Rate Monotonic Research Articles

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.

Removing bias from the judgment day: A Ravenscar-based toolbox for quantitative comparison of EDF-to-RM uniprocessor scheduling

Since 1973, Earliest Deadline First (EDF) uniprocessor scheduling has been known to be superior to its Rate Monotonic (Fixed-Priority, FPS) counterpart for its capacity to feasibly sustain full utilization, which FPS can only achieve in certain favorable circumstances. In 2005, further research results illustrated EDF’s superiority in a number of finer-grained performance indicators, such as preemption count, release and response-time jitter, and resilience to transient over+load. Recent work allowed EDF uniprocessor scheduling to be used in place of default FPS in Ada Ravenscar systems with unchanged application code: this development provides an interesting and practical avenue for empirical EDF-to-RM performance comparisons in an especially lean and efficient embedded setting. We leveraged that opportunity to develop a toolbox that enables quantitative per-application comparison of EDF-to-RM uniprocessor scheduling on common embedded targets, using lightweight run-time tracing and offline analytic. Using that toolbox, we performed a vast range of experiments to examine how EDF and RM uniprocessor scheduling operate quantitatively in situations of interest to system design, at a level much deeper than classic utilization tests and their ramifications. Interestingly, the surge of attention around the Edge Computing paradigm makes simple yet efficient and predictable embedded technology that targets Edge-friendly processors, such as our Ravenscar runtime, especially attractive for future use.

Relative Deadline Analysis in Multitasking RTS using RM & EDF Scheduling

In embedded systems the time required for any process to complete its execution in multitasking environment is an important factor to understand the performance of Real Time System (RTS) and its ability fulfill the deadline requirement of each process under different process load conditions. Even though some non-critical systems provide flexibility over deadlines, the hard real time systems are to be designed to meet the deadline requirement of all processes under peak process load condition. The number of processes available in scheduling queue may vary with time, the dynamic load on processing unit also changes proportionately which in turn affects the relative deadlines of each process. The scheduling policies considered are widely used scheduling policies like Rate Monotonic (RM) and Earliest Deadline First (EDF) to analyze and understand the impact on relative deadline with respect to number of scheduled processes. The real time execution timings of each process is observed on Raspberry Pi 3b+ processing unit operating at standard frequency of 700 MHz in multitasking mode of operation. The results obtained will decisively conclude the suitable scheduling policy for a set of processes under different process load conditions.

Memory-Aware Scheduling Parallel Real-Time Tasks for Multicore Systems

Shared resources on the multicore chip, such as main memory, are increasingly becoming a point of contention. Traditional real-time task scheduling policies focus on solely on the CPU, and do not take in account memory access and cache effects. In this paper, we propose parallel real-time tasks scheduling (PRTTS) policy on multicore platforms. Each set of tasks is represented as a directed acyclic graph (DAG). The priorities of tasks are assigned according to task periods Rate Monotonic (RM). Each task is composed of three phases. The first phase is read memory stage, the second phase is execution phase and the third phase is write memory phase. The tasks use locks and critical sections to protect data access. The global scheduler maintains the task pool in which tasks are ready to be executed which can run on any core. PRTTS scheduling policy consists of two levels: the first level scheduling schedules ready real-time tasks in the task pool to cores, and the second level scheduling schedules real-time tasks on cores. Tasks can preempt the core on running tasks of low priority. The priorities of tasks which want to access memory are dynamically increased above all tasks that do not access memory. When the data accessed by a task is in the cache, the priority of the task is raised to the highest priority, and the task is scheduled immediately to preempt the core on running the task not accessing memory. After accessing memory, the priority of these tasks is restored to the original priority and these tasks are pended, the preempted task continues to run on the core. This paper analyzes the schedulability of PRTTS scheduling policy. We derive an upper-bound on the worst-case response-time for parallel real-time tasks. A series of extensive simulation experiments have been performed to evaluate the performance of proposed PRTTS scheduling policy. The results of simulation experiment show that PRTTS scheduling policy offers better performance in terms of core utilization and schedulability rate of tasks.

