CHAPTER 4 CONTENT LECTURE 1 November :28 AM

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1 CHAPTER 4 By Radu Muresan University of Guelph Page 1 CHAPTER 4 CONTENT LECTURE 1 November :28 AM UNIPROCESSOR SCHEDULING Real Time Task Model Concepts Types of Real Time Tasks and Their Characteristics Task Scheduling Clock Driven Scheduling Hybrid Schedulers Event Driven Scheduling Earliest Deadline First (EDF) Scheduling Rate Monotonic Algorithm (RMA) Some Issues Associated with RMA RMA in Practical Situations MULTI PROCESSOR SCHEDULING Multiprocessor Task Allocation Dynamic Allocation Tasks Fault Tolerant Scheduling of Tasks Clocks in Distributed Real Time Systems Centralized Clock Synchronization Distributed Clock Synchronization REFERENCES: Real Time Systems by Jane Liu; Real Time Systems by Krishna Shin Real Time Systems by Rajib Mall, 2008.

2 CHAPTER 4 By Radu Muresan University of Guelph Page 2 REAL TIME TASK SCHEDULING November :04 PM Assume only deadline constraints on real time tasks Other types of time constraints are related to performance constraints and behavioural constraints. Real time tasks get generated (issued or released) in response to some events that may be either internal or external to a system: Examples: internal clock interrupt; user pressing a switch. When a task gets generated, it is said to have arrived or released. Every real time system usually consists of a number of real time tasks the time bounds on different tasks may vary. Criticality of a real time task relates to the consequences of a task missing its time bounds. Appropriate scheduling of tasks is the basic mechanism adopted by a RTOS to meet the time constraints of a task. Therefore, selection of an appropriate task scheduling algorithm is central to the proper functioning of a real time system. Understanding the scheduling algorithms will help us appreciate the modern commercial RTOS.

3 CHAPTER 4 By Radu Muresan University of Guelph Page 3 REAL TIME TASK CONCEPTS Task instance: each time an event occurs, it triggers the task that handles this event to run (a task is generated when some specific event occurs). Real time tasks, therefore, normally recur a large number of times at different instants of time depending on the event occurrence time: At random instants Ex. A task handling a device interrupt Within certain fixed periods Temperature sensing task in a chemical plant The first time a task occurs is called the first instance of the task; the next occurrence is called the second instance and so on. The j th instance of a task T i would be denoted as T i (j). Each instance of a real time task is associated with a deadline by which it needs to complete and produce results. We can use interchangeably task instance or process.

4 CHAPTER 4 By Radu Muresan University of Guelph Page 4 RELATIVE AND ABSOLUTE DEADLINE OF A TASK PLUS THE RESPONSE TIME OF A TASK The absolute deadline of a task is the absolute time value by which the results from the task are expected. The Relative deadline is the time interval between the start of the task and the instant at which the deadline occurs. The response time is the time duration from the occurrence of the event generating the task to the time the task produces its result. 1) Note that for hard real time tasks we need to meet their deadlines no special advantage in completing the tasks early. 2) For soft real time tasks, average response time of tasks is an important metric measure need to minimize the average response time for soft real time tasks.

5 CHAPTER 4 By Radu Muresan University of Guelph Page 5 TASK PRECEDENCE A task is said to precede another task, if the first task must complete before the second task can start. When a task Ti precedes another task Tj, then each instance of Ti precedes the corresponding instance of Tj Ex. If T1 proceeds T2 then: T1(1) precedes T2(1); T1(2) precedes T2(2); etc. A precedence order defines a partial order among tasks a partial ordering relation is reflexive, antisymmetric, and transitive. Figure below is an example of partial ordering among tasks task T1 precedes T2, but we cannot relate T1 with either T3 or T4.

6 CHAPTER 4 By Radu Muresan University of Guelph Page 6 DATA SHARING AMONG TASKS Tasks often need to share their results among each other precedence relation between two tasks sometimes implies data sharing (not always true, sometimes we need to perform only actions without data sharing). Data sharing may occur not only when one tasks precedes another, but might occur among truly concurrent tasks, and overlapping tasks. Data sharing among tasks does not necessarily impose any particular ordering among tasks. As a result, data sharing relation among tasks is represented using a dashed arrow as in the figure above where: T2 uses the results of T3, but T2 and T3 may execute concurrently, T2 may even start executing first, after sometimes it may receive some data from T3, and continue its execution, and so on.

