Scheduling Of Periodic Real-time Tasks Research Articles

Enhanced Harmonic Partitioned Scheduling of Periodic Real-Time Tasks Based on Slack Analysis.

The adoption of multiprocessor platforms is growing commonplace in Internet of Things (IoT) applications to handle large volumes of sensor data while maintaining real-time performance at a reasonable cost and with low power consumption. Partitioned scheduling is a competitive approach to ensure the temporal constraints of real-time sensor data processing tasks on multiprocessor platforms. However, the problem of partitioning real-time sensor data processing tasks to individual processors is strongly NP-hard, making it crucial to develop efficient partitioning heuristics to achieve high real-time performance. This paper presents an enhanced harmonic partitioned multiprocessor scheduling method for periodic real-time sensor data processing tasks to improve system utilization over the state of the art. Specifically, we introduce a general harmonic index to effectively quantify the harmonicity of a periodic real-time task set. This index is derived by analyzing the variance between the worst-case slack time and the best-case slack time for the lowest-priority task in the task set. Leveraging this harmonic index, we propose two efficient partitioned scheduling methods to optimize the system utilization via strategically allocating the workload among processors by leveraging the task harmonic relationship. Experiments with randomly synthesized task sets demonstrate that our methods significantly surpass existing approaches in terms of schedulability.

Energy-aware primary/backup scheduling of periodic real-time tasks on heterogeneous multicore systems

For real-time embedded systems, energy management and fault tolerance are both critical. However these two objectives are often at odds, because extra resources needed to tolerate faults significantly increase the energy consumption. In this paper, we consider energy-aware and fault-tolerant scheduling of periodic real-time tasks. Our target platform is a heterogeneous multicore system. We reduce the energy consumption by both applying DVFS to scale the primary tasks, and maximizing the opportunities to cancel the back-up tasks in fault-free execution scenarios. To tolerate both transient and permanent faults, primary and backup copies of tasks are scheduled on different cores. Our framework consists of offline and online phases to manage energy and fault-tolerant scheduling of periodic tasks in tandem. The latter objective is achieved through an explicit task priority assignment phase, coupled with a dual queue based back-up delaying algorithm. In particular, we propose a scheme called Reverse Preference-Oriented Priority Assignment (RPPA) which is experimentally shown to be very effective to reduce the energy consumption. RPPA, when coupled with the dual-queue based delaying algorithm, outperforms other schemes and approaches the energy performance of a theoretical lower bound. All the proposed schemes satisfy the stringent timing and fault tolerance requirements of periodic real-time tasks while managing the energy consumption dynamically.

Improved Multi-Core Real-Time Task Scheduling of Reconfigurable Systems With Energy Constraints

This paper deals with the scheduling of real-time periodic tasks executed on heterogeneous multicore platforms. Each processor is composed of a set of multi-speed cores with limited energy resources. A reconfigurable system is sensible to unpredictable reconfiguration events from related environment, such as the activation, removal or update of tasks. The problem is to handle feasible reconfiguration scenarios under energy constraints. Since any task can finish execution before achieving its worst-case execution time (WCET), the idea is to distribute this execution on different processor cores for meeting related deadlines and reducing energy consumption. The methodology consists in using lower processor speeds first to consume less energy. If the system is still non-feasible after reconfiguration, then we adjust the task periods as a flexible solution or migrate some of them to the least loaded processors. Accordingly, an integer linear program (ILP) is formulated to encode the execution model that assigns tasks to different cores with optimal energy consumption, thereby realizing energy-efficient computing/green computing. The potency and effectiveness of the proposed approach are rated through simulation studies. By measuring the energy consumption cost, our solution offers better than 11% of gain than recently published methods and improves by 85% the overall number of adjusted periods.

Investigating the Schedulability of Periodic Real-Time Tasks in Virtualized Cloud Environment

In this paper, we developed a computing architecture and algorithms for supporting soft real-time task scheduling in a cloud computing environment through the dynamic provisioning of virtual machines. The architecture integrated three modified soft real-time task scheduling algorithms, namely, Earliest Deadline First, Earliest Deadline until Zero-Laxity, and Unfair Semi-Greedy. A deadline look-a-head module was incorporated into each of the algorithms to fire deadline exceptions and avoid the missing deadlines, and to maintain the system criticality. The results of the implementation of the proposed algorithms are presented in this paper in terms of the average deadline exceptions, the extra resources consumed by each algorithm in handling deadline exceptions, and the average response time. The results not only suggest the feasibility of the soft real-time scheduling of periodic real-time tasks in cloud computing but that the process can also be scaled up to handle the near-hard real-time task scheduling.

