Deadline Monotonic Research Articles

Energy Harvesting Deadline Monotonic Approach for Real-time Energy Autonomous Systems

This paper presents an innovative scheduling algorithm designed specifically for real-time energy harvesting systems, with a primary focus on minimizing energy consumption and extending the battery's lifespan. The algorithm employs a fixed priority assignment which is the deadline monotonic policy, we have chosen it for its optimality and superior performance compared to other fixed priority scheduling methods. To achieve a balance between energy efficiency and system performance, we incorporated a DVFS (Dynamic Voltage and Frequency Scaling) technique into the algorithm. This adaptive approach enables precise control over the processor's operating frequency, effectively managing energy consumption while ensuring satisfactory system functionality. The core objective of our scheduling algorithm centers on optimizing energy utilization in real-time energy harvesting systems, specifically tailored to extend the battery's operational life. Rigorous evaluations, including comprehensive comparisons against established fixed priority scheduling algorithms, validate the algorithm's efficacy in significantly reducing energy consumption while preserving the system's overall functionality. By combining the deadline monotonic policy and DVFS technique, our proposed algorithm emerges as a promising solution for energy-autonomous systems, contributing to the advancement of sustainable energy practices in real-time applications. As energy harvesting technologies continue to progress, our algorithm holds valuable potential to provide critical insights for enhancing the efficiency and reliability of future energy harvesting systems.

A New Scheduling Algorithm for Shortening Response Time of Static Priority Task

In scheduling algorithms based on the Rate Monotonic (RM) method which are widely used in development of real-time systems, tasks with shorter periods have higher priorities. In contrast, ones with longer periods are likely to suffer from increased response times and jitter due to their lower priorities. We propose an Execution Right Delegation (ERD) method based on RM where a high-priority server for particular (or important) task is introduced to shorten response time and jitter of the task. In the evaluation, it is confirmed that response times and jitter of a particular (important) task are reduced. We also show Response Time Analysis (RTA), which assures worst-case response time of the task. This paper shows the algorithm and RTA of ERD and evaluates it by comparing it to a Deadline Monotonic method. The evaluation by simulation shows that ERD can reduce the average worst-case response time by 13.45% at maximum compared to the Deadline Monotonic scheduling. In addition, we confirm that the RTA provides a worst-case response time close to the simulation results.

A New Scheduling Algorithm for Shortening Response Time of Static Priority Task

In scheduling algorithms based on the Rate Monotonic (RM) method which are widely used in development of real-time systems, tasks with shorter periods have higher priorities. In contrast, ones with longer periods are likely to suffer from increased response times and jitter due to their lower priorities. We propose an Execution Right Delegation (ERD) method based on RM where a high-priority server for particular (or important) task is introduced to shorten response time and jitter of the task. In the evaluation, it is confirmed that response times and jitter of a particular (important) task are reduced. We also show Response Time Analysis (RTA), which assures worst-case response time of the task. This paper shows the algorithm and RTA of ERD and evaluates it by comparing it to a Deadline Monotonic method. The evaluation by simulation shows that ERD can reduce the average worst-case response time by 13.45% at maximum compared to the Deadline Monotonic scheduling. In addition, we confirm that the RTA provides a worst-case response time close to the simulation results.

A Schedulability Test for Sporadic Task DM Scheduling Based on Density Upper Bound

Due to low runtime overhead and simple implementation of DM (Deadline Monotonic) scheduling, it is widely used in real-time systems. Aiming at the schedulability test problem of the sporadic task DM scheduling under uniprocessor, a density upper bound of 0.693 is analyzed, which can determine the schedulability of a task set in linear time. The theoretical analysis process is carried out in three steps. Firstly, the case is considered where the task set contains only two tasks and the deadline ratio between the tasks is less than 2. Secondly, the case is considered where the task set contains multiple tasks and the deadline ratio between any two tasks is still less than 2. Finally, the case is considered where the task set contains multiple tasks and the deadline ratio between tasks is an arbitrary value. The experimental results show that the upper bound of density is higher than related methods and the run time overhead is much lower than that of other available exact schedulability tests. The time complexity is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(1)$ </tex-math></inline-formula> when a task is dynamically added to a task set, while that of other methods increases rapidly along with the number of tasks. In addition, we combined this upper bound and other tests to further propose an exact schedulability test, which effectively reduces the running time overhead by 30.8% compared with the state-of-the-art schedulability test. Due to the high efficiency of our schedulability test, an online schedulability test can be implemented in open real-time systems.

