Real-time systems are systems that carry real-time tasks. These tasks need to be performed immediately with a certain degree of urgency. In particular, these tasks are related to control of certain events (or) reacting to them. Real-time tasks can be classified as hard real-time tasks and soft real-time tasks.
A hard real-time task must be performed at a specified time which could otherwise lead to huge losses. In soft real-time tasks, a specified deadline can be missed. This is because the task can be rescheduled (or) can be completed after the specified time,
In real-time systems, the scheduler is considered as the most important component which is typically a short-term task scheduler. The main focus of this scheduler is to reduce the response time associated with each of the associated processes instead of handling the deadline.
If a preemptive scheduler is used, the real-time task needs to wait until its corresponding tasks time slice completes. In the case of a non-preemptive scheduler, even if the highest priority is allocated to the task, it needs to wait until the completion of the current task. This task can be slow (or) of the lower priority and can lead to a longer wait.
A better approach is designed by combining both preemptive and non-preemptive scheduling. This can be done by introducing time-based interrupts in priority based systems which means the currently running process is interrupted on a time-based interval and if a higher priority process is present in a ready queue, it is executed by preempting the current process.
Based on schedulability, implementation (static or dynamic), and the result (self or dependent) of analysis, the scheduling algorithm are classified as follows.
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Static table-driven approaches:
These algorithms usually perform a static analysis associated with scheduling and capture the schedules that are advantageous. This helps in providing a schedule that can point out a task with which the execution must be started at run time.
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Static priority-driven preemptive approaches:
Similar to the first approach, these type of algorithms also uses static analysis of scheduling. The difference is that instead of selecting a particular schedule, it provides a useful way of assigning priorities among various tasks in preemptive scheduling.
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Dynamic planning-based approaches:
Here, the feasible schedules are identified dynamically (at run time). It carries a certain fixed time interval and a process is executed if and only if satisfies the time constraint.
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Dynamic best effort approaches:
These types of approaches consider deadlines instead of feasible schedules. Therefore the task is aborted if its deadline is reached. This approach is used widely is most of the real-time systems.
Advantages of Scheduling in Real-Time Systems:
- Meeting Timing Constraints: Scheduling ensures that real-time tasks are executed within their specified timing constraints. It guarantees that critical tasks are completed on time, preventing potential system failures or losses.
- Resource Optimization: Scheduling algorithms allocate system resources effectively, ensuring efficient utilization of processor time, memory, and other resources. This helps maximize system throughput and performance.
- Priority-Based Execution: Scheduling allows for priority-based execution, where higher-priority tasks are given precedence over lower-priority tasks. This ensures that time-critical tasks are promptly executed, leading to improved system responsiveness and reliability.
- Predictability and Determinism: Real-time scheduling provides predictability and determinism in task execution. It enables developers to analyze and guarantee the worst-case execution time and response time of tasks, ensuring that critical deadlines are met.
- Control Over Task Execution: Scheduling algorithms allow developers to have fine-grained control over how tasks are executed, such as specifying task priorities, deadlines, and inter-task dependencies. This control facilitates the design and implementation of complex real-time systems.
Disadvantages of Scheduling in Real-Time Systems:
- Increased Complexity: Real-time scheduling introduces additional complexity to system design and implementation. Developers need to carefully analyze task requirements, define priorities, and select suitable scheduling algorithms. This complexity can lead to increased development time and effort.
- Overhead: Scheduling introduces some overhead in terms of context switching, task prioritization, and scheduling decisions. This overhead can impact system performance, especially in cases where frequent context switches or complex scheduling algorithms are employed.
- Limited Resources: Real-time systems often operate under resource-constrained environments. Scheduling tasks within these limitations can be challenging, as the available resources may not be sufficient to meet all timing constraints or execute all tasks simultaneously.
- Verification and Validation: Validating the correctness of real-time schedules and ensuring that all tasks meet their deadlines require rigorous testing and verification techniques. Verifying timing constraints and guaranteeing the absence of timing errors can be a complex and time-consuming process.
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Scalability: Scheduling algorithms that work well for smaller systems may not scale effectively to larger, more complex real-time systems. As the number of tasks and system complexity increases, scheduling decisions become more challenging and may require more advanced algorithms or approaches.