Comprehensive study of schedulability tests and optimal design for rate-monotonic scheduling

Schedulability analysis of the rate-monotonic (RM) scheduling algorithm is an important research field in real-time systems. New dimensions in RM have been explored, ranging from faster schedulability tests to the optimal design of real-time systems. Recently, researchers have extended the schedulability problem to a more generalized case: the rate-monotonic optimal-design problem (RM-ODP), wherein the execution time is limited to an interval rather than a sole point. The RM-ODP processes task-execution-time adjustment such that (i) the system is RM schedulable and (ii) certain system performances (e.g., processor utilization) are optimized. In this paper, we review RM scheduling and summarize three aspects of the related research: (1) evaluate existing relevant feasibility tests by categorizing them based on inexact or exact conditions; (2) comprehensively review RM-ODP, summarize associated advantages and disadvantages, and provide some recommendations on schedulability tests to achieve the optimal system design under RM-scheduling policies; and (3) propose a hybrid search method based on linear programming with respect to both depth-first search and breadth-first search using the dynamic pruning strategy.

Energy Reduction Through Memory Aware Real-Time Scheduling on Virtual Machine in Multi-Cores Server

Not only weighty energy usage pose issues for the environment, but it also raises server maintenance costs in data centers. The massive task with the various power control functions in computer components was made to minimize energy consumption. Increasing consumption of energy in data server environments means that data centers will have high maintenance costs. Various geo-distributed data centers are starting to grow in an age of data proliferation and information growth. Energy management for servers is now demanded for technological, environmental, and economic reasons. In this environment, the main memory is a major energy consumer, not less than the processor. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping Virtual Machine (VM) resources to the Main Memory (MM) demands to achieve good performance by minimizing the energy consumption within a certain limit is a huge challenge. This paper simulates energy-efficient task scheduling algorithms in a heterogeneous virtualized environment using real-time virtual machine scheduling to resolve the issue of energy consumption. Using a simulator Real-Time system SIMulator (RTSIM), several hardware-based scheduling algorithms are implemented to observe VM memory scheduling efficiency to save memory energy. The simulation results show that, compared to current energy-efficient scheduling methods Rate Monotonic (RM), Earliest-Deadline-First (EDF), and Least-Laxity-First (LLF), helps to reduce energy consumption and improve performance. It is also observed that memory-aware energy management architecture reduces energy and memory consumption efficiently by using EDF scheduling algorithms. In particular, EDF saves approximately 58.3 percent of memory energy than conventional systems that cannot benefit from memory-aware energy management algorithms. The energy efficiency of the algorithms continues to improve as the level of server consolidation rises. We also implemented the EDF scheduling algorithm in Xen’s Credit scheduler to see if the simulation outcomes can be simulated on physical systems. Results of simulation and deployment are equated, and comparable outcomes are achieved. We also identified that shared memory between virtual machines deliberately affects memory’s energy consumption based on the implementation.

An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres

Datacentres provide the foundations for cloud computing, but require large amounts of electricity for their operation. Approaches that promise to reduce power use by minimizing execution time, for example using different scheduling and resource management techniques, are discussed in the literature. This paper summarizes some of the most important scheduling techniques in clouds focusing on power consumption, covering VM-level, host-level and task-level scheduling where the most promising approach is task level scheduling, with energy savings by means of load filtering, consolidation, adapted CPU throughput, or host power control. We explore use of the rate monotonic (RM) and backfilling algorithms for real-time task scheduling in cloud environment because RM is the simplest fixed priority scheduling technique, and thus the choice for modern real-time systems, and prior uses of RM in task scheduling have demonstrated power efficiency with optimal results. We specifically consider deadline-based tasks scheduling for real-time clouds which, to the best of our knowledge, has not been employed previously. RM with backfilling is experimentally evaluated and results show that, compared to the classical algorithms, all tasks were scheduled with minimum power consumption (5.5% - 29.3%), on minimum resources (3.9% - 25.2% less) while majority were meeting their deadlines (93.21% - 94.7%). The approach can guarantee deadline oriented Software as a Service (SaaS) in cloud if arrival rate i.e. network transfer time can be estimated in advance. We subsequently provided an extension of the proposed approach to task-based load balancing for almost balanced resource utilization and approximately 1.0% to 1.6% energy efficiency.

