a uniprocessor system.Theory of EDFMinimize maximum lateness Lmax = max {Li | i = 1, …, n} = max {Ci- di | i = 1, …, n} The problem: 1 | nonpmtn | Lmax Any sequence of jobs in nondecreasing order of due dates di, results in an optimal schedule. The scheduling problem {1 | nonpmtn, ri | Lmax}is NP-hard. Let Lmax = max {Ci- di | i = 1, …, n} = 0, that is, all deadlines of tasks must be met. POSIX 1003.1bPortable Operating System Interface (POSIX) 1003.1b, the IEEE Computer Society’s Portable Application Standards Committee (PASC) - SCHED FIFO - SCHED RR - SCHED OTHERRelated WorkDomino Effect of EDF - OverloadOverload Detection And Control - Best-effort by value-density V/C - Admission control - Disadvantage:Needing accurate utilization computing Switching between two scheduling schemes Using Worst Case Execution Time (WCET)Related WorkSCAN-EDF for disk scheduling - Use SJF to break deadline tiesQuantized deadlines (from CMU) - Static deadline windowsOur Real-time ModelA task (job) in a real-time system or a thread in multithreading processing i is defined as: i = (ri, ei, Di, Pi)Overview of gEDFDivide real-time jobs into groups by their deadlines, dynamically. Groups are based on EDF but tasks within a group may be scheduled based on a different scheme - SJF, Value, Priority, etc. gEDF is used both in underload and overload. Overview of gEDF (Cont’d)We use SJF to enhance EDF, but it is extensible to other scheduling schemes. gEDF is suitable for non-preemptive soft-real-time systems. The criteria of selecting suitable grouping policy is flexible Static deadline windows Dynamic windows as jobs arrive Overview of gEDF (Cont’d)A group in the gEDF algorithm depends on a group range parameter Gr. A job j belongs to the same group as job i if didj (di + Gr*(di – t)), where t is the current time, 1 i, jN. We group jobs with deadlines that are very close to each other. - The jobs with very close deadlines are in a group (but not necessary if at the boundary of groups) The gEDF AlgorithmWe assume a uniprocessor system. QgEDF is a queue for gEDF scheduling. The current time is represented by t. |QgEDF| represents the length of the queue QgEDF. = (r, e, D, P) is the job at the head of the queue. - gEDF Group = {k | k QgEDF, dk – d1D1* Gr, 1 km, where m |QgEDF|}, and D1 is the deadline of the first job in a groupThe gEDF Algorithm (Cont’d)Function Enqueue (QgEDF, ) if ( ’s deadline d > t ) then insert job into QgEDF by Earliest Deadline First, i.e. di di+1di+2, where i, i+1,i+2 QgEDF, 1 i |QgEDF| - 2;end- Enqueue is invoked on job arrivals.The gEDF Algorithm (Cont’d)Function Dequeue (QgEDF) if QgEDFthen find a job min withemin = min {ek | k QgEDF,dk – d1Gr*D1, 1 km, where m |QgEDF|}; run it and delete min from QgEDF;end- Dequeue is called when the processor becomes idle.The ExperimentUsed MATLAB provided tools to generate tasks. - In each experiment generated N tasks. - The jobs are scheduling using EDF & gEDF. - The experiment is truncated at a predetermined time T. Success rate is computed based on m out of N jobs completed.The Experiment (Cont’d)Varied - Load (or utilization) - Execution time - Deadline (tight deadlines & loose deadlines) - Group range - Deadline tolerance (hard vs. soft real-time)The Experiment (Cont’d)For each set of parameters, the experiment is repeated 100 times and the results shown are the averages from the 100 experiments. Success Ratio: gEDF vs. EDFDeadline Tolerance Tr = 0.2 Success Ratio: gEDF vs. EDFDeadline Tolerance Tr = 0.5Success Ratio: gEDF vs. EDFDeadline Tolerance Tr = 1.0Success Ratio: gEDF vs. EDFSummary of the three previous figuresSuccess Ratio: gEDF vs. EDFSummary of the three previous figuresThe gEDF algorithm obtains higher success ratio under higher system loads. Suitable for soft real-time systems. Success Ratio: gEDF vs. EDF/Best-Effort/GuaranteeSummary when Tr = 0.5Effect of Deadline Laxity on Success RatioTight Deadline D = 1 (Deadline = Execution Time)and hard real-time Effect of Deadline Laxity on Success RatioTight Deadline D = 1 (Deadline = Execution Time)and softer real-time Effect of Deadline Laxity on Success RatioLoose Deadline D = 5 (Deadline = 5*Execution Time)Effect of Deadline Laxity on Success RatioLoose Deadline D = 5 (Deadline = 5*Execution Time)Effect of Deadline on Success RatioSuccess Ratio of EDF when D = 1, 2, 5, 10, and 15(i.e. Deadline = D*Execution Time)Effect of Deadline onSuccess RatioSuccess Ratio of gEDF when D = 1, 2, 5, 10, and 