An exact comparison of global, partitioned, and semi-partitioned fixed-priority real-time multiprocessor schedulers

We evaluate the performance of global, partitioned, and semi-partitioned fixed-priority ( FP ) multiprocessor schedulers for sporadic real-time tasks. By conducting a range of exhaustive experiments, we support a prior observation of Baruah regarding the incomparability of global and partitioned fixed-priority schedulers, by demonstrating the existence of multiple task sets, which are schedulable by either one or another approach, but not both simultaneously. In addition, we demonstrate that a suboptimal semi-partitioning heuristic, named Deadline Monotonic with Priority Migration ( DM - PM ), in certain cases outperforms both global and partitioned FP schedulers. Unlike other works, our evaluation is based on an exact schedulability test for global scheduling, as well as relies on optimal task priorities for global FP , and an optimal partitioning of tasks to processors. However, due to the exponential computational complexity of the available exact schedulability test, our evaluation is constrained to a limited number of tasks in a set. On average, partitioned scheduling performs better for a lower number of heavy tasks in a task set, while global scheduling is the opposite. The schedulability gain of one approach over another might exceed 50%, for both cases. To make such an evaluation possible, we derive a method to compute optimal task priorities for global FP by using a set of pruning rules. We demonstrate that the gain of such optimal priorities over other widely used priority assignment heuristics, including Deadline Monotonic ( DM ), can be significant, in certain cases. Moreover, we evaluate the performance of an optimal task partitioning over the available suboptimal partition heuristics, namely first-fit decreasing (FFD) and worst-fit decreasing (WFD), and confirm no significant performance difference between them, for considered task settings. Software tools used in our evaluation are implemented using C++ libraries, and are publicly available.

Priority Assignment on Partitioned Multiprocessor Systems With Shared Resources

Driven by industry demand, there is an increasing need to develop real-time multiprocessor systems which contain shared resources. The Multiprocessor Stack Resource Policy (MSRP) and Multiprocessor resource sharing Protocol (MrsP) are two major protocols that manage access to shared resources. Both of them can be applied to Fixed-Priority Preemptive Scheduling (FPPS), which is enforced by most commercial real-time systems regulations, and which requires task priorities to be assigned before deployment. Along with MSRP and MrsP, there exist two forms of schedulability tests that bound the worst-case blocking time due to resource accesses: the traditional ones being more widely adopted and the more recently developed holistic ones which deliver tighter analysis. On uniprocessor systems, there are several well-established optimal priority assignment algorithms. Unfortunately, on multiprocessor systems with shared resources, the issue of priority assignment has not been adequately understood. In this article, we investigate three mainstream priority assignment algorithms–Deadline Monotonic Priority Ordering (DMPO), Audsley’s Optimal Priority Assignment (OPA), and Robust Priority Assignment (RPA), in the context of partitioned multiprocessor systems with shared resources. Our contributions are multifold: First, we prove that DMPO is optimal with the traditional schedulability tests. Second, two counter examples are given as evidence that DMPO is not optimal with the tighter holistic schedulability tests. Third, we then analyze the pessimism arising from the adoption of OPA and RPA with the holistic tests. Lastly, we propose a Slack-based Priority Ordering (SPO) algorithm that minimises such pessimism, and has polynomial time complexity. Comprehensive experiments show that SPO outperforms (i.e., results in a larger number of schedulable systems) DMPO, OPA, and RPA in general with the holistic schedulability tests, by up to 15 percent. With the theoretical contributions, this paper is a useful guide to priority assignment in real-time partitioned multiprocessor systems with shared resources.

A Generalized Parallel Task Model for Recurrent Real-Time Processes

A model is considered for representing recurrent precedence-constrained tasks that are to execute on multiprocessor platforms. A recurrent task is specified as a directed acyclic graph (DAG), a period, and a relative deadline. Each vertex of the DAG represents a sequential job, while the edges of the DAG represent precedence constraints between these jobs. All the jobs of the DAG are released simultaneously and need to complete execution within the specified relative deadline of their release. Each task may release jobs in this manner an unbounded number of times, with successive releases occurring at least the specified period apart. Conditional control structures are also allowed. The scheduling problem is to determine whether a set of such recurrent tasks can be scheduled to always meet all deadlines upon a specified number of identical processors. This problem is shown to be computationally intractable, but amenable to efficient approximate solutions. Earliest Deadline First (EDF) and Deadline Monotonic (DM) are shown to be good approximate global scheduling algorithms. Polynomial and pseudo-polynomial time schedulability tests, of differing effectiveness, are presented for determining whether a given task set can be scheduled by EDF or DM to always meet deadlines on a specified number of processors.