Hybrid Scheduler (S_LST) for Soft Real-Time System based on Static and Dynamic Algorithm

In the Soft Real-Time System scheduling process with the processor is a critical task. The system schedules the processes on a processor in a time interval, and hence the processes get chance to executes on the processor. Priority-driven scheduling algorithms are sub-categorized into mainly two categories called Static Priority and Dynamic Priority Scheduler. Critical Analysis of more static and dynamic priority scheduling algorithms have been discussed in this paper. This paper has covered the static priority algorithms like Rate Monotonic (RM) and Shortest Job First (SJF) and the dynamic priority algorithms like Earliest Deadline First (EDF) and Least Slack Time First (LST). These all algorithms have been analyzed with preemptive process set and this paper has considered all the process set are periodic. This paper has also proposed a hybrid approach for efficient scheduling. In a critical analysis, it has been observed that while scheduling in underload situation dynamic priority algorithms perform well and even EDF also make sure that all process will meet their deadline. However, in an overload situation, the performance of dynamic priority algorithms reduce quickly, and most of the task will miss its deadline, whereas static priority scheduling algorithms miss a few deadlines, even it is possible to schedule all processes in underload situation, whereas in an overload situation, the static algorithms perform well compared to the dynamic scheduler. This paper is proposing one Hybrid algorithm call S_LST which uses the concept of LST and SJF scheduling algorithm. This algorithm has been applied to the periodic task set, and observations are registered. We have observed the Success Ratio (SR) & Effective CPU Utilization (ECU) and compared all algorithms in the same conditions. It is noted that instead of using LST and SJF as an independent algorithm, Hybrid algorithm S_LST performs well in underload and overload scenario. Practical investigations have been led on a huge dataset. Data Set consists of the 7000+ process set, and each process set has one to nine processes and load varies between 0.5 to 5. It has been tried on 500-time unit to approve the rightness everything being equal.

Weakly-hard Real-time Guarantees for Earliest Deadline First Scheduling of Independent Tasks

The current trend in modeling and analyzing real-time systems is toward tighter yet safe timing constraints. Many practical real-time systems can de facto sustain a bounded number of deadline-misses, i.e., they have Weakly-Hard Real-Time (WHRT) constraints rather than hard real-time constraints. Therefore, we strive to provide tight Deadline Miss Models (DMMs) in complement to tight response time bounds for such systems. In this work, we bound the distribution of deadline-misses for task sets running on uniprocessors using the Earliest Deadline First (EDF) scheduling policy. We assume tasks miss their deadlines due to transient overload resulting from sporadic jobs, e.g., interrupt service routines. We use Typical Worst-Case Analysis (TWCA) to tackle the problem in this context. Also, we address the sources of pessimism in computing DMMs, and we discuss the limitations of the proposed analysis. This work is motivated by and validated on a realistic case study inspired by industrial practice (satellite on-board software) and on a set of synthetic test cases. The synthetic experiment is dedicated to extensively study the impact of EDF on DMMs by presenting a comparison between DMMs computed under EDF and Rate Monotonic (RM). The results show the usefulness of this approach for temporarily overloaded systems when EDF scheduling is considered. They also show that EDF is especially useful for WHRT tasks.

Fault-Tolerant Energy-Aware Task Scheduling on Multiprocessor System for Fixed-Priority Real-Time Tasks

Energy-aware real-time scheduling is gaining attention in recent years owing to environmental concerns and applications in numerous fields. System reliability also gets affected adversely with increasing energy dissipations posing serious challenges before the researchers. Keeping these in view, in recent times researchers have diverted to combining issues of fault-tolerance and energy efficiency. In literature, DVFS and DPM, most commonly used techniques for power management in task scheduling, are often combined with Primary/Backup technique to achieve fault tolerance against transient and permanent faults. Optimal algorithms, Earliest deadline first (EDF) and Rate-Monotonic (RM), meant for scheduling dynamic and fixed priority tasks respectively, have mainly been analyzed using a dual-processor approach for fault-tolerance and energy efficiency. In this paper, to handle higher workload of fixed-priority real-time tasks, energy-aware fault-tolerant scheduling algorithms are proposed for multiprocessor systems with balanced and unbalanced number of main and auxiliary processors. Simulations over extensive task-sets indicate that balanced approach is more energy-efficient than the unbalanced one.