15(i.e. Deadline = D*Execution Time)Effect of Deadline onSuccess RatioThe gEDF algorithm has higher performance (i.e. success ratio) than EDF with greater deadline laxity and greater deadline tolerances. Effect of Group Range (Gr)Gr = 0.1, 0.2, 0.5, 1.0, Tr = 0.1Effect of Group Range (Gr)Gr = 0.1, 0.2, 0.5, 1.0, Tr = 0.5Effect of Group Range (Gr)Within our experimental range, the size of the group does not seem to show a great variance. Intuitively - very large range means gEDF = SJF - Very short range means gEDF = EDFOptimal window depends on execution times of jobs, deadline tightness, deadline tolerance. Response Time: gEDF vs. EDFDeadline Tolerance Tr = 0Response Time: gEDF vs. EDFDeadline Tolerance Tr = 0.5Response Time: gEDF vs. EDFDeadline Tolerance Tr = 1.0Response Time: gEDF vs. EDFThe gEDF algorithm can yield better (=faster) response times than EDF. Both in underloaded and overloaded situations. Deadline tolerance Tr has greater impact on gEDF than on EDF. Response Time: gEDF vs. EDF/Best-Effort/GuaranteeSummery Tr = 0.2The Effect of Deadline onResponse TimeResponse time of EDF when D = 1, 2, 5, and 10The Effect of Deadline on Response TimeResponse time of gEDF when D = 1, 2, 5, and 10The Effect of Deadline onResponse TimeWhen expected value of deadlines D is sufficiently large (>2), gEDF results in faster response times than EDF does. The gEDF Implementation in the Linux KernelKeep the original functions for non-real-time applications. Modify structure task_struct and add a new specific runqueue for EDF/gEDF. Add the system call (extension to POSIX) sys_sched_setscheduler_plusThe gEDF Implementation in the Linux Kernel (Cont’d)Add a new structure struct edf_param { unsigned long policy; unsigned long period; unsigned long length; }The gEDF Implementationin the Linux Kernel (Cont’d)Dequeue_edf_task() Enqueue_edf_task() (for EDF & gEDF) Schedule() (include the gEDF algorithm) - Every one jiffy (1ms), entering the kernel to run schedule function (user process can also yield to other process) - Complexity O(n) (If using heap, O(log(n)). ref. Ingo Molnar O(1))Benchmark TestingTest SuitesBenchmark Testing (Cont’d)Another Test SuiteTesting ResultsTesting Results (Cont’d)gEDF’s Success Ratio/EDF’s Success RatioY - axis: LoadX - axis: gEDF’s Success Ratio / EDF’s Success RatioConclusionsgEDF performs as well as or better than EDF and adaptive algorithms such as Best-Effort and Guarantee schemes. In underloaded, gEDF performs as well as EDF in terms of success ratio; gEDF shows higher success rates than EDF when dealing with soft real-time tasks. In underloaded, gEDF performs much better than EDF in terms of response time. Conclusions (Cont’d)In underloaded, gEDF obtains overall better performance than adaptive algorithms in terms of success ratio and response time. In overloaded, gEDF consistently outperforms EDF both in success ratio and response time. In overloaded, gEDF obtains overall better performance than adaptive algorithms in terms of success ratio and response time. AlgorithmSuccess RatioResponse TimeUnderloadOverloadUnderloadOverloadGroup-EDF vs. EDF=>>=>>Group –EDF vs. AdaptiveAlgorithmBest-Effort=>>=>GuaranteeScheme=>>>=>>Conclusions (Cont’d)Summary=: at least as good as>=:better or as good as>:better>>:much betterFuture WorkExplore the applicability of gEDF algorithm for Scheduled Dataflow (SDF) Architecture. Explore if gEDF can be used to obtain acceptable (and near optimal) results for multiprocessor systems with soft real-time tasks. Exploring different scheduling scheme applied within each gEDF. gEDF for SDF SU: Scheduling Unit EP: Execution Processor SP: Synchronization Processor PLC: Preload PSC: Poststore EXC: ExecutiongEDF for MultiprocessorEDF is not optimal for multiprocessor real-time systems. The EDF scheme can be used to schedule dynamic groups on multiprocessors. An optimal or near optimal algorithm may be adopted to schedule jobs distributed on different processors within each dynamic group. gEDF for Multiprocessor (Cont’d)Advantage for using gEDF - Not limited to SJF - Possible higher success ratios in underloaded and overloaded situationsScheduling within A Group Exploring different scheduling scheme applied within each gEDF. - A promising research of applying the gEDF scenario.Reduce overall power consumption. - Explore a scheduling scheme that minimizes the power consumed by tasks in a group.Thank You !

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