Stretching algorithm for global scheduling of real-time DAG tasks

Parallelism is becoming more important nowadays due to the increasing use of multiprocessor systems. A Directed Acyclic Graph (DAG) is a general model of parallel tasks with inter-subtask parallelism. It consists of a collection of dependent subtasks under precedence constraints. In this paper, we study the problem of scheduling n periodic parallel real-time DAG tasks on m homogeneous multiprocessor systems. The dependencies between subtasks make scheduling process more challenging. We propose a stretching algorithm to be applied on each DAG task prior to scheduling process. Thus, DAGs are transformed into a set of independent sequential threads with intermediate offsets and deadlines. The threads obtained due to the transformation are of two types, (i) fully-stretched master threads with utilization equal to 1 and (ii) independent constrained-deadline threads. We propose a scheduling method over RTOS to ensure the execution of fully-stretched master threads on dedicated processors while the remaining processors $$\overline{m} \le m$$ , are used to schedule the independent constrained-deadline threads using any multiprocessor scheduling algorithm. In this work, we analyze the stretching algorithm while considering two global preemptive scheduling algorithms from different priority assignment families; the Global Earliest Deadline First (GEDF) from the fixed job priority family, and the Global Deadline Monotonic (GDM) from the fixed task priority family. We prove that GEDF scheduling of stretched threads has a resource augmentation bound equal to $$\frac{3+\sqrt{5}}{2}$$ for all task sets with $$n < \varphi \times \overline{m}$$ , where n is the number of tasks in the set and $$\varphi $$ is the golden ratio (the value of the golden ratio is $$\frac{1+\sqrt{5}}{2}$$ ). While GDM has a resource augmentation bound equal to $$2+\sqrt{3}$$ for all task sets with $$n < \frac{1+\sqrt{3}}{2} \times \overline{m}$$ .

Multishape Task Scheduling Algorithm for Real Time Micro-Controller Based Application

Embedded Systems are usually microcontroller-based systems that represent a class of reliable and dependable dedicated computer systems designed for specific purposes. Micro-controllers are used in most electronic devices in an endless variety of ways. Some micro-controller-based embedded systems are required to respond to external events in the shortest possible time and such systems are known as real time embedded systems. So in multitasking system there is a need of task Scheduling, there are various scheduling algorithms like Fixed priority Scheduling (FPS), Earliest deadline first (EDF), Rate Monotonic(RM), Deadline Monotonic(DM), etc. have been researched. In this Report various conventional algorithms have been reviewed and analyzed, these algorithms consists of single shape task, A new Multi-shape task scheduling algorithms has been proposed , implemented and analyzed.

Assessment of nested-parallel task model under real-time scheduling on multi-core processors

Real-time applications contain numerous time-bound parallel tasks with enormous computations. Parallel models, not the sequential models, have the capability to handle intra-task parallelism and accomplish such tasks in a specific time or before. Previous researchers presented the task models for parallel tasks, but not for the nested-parallel tasks. This paper deals with the real-time scheduling of periodic nested-parallel tasks having an implicit deadline on multi-core processors. Initially, an nested-parallel task model is developed. Next, a novel task disintegration technique is studied where the MAM's ratio is defined to categorise the segments. It is theoretically proved that the discussed disintegration technique achieved a speedup factor of 4.30 and 3.40 when the tasks, after disintegration, are scheduled under partitioned deadline monotonic (DM) and global earliest deadline first (EDF) scheduling, respectively. Further, considering the overhead factor (β) for non-preemptive global EDF scheduling, disintegration technique is analysed and achieved a speedup factor of 3.73 (for β = 1). The proposed disintegration technique is assessed through the simulations thereby indicating the adequacy of derived speedup factors.

Exact speedup factors for linear-time schedulability tests for fixed-priority preemptive and non-preemptive scheduling

In this paper, we investigate the quality of several linear-time schedulability tests for preemptive and non-preemptive fixed-priority scheduling of uniprocessor systems. The metric used to assess the quality of these tests is the resource augmentation bound commonly known as the processor speedup factor. The speedup factor of a schedulability test corresponds to the smallest factor by which the processing speed of a uniprocessor needs to be increased such that any task set that is feasible under an optimal preemptive (non-preemptive) work-conserving scheduling algorithm is guaranteed to be schedulable with preemptive (non-preemptive) fixed priority scheduling if this scheduling test is used, assuming an appropriate priority assignment. We show the surprising result that the exact speedup factors for Deadline Monotonic (DM) priority assignment combined with sufficient linear-time schedulability tests for implicit-, constrained-, and arbitrary-deadline task sets are the same as those obtained for optimal priority assignment policies combined with exact schedulability tests. Thus in terms of the speedup-factors required, there is no penalty in using DM priority assignment and simple linear schedulability tests.