A Task Parameter Inference Framework for Real-Time Embedded Systems

While recent studies addressed security attacks in real-time embedded systems, most of them assumed prior knowledge of parameters of periodic tasks, which is not realistic under many environments. In this paper, we address how to infer task parameters, from restricted information obtained by simple system monitoring. To this end, we first develop static properties that are independent of inference results and therefore applied only once in the beginning. We further develop dynamic properties each of which can tighten inference results by feeding an update of the inference results obtained by other properties. Our simulation results demonstrate that the proposed inference framework infers task parameters for RM (Rate Monotonic) with reasonable tightness; the ratio of exactly inferred task periods is 95.3% and 65.6%, respectively with low and high task set use. The results also discover that the inference performance varies with the monitoring interval length and the task set use.

System level fixed priority energy management algorithm for embedded real time application

Dynamic power management (DPM) and dynamic voltage scaling (DVS) are crucial techniques to reduce the energy consumption in embedded real-time systems. Many previous studies have focused on the energy consumption of the processor or I/O devices. In this paper, we focus on the problem of energy management integrating DVS and DPM techniques for periodic embedded real-time applications with rate monotonic (RM) policy and present a system level fixed priority energy-efficient scheduling (SLFPEES) algorithm. The SLFPEES algorithm consists of I/O device scheduling and job scheduling. I/O device scheduling is based on the dynamic power management with rate monotonic (DPM-RM) policy which puts devices into the sleep state when the idle interval is larger than devices break even time. Job scheduling is based on the RM policy and uses stack resource protocol (SRP) to guarantee exclusive access to the shared resources. For energy efficiency, the SLFPEES algorithm schedules the task with a lower speed and a higher speed. The experimental result shows that the SLFPEES algorithm can yield significantly energy savings with respect to the existing techniques.

Optimal harmonic period assignment: complexity results and approximation algorithms

Harmonic periods have wide applicability in industrial real-time systems. Rate monotonic (RM) is able to schedule task sets with harmonic periods up to 100% utilization. Also, if there is no release jitter and execution time variation, RM and EDF generate the same schedule for each instance of a task. As a result, all instances of a task are interfered by the same amount of workload. This property decreases the jitters that happen during sampling and actuation of the tasks, and hence, it increases the quality of service in control systems. In this paper, we consider the problem of optimal period assignment where the periods are constrained to be harmonic and the task set is required to be feasible. We study two variants of this problem. In the first one, the objective is to maximize the system utilization, while in the second one, the goal is to minimize the total weighted sum of the periods. First, we assume that an interval is determined a priori for each task from which its period can be selected. We show that both variants of the problem are (at least) weakly NP-hard. This is shown by reducing the NP-complete number partitioning problem to the mentioned harmonic period assignment problems. Afterwards, we consider a variant of the second problem in which the periods are not restricted to a special interval. We present two approximation algorithms with polynomial-time complexity for this problem and show that the maximum relative error of these algorithms is bounded by a factor of 1.125. Our evaluations show that, on the average, results of the approximation algorithms are very close to an optimal solution.

Formal Verification of a Timing Enforcer Implementation

A timing enforcer is a scheduler that not only allocates CPU cycles to threads, but also uses timers to enforce time budgets. An approach for verifying safety properties of timing enforcers at the source code level is presented. We assume that the enforcer is implemented as a set of “enforcer” functions that are executed atomically on critical system-level events, such as the arrival and departure of jobs, and triggering of timers. The key idea is to express the safety property as an invariant, and prove that it is inductive across all the enforcer functions. A formal semantics of timing enforcers is presented, including the semantics of functions used to read the system clock and set timers. Using this semantics, the verification approach is presented, and its soundness proved. Further, the approach also takes into consideration the periodicity of tasks. It is validated by proving the correctness of the enforcement of CPU cycle budgets for tasks by the Zero-Slack Rate Monotonic ( zsrm ) scheduler, which is implemented in C as a Linux kernel module. The inductiveness of the necessary zsrm invariants is proved by expressing them as function contracts using the acsl specification language, and verifying the contracts using the frama-c tool.