7 CHAPTER 4 By Radu Muresan University of Guelph Page 7 TYPES OF REAL TIME TASKS AND THEIR CHARACTERISTICS Based on the task recurrence over a period of time the real time tasks are classified into 3 main categories: PERIODIC TASKS Periodic tasks repeat after a certain fixed time interval. SPORADIC TASKS Sporadic tasks recur at random instants. APERIODIC TASKS Same as sporadic except that minimum separation between two instances can be 0.

8 CHAPTER 4 By Radu Muresan University of Guelph Page 8 PERIODIC TASKS Formally, a periodic task Ti can be represented by a four tuple (Φi, pi, ei, di), where: Φi is the phase of task Ti and is defined as the time from 0 until the first instance of Ti (i.e., Ti(1)); pi is the period of the task Ti; ei is the worst case execution time of Ti; di is the relative deadline of the task. The periods are usually demarcated by clock interrupts and the task are also called clock driven tasks.

9 CHAPTER 4 By Radu Muresan University of Guelph Page 9 Majority of the task present in a typical real time system are periodic. EXAMPLES numerous in chemical plants. In a chemical plant several temperature monitors, pressure monitors, and chemical concentration monitors periodically sample the current temperature, pressure, and chemical concentration values which are then communicated to the plant controller. The instance of the temperature, pressure, and chemical concentration monitoring tasks are normally generated through the interrupts received from a periodic timer the data inputs are used to compute corrective actions required to maintain the chemical reaction at a certain rate. The corrective actions are then carried out through actuators. The periodic tasks in the above example exist from initialization however, periodic tasks can also come into existence dynamically. EXAMPLE the computation that occurs in air traffic monitors, once a flight is detected by the radar until the radar exits the radar signal zone is an a dynamically created periodic task.

10 CHAPTER 4 By Radu Muresan University of Guelph Page 10 SPORADIC TASKS A sporadic task is one that recurs at random instants. A sporadic task Ti can be represented by a three tuple: Ti = (ei, gi, di) Where: ei is the worst case execution time of an instance of the task; gi denotes the minimum separation between two consecutive instances of the task; di is the relative deadline. NOTE that gi restricts the rate at which sporadic tasks can arise. The first instance of a sporadic task Ti is denoted by Ti(1) and the successive instances by Ti(2), Ti(3), etc. Many sporadic tasks such as emergency message arrivals are highly critical in nature. Ex. 1) In a robot a task that gets generated to handle an obstacle that suddenly appears is a sporadic task. 2) In a factory, the task that handles fire conditions is a sporadic task. The criticality of sporadic tasks varies from highly critical to moderately critical Ex. 1) An I/O device interrupt, or a DMA interrupt is moderately critical; 2) a task handling the reporting of fire conditions is highly critical.

11 CHAPTER 4 By Radu Muresan University of Guelph Page 11 APERIODIC TASKS An aperiodic task as a sporadic task can arise at random instances. However, the minimum separation gi between two consecutive aperiodic tasks can be 0 => two or more instances of an aperiodic task might occur at the same time instant. The deadline for an aperiodic task is expressed as either an average value or is expressed statistically. Aperiodic tasks are generally soft real time tasks. Aperiodic tasks can recur in quick succession; as a result, it becomes very difficult to meet the deadline of all instances of an aperiodic task. Example: 1) logging task in a distributed system the logging task can be started by different tasks running on different nodes; the logging requests from different tasks may arrive at the logger almost at the same time, or requests may be spaced in time; 2) operator requests, keyboard presses, mouse movements, etc. (all interactive commands issued by users are handled by aperiodic tasks.

12 CHAPTER 4 By Radu Muresan University of Guelph Page 12 TASK SCHEDULING Real Time task scheduling refers in general to determining the order in which the various tasks are to be taken up for execution by the operating system. Every operating system relies on one or more task schedulers to prepare the schedule of execution of various tasks it needs to run. Each scheduler is characterized by the scheduling algorithm it employs. Real time scheduling on uniprocessor is a mature discipline with most of the important results having been worked out in the early 1970s. We present broad scheduling classes and study the characteristics of a few algorithms.