Temporal Consistency Maintenance Upon Partitioned Multiprocessor Platforms

Maintaining timeliness and data freshness for real-time data objects has long been recognized as an important problem in real-time database research. Despite years of active research, most of the past work focuses on uniprocessor systems. In this paper, we study the workload-aware temporal consistency maintenance problem upon multiprocessor platforms. We consider the problem of how to partition a set of update transactions to $m \ge 2$ processors to maintain the temporal consistency of real-time data objects under both earliest deadline first (EDF) and deadline monotonic (DM) scheduling in each processor, while minimizing the total workload on $m$ processors. Firstly, we only consider the feasibility aspect of the problem by proposing two polynomial time partitioning schemes, Temporal Consistency Partitioning under EDF ( $\mathsf TCP_{\mathsf{EDF}}$ ) and Temporal Consistency Partitioning under DM ( $\mathsf TCP_{\mathsf{DM}}$ ), and formally showing that the resource augmentation bounds of both $\mathsf TCP_{\mathsf{EDF}}$ and $\mathsf TCP_{\mathsf{DM}}$ are $({3 - \frac{1}{m}})$ . Secondly, we address the partition problem globally by proposing a polynomial time heuristic, Density factor Balancing Fit ( $\mathsf{DBF}$ ), where density factor balancing plays a major role in producing workload-efficient partitionings. Finally, we evaluate the feasibility and workload performances of $\mathsf{DBF}$ versus other heuristics with comparable quality experimentally.

Performance Based Improvement of the CPU by reducing the Real-Time Interrupt Latency using the PPTS Algorithm

The main objective of the research is to improve the performance of the CPU at software level by reducing the Interrupt Latency of the Real time systems. Interrupt Latency provides an important metric in increasing the performance of the Real Time Kernal. So far the research has been investigated with respect to real-time interrupt latency reduction using non-pre-emptive task scheduling algorithms. A general disadvantage of the non-preemptive discipline is that it introduces additional blocking time in higher priority tasks, so reducing schedulability. If the interrupt latency is increased, the task switching delay shall be increasing with respect to each task. Hence most of the research work has been focussed to reduce interrupt latency by using the Pre-emptive Task scheduling algorithm. Based on the literature survey, A Deadline Monotonic Priority Assignment technique is used to reduce the latency with respect to the deadline. Deferred pre-emption scheduling and Fixed pre-emptive scheduling algorithm are used to reduce the interrupt latencies based on the queue fixed to the priority of the tasks to be handled. Here we suggest a new algorithm named “Priority Preemptive task scheduling algorithm” which preempts and serve the task with highest priority and also concentrates on the low priority tasks during the buffering time of the higher priority tasks.

Response time analysis for fixed priority real-time systems with energy-harvesting

This paper introduces sufficient schedulability tests for fixed-priority pre-emptive scheduling of a real-time system under energy constraints. In this problem, energy is harvested from the ambient environment and used to replenish a storage unit or battery. The set of real-time tasks is decomposed into two different types of task depending on whether their rate of energy consumption is (i) more than or (ii) no more than the storage unit replenishment rate. We show that for this task model, where execution may only take place when there is sufficient energy available, the worst-case scenario does not necessarily correspond to the synchronous release of all tasks. We derive sufficient schedulability tests based on the computation of worst-case response time upper and lower bounds. We show that these tests are sustainable with respect to decreases in the energy consumption of tasks, and increases in the storage unit replenishment rate. Further, we show that Deadline Monotonic priority assignment is optimal with respect to the derived tests. We examine both the effectiveness and the tightness of the bounds, via an empirical investigation.

Task partitioning and priority assignment for distributed hard real-time systems

In this paper, we propose the Distributed using Optimal Priority Assignment (DOPA) heuristic that finds a feasible partitioning and priority assignment for distributed applications based on the linear transactional model. DOPA partitions the tasks and messages in the distributed system, and makes use of the Optimal Priority Assignment (OPA) algorithm known as Audsley's algorithm, to find the priorities for that partition. The experimental results show how the use of the OPA algorithm increases in average the number of schedulable tasks and messages in a distributed system when compared to the use of Deadline Monotonic (DM) usually favoured in other works. Afterwards, we extend these results to the assignment of Parallel/Distributed applications and present a second heuristic named Parallel-DOPA (P-DOPA). In that case, we show how the partitioning process can be simplified by using the Distributed Stretch Transformation (DST), a parallel transaction transformation algorithm introduced in [1].