Towards optimal solutions for the low power hard real-time task allocation on multiple heterogeneous processors

The usage of heterogeneous multicore platforms is appealing for applications, e.g. hard real-time systems, due to the potential reduced energy consumption offered by such platforms. However, even in such platforms the power wall phenomena still imposes limits to performance. Hard real-time systems are part of life critical environments and reducing the energy consumption on such systems is an onerous and complex process. We tackle the problem from the perspective of different representative integer programming mathematical formulations and their interplay on the search for optimal solutions for Rate Monotonic (RM) and Earliest Deadline First (EDF) scheduling algorithms. The proposed models are based on a well-established formulation in the operational research literature, namely, the Multilevel Generalized Assignment Problem (MGAP). This paper, therefore, assesses the problem of finding optimal allocations and frequency assignments of hard real-time tasks among heterogeneous processors targeting low power consumption, but taking into account timing constraints. Computational experiments show that finding optimal solutions reduces the estimated energy consumption of the evaluated cases when compared to state-of-the-art algorithms. • Representative theoretical models of several hard real-time scheduling policies. • New application of well-known classical combinatorial optimization problem: MGAP. • Applicability on embedded systems with low power and hard real-time requirements. • Optimal workload partitioning on heterogeneous processors in project time. • Computational experiments on finding optimal solutions for the proposed models.

Schedulability Analysis for Real-Time Task Set on Resource with Performance Degradation and Dual-Level Periodic Rejuvenations

Researches in real-time scheduling often assume that the performance of a computing resource does not change over time. However, as system softwares and system architectures become increasingly complex, resource performance degradation over time becomes more evident. In this paper, we study the schedulability of a hard real-time task set on a resource which has performance degradation over time with a known pattern and use both cold and warm periodic rejuvenations as countermeasures. Such resource model is referred to as $P^2D$ -resource model for p erformance degradation and p eriodic rejuvenation with d ual-levels. In this paper, we study (1) the formal specification of the $P^2D$ -resource model, (2) $P^2D$ -resource supply analysis, and (3) task set utilization bounds of a $P^2D$ -resource under Earliest Deadline First (EDF) and Rate Monotonic (RM) scheduling policies.

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.

Minimum energy consumption for rate monotonic scheduled tasks

Limited battery power is a typical constraint in stand-alone embedded systems. One way to extend the battery lifetime is by reducing CPU power consumption. Because of the quadratic relationship between power consumption in CMOS circuits and CPU voltage, power reduction can be obtained by scaling down supply voltage, or dynamic voltage scaling. However, reducing supply voltage slows down CPU speed since supply voltage has a proportional relationship with CPU frequency. On the other hand, in any real-time embedded environment (especially hard real-time), timing constraints are critical. In this paper, we focus on dynamic energy reduction of tasks scheduled by rate monotonic (RM) algorithm in a hard real-time embedded environment. The RM algorithm preemptively schedules any set of periodic tasks by assigning higher priorities to frequent tasks. For any periodic task set that satisfies the CPU utilization bound, we determine the provably optimal scaling of the worst-case execution time of each task that consumes minimum dynamic energy while satisfying the utilization bound. As RM algorithm is widely used, we expect this work can lead to better energy reduction management and expectations.

A Note on the EDF Preemption Behavior in “Rate Monotonic Versus EDF: Judgment Day”

In G. C. Buttazzo, Real-Time Syst., vol. 29, no. 1, pp. 5–26, 2005, the author empirically compared earliest deadline first (EDF) and rate monotonic (RM) scheduling algorithms and made a few EDF preemption behavior observations based on data obtained from the first 1000 time units of scheduling activities. However, based on test settings given in (G. C. Buttazzo, Real-Time Syst., vol. 29, no. 1, pp. 5–26, 2005), the first 1000 time units occupies only a small percentage of the entire task set’s hyper-period. We extend EDF preemption behavior study by extending scheduling activities from the first small percentage of a hyper-period of a given task to the entire hyper-period. The extended experiments indicate that the number of preemptions occurred at the beginning of a task set’s hyper-period does not necessarily represent the trend for the entire hyper-period. Hence, comparisons and conclusions made based on a small percentage of a scheduling interval over a task set’s hyper-period may not be accurate. Second, the total number of preemptions within a task set’s hyper-period does not decrease when the task set total utilization increases which is different from the observation obtained in (G. C. Buttazzo, Real-Time Syst., vol. 29, no. 1, pp. 5–26, 2005). We also investigate the impact of execution time differences among tasks on the preemption behavior.