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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 SystemsContributions
  • A 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.Focus
  • Soft 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 Systems
  • Every resource management system must work in the correct order to meet time constraints. No deadline miss is allowed.
  • Disadvantage
  • - Low utilizationSoft Real-Time Systems
  • It 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 Scheduling
  • Why 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 Scheduling
  • First 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/Pnn (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 EDF
  • For 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 EDF
  • Optimal 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 EDF
  • Minimize 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 1003.1b
  • Portable Operating System Interface (POSIX) 1003.1b, the IEEE Computer Society’s Portable Application Standards Committee (PASC)
  • - SCHED FIFO - SCHED RR - SCHED OTHERRelated Work
  • Domino Effect of EDF
  • - Overload
  • Overload 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 Work
  • SCAN-EDF for disk scheduling
  • - Use SJF to break deadline ties
  • Quantized deadlines (from CMU)
  • - Static deadline windowsOur Real-time Model
  • A task (job) in a real-time system or a thread in multithreading processing i is defined as:
  • i = (ri, ei, Di, Pi)Overview of gEDF
  • Divide 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 didj (di + Gr*(di – t)), where t is the current time, 1 i, jN. 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 Algorithm
  • We 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 – d1D1* Gr, 1 km, 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+1di+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 QgEDFthen find a job min withemin = min {ek | k QgEDF,dk – d1Gr*D1, 1 km, where m |QgEDF|}; run it and delete min from QgEDF;end- Dequeue is called when the processor becomes idle.The Experiment
  • Used 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 figures
  • The 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 Ratio
  • The 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 = EDF
  • Optimal 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. EDF
  • The 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 Time
  • When expected value of deadlines D is sufficiently large (>2), gEDF results in faster response times than EDF does.
  • The gEDF Implementation in the Linux Kernel
  • Keep 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 RatioConclusions
  • gEDF 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 Work
  • Explore 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 Multiprocessor
  • EDF 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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