A Review of Sufficient Schedulability Analysis for Fixed Priority Scheduling Systems

This papercarries out a survey of sufficient schedulability analysis forfixed priority (FP) scheduling. The most common used fixed priority assignment is the rate monotonic (RM) algorithm, according to its policy, the task priorities are ordered based on their activation rates, so that the task with the shortest period is assigned the highest priority. However, when each task’s relative deadline is not equal to its period, the RM algorithm is not suitable to assign the task priorities. When relative deadlines are less than or equal to periods,the deadline monotonic (DM) algorithm can be deployed to schedule the tasks. The utilization based schedulability analysis has the advantage of simple implementation, and manyexisting schedulability analyses on uniprocessor are covered.

Simulation-based evaluations of DAG scheduling in hard real-time multiprocessor systems

The scheduling of parallel real-time tasks on multiprocessor systems is more complicated than the one of independent sequential tasks, specially for the Directed Acyclic Graph (DAG) model. The complexity is due to the structure of DAG tasks and the precedence constraints between their subtasks. The trivial DAG scheduling approach is to directly apply common real-time scheduling algorithms on DAGs despite their lack of compatibility with the parallel model. Another scheduling approach, which is called the stretching method, aims at transforming each parallel DAG task in the set into a collection of independent sequential threads that are easier to be scheduled. In this paper, we are interested in analyzing global preemptive scheduling of DAGs using both approaches by showing that they are not comparable when associated with Deadline Monotonic (DM) and Earliest Deadline First (EDF) scheduling algorithms. Then we use extensive simulations to evaluate their schedulability performance. To this end, we use our simulation tool YARTISS to generate random DAG tasks with many parameter variations so as to guarantee reliable experimental results.

Multi-layered scheduling of mixed-criticality cyber-physical systems

In this paper, we deal with the schedule synthesis problem of mixed-criticality cyber-physical systems (MCCPS), which are composed of hard real-time tasks and feedback control tasks. The real-time tasks are associated with deadlines that must always be satisfied whereas feedback control tasks are characterized by their Quality of Control (QoC) which needs to be optimized. A straight-forward approach to the above scheduling problem is to translate the QoC requirements into deadline constraints and then, to apply traditional real-time scheduling techniques such as Deadline Monotonic (DM). In this work, we show that such scheduling leads to overly conservative results and hence is not efficient in the above context. On the other hand, methods from the mixed-criticality systems (MC) literature mainly focus on tasks with different criticality levels and certification issues. However, in MCCPS, the tasks may not be fully characterized by only criticality levels, but they may further be classified according to their criticality types, e.g., deadline-critical real-time tasks and QoC-critical feedback control tasks. On the contrary to traditional deadline-driven scheduling, scheduling MCCPS requires to integrate both, deadline-driven and QoC-driven techniques which gives rise to a challenging scheduling problem. In this paper, we present a multi-layered schedule synthesis scheme for MCCPS that aims to jointly schedule deadline-critical, and QoC-critical tasks at different scheduling layers. Our scheduling framework (i) integrates a number of QoC-oriented metrics to capture the QoC requirements in the schedule synthesis (ii) uses arrival curves from real-time calculus which allow a general characterization of task triggering patterns compared to simple task models such as periodic or sporadic, and (iii) has pseudo-polynomial complexity. Finally, we show the applicability of our scheduling scheme by a number of experiments.

Improved Deadline Monotonic Scheduling With Dynamic and Intelligent Time Slice for Real-time Systems

In this paper the deadline-monotonic scheduling algorithm is improved to schedule processes in real-time systems. In real-time systems each task should have deadline that is greater than execution time and less than time period. Failure to meet the deadline in real-time systems degrades the system’s performance. The proposed algorithm ensures that the processes meet their deadlines using iterative calculations using the exact schedulability test to determine which processes are schedulable. After finding the schedulable processes, they are scheduled using a suitable scheduling algorithm. Simple Round Robin scheduling algorithm shows high context switching and higher waiting time and response time. An improved-RR algorithm is proposed which calculates intelligent time slice for individual processes and taking dynamic time quantum into account. A comparative study is made to observe the interference due to higher priority processes and it was found that the proposed algorithm performs better than [1].

Efficient computation of response time bounds for preemptive uniprocessor deadline monotonic scheduling

The deadline-monotonic (DM) scheduling of sporadic task systems upon a preemptive uniprocessor is considered. A technique is derived for determining upper bounds on the response time of the jobs of each task, when a constrained-deadline sporadic task system is scheduled. This technique yields a generalization to a load-based sufficient schedulability condition for DM, the generalization being the added ability to account for blocking in the presence of non-preemptable serially re-usable resources.