13 CHAPTER 4 By Radu Muresan University of Guelph Page 13 BASIC CONCEPTS AND TERMINOLOGIES VALID SCHEDULE: a valid schedule for a set of tasks is one where at most one task is assigned to a processor at a time, no task is scheduled before its arrival time, and the precedence and resource constraints of all tasks are satisfied. FEASIBLE SCHEDULE: a valid schedule is called a feasible schedule, only if all tasks meet their respective time constraints in the schedule. PROFICIENT SCHEDULER: a task scheduler SCH1 is said to be more proficient than another scheduler SCH2, if SCH1 can feasibly schedule all task sets that SCH2 can, but there is at least one task set that SCH2 can not feasibly schedule, whereas SCH1 can. If SCH1 can feasibly schedule all task sets that SCH2 can feasibly schedule and vice versa, then SCH1 and SCH2 are called equally proficient schedulers. OPTIMAL SCHEDULER: a real time scheduler is called optimal, if it can feasibly schedule any task set that can be feasibly scheduled by any other scheduler we can't find a more proficient scheduling algorithm than the optimal scheduler.

14 CHAPTER 4 By Radu Muresan University of Guelph Page 14 SCHEDULING POINTS the scheduling points of a scheduler are the points in time where the scheduler makes decisions regarding to what task to run next. A task scheduler is activated by the OS only at the scheduling points to make these decisions. Ex. 1) in a clock driven scheduler the scheduling points are the time instants marked by interrupts generated by a periodic timer; 2) in an event driven scheduler the scheduling points are determined by occurrence of certain events. PREEMPTIVE SCHEDULER: See Chapter 2. UTILIZATION: the processor utilization of a task is the average time for which it executes per unit time interval. 1) For a periodic task Ti: 2) For a set of periodic tasks {Ti}: JITTER: is the deviation of a periodic task from its strict periodic behaviour. ARRIVAL TIME JITTER is the deviation of the task from arriving at the precise periodic time of arrival. It may be caused by imprecise clocks, or other factors such as network congestions. COMPLETION TIME JITTER is the deviation of the completion of a task from precise periodic points. It may be caused by the specific algorithm employed for scheduling and the load at an instant.

15 CHAPTER 4 By Radu Muresan University of Guelph Page 15 CLASSIFICATION OF REAL TIME TASK SCHEDULING ALGORITHMS A) 1) 2) 3) B) 1) 2) COMMON CLASSIFICATIONS of real time task scheduling algorithms are based on: HOW THE SCHEDULING POINTS ARE DEFINED in this class we have the following types of schedulers: CLOCK DRIVEN a. Table driven b. Cyclic EVENT DRIVEN more complex, more proficient (they can schedule some task sets that clock driven can't), and more flexible (they can feasibly schedule sporadic and aperiodic tasks in addition to periodic tasks) than clock driven. EDF and RMA are the most popular scheduling algorithms that have been developed. These two algorithms form the basis of uniprocessor scheduling. a. Simple priority based; b. Rate Monotonic Analysis (RMA) c. Earliest Deadline First (EDF) HYBRID a. Round robin TYPE OF ACCEPTANCE TEST PERFORMED BY SCHEDULER in this class we have two broad categories Planning base Best effort

16 CHAPTER 4 By Radu Muresan University of Guelph Page 16 C) 1) 2) 3) Best effort no acceptance test is performed. All tasks that arrive are taken up for scheduling and best effort is made to meet their deadlines. But, no guarantee is given as to whether a task's deadline would be met. Planning Base the scheduler first determines whether the task will meet its deadlines and if will not cause other already scheduled tasks to miss their deadlines, then the task it is taken up for execution. THE TARGET PLATFORM on which the tasks are to be run. According to this scheme, we have the following classes of scheduling algorithms: Uniprocessor: are the simplest scheduling algorithms Multiprocessor: algorithms must first decide which task needs to run on which processor and then these tasks are scheduled. Distributed systems we need first to allocate tasks and we need carefully designed scheduling algorithms: a. In contrast to multiprocessor systems the processors in a distributed system: do not possess shared memory; there is no global up to date state information available. As a result, uniprocessor algorithms that assume a central state information of all tasks are unsuitable to be used in distributed systems. b. The communication among tasks in distributed systems is through message passing which is costly. As a result, the communication among tasks in distributed systems should be minimized.