Weakly Hard Schedulability Analysis for Fixed Priority Scheduling of Periodic Real-Time Tasks

The hard deadline model is very popular in real-time research, but is representative or applicable to a small number of systems. Many applications, including control systems, are capable of tolerating occasional deadline misses, but are seriously compromised by a repeating pattern of late terminations. The weakly hard real-time model tries to capture these requirements by analyzing the conditions that guarantee that a maximum number of deadlines can be possibly missed in any set of consecutive activations. We provide a new weakly hard schedulability analysis method that applies to constrained-deadline periodic real-time systems scheduled with fixed priority and without knowledge of the task activation offsets. The analysis is based on a Mixed Integer Linear Programming (MILP) problem formulation; it is very general and can be adapted to include the consideration of resource sharing and activation jitter. A set of experiments conducted on an automotive engine control application and randomly generated tasksets show the applicability and accuracy of the proposed technique.

Optimal Priority-Free Conditionally-Preemptive Real-Time Scheduling of Periodic Tasks Based on DES Supervisory Control

This paper presents a general discrete-event system (DES)-based hard periodic real-time task model. Based on supervisory control theory (SCT), an optimal priority-free real-time scheduling technique is proposed to process all the tasks running in uniprocessor or multiprocessor real-time systems (RTS). The preemption relation in this paper generalizes priority-based preemption. First, regular languages are utilized to describe the processor behavior related to each task’s execution. Thereafter, the languages are represented by DES generators. Finally, the global processor behavior is generated as the synchronous product of these DES generators. By discarding the priorities, a novel preemption policy, namely conditional-preemption, is developed. Two sets of conditional-preemption specifications are developed, on the processor level and task level, respectively. Moreover, in order to control the system to be nonblocking and also limit the worst-case response time of the tasks, two corresponding sets of specifications are presented. After generating the global specification as the synchronous product, by implementing SCT the calculated supervisor can provide all the safe real-time execution sequences. The supervisor calculation can be sped up by a three-step algorithm. Finally, the real-time scheduling is implemented for real-world examples.

Preference-oriented fixed-priority scheduling for periodic real-time tasks

A preference priority assignment (PPA) scheme that explicitly incorporates the ASAP and ALAP execution preferences of periodic real-time tasks is proposed.An online dual-queue based preference-oriented fixed- priority (POFP) scheduler is proposed 4, which is preemptive and non-work-conserving in nature to better serve the tasks ASAP and ALAP execution preferences.Runtime techniques are also investigated to further delay (expedite) the executions of ALAP (ASAP) tasks by exploiting system slack at runtime.The proposed PPA scheme, POFP scheduler and runtime techniques are evaluated through extensive simulations with synthetic tasks, which are shown to be very effective to fulfill the tasks ASAP and ALAP execution requirements, when compared to that of the preference- oblivious RMS scheduler. Traditionally, real-time scheduling algorithms prioritize tasks solely based on their timing parameters and cannot effectively handle tasks that have different execution preferences. In this paper, for a set of periodic real-time tasks running on a single processor, where some tasks are preferably executed as soon as possible (ASAP) and others as late as possible (ALAP), we investigate Preference-Oriented Fixed-Priority (POFP) scheduling techniques. First, based on Audsley's Optimal Priority Assignment (OPA), we study a Preference Priority Assignment (PPA) scheme that attempts to assign ALAP (ASAP) tasks lower (higher) priorities, whenever possible. Then, by considering the non-work-conserving strategy, we exploit the promotion times of ALAP tasks and devise an online dual-queue based POFP scheduling algorithm. Basically, with the objective of fulfilling the execution preferences of all tasks, the POFP scheduler retains ALAP tasks in the delay queue until their promotion times while putting ASAP tasks into the ready queue right after their arrivals. In addition, to further expedite (delay) the executions of ASAP (ALAP) tasks using system slack, runtime techniques based on dummy and wrapper tasks are investigated. The proposed schemes are evaluated through extensive simulations. The results show that, compared to the classical fixed-priority Rate Monotonic Scheduling (RMS) algorithm, the proposed priority assignment scheme and POFP scheduler can achieve significant improvement in terms of fulfilling the execution preferences of both ASAP and ALAP tasks, which can be further enhanced at runtime with the wrapper-task based slack management technique.

Fuzzy logic based adaptive hierarchical scheduling for periodic real-time tasks

In this paper, we present a new scheduling approach for real-time tasks in an embedded system. Our method utilizes hierarchical scheduling to provide a resource based allocation scheme while using a fuzzy logic based feedback scheduler to react to environmental changes within the application. The primary goal is to provide a scheduling mechanism that can adapt to overload conditions but still present a level of service while enforcing the temporal isolation between independent applications. The scheduler then considers this level of service to make scheduling decisions based upon a task's service requirements, such as criticality or timeliness. Implemented in VxWorks on a uniprocessor-based platform results show that our adaptive approach provides significant advantages, during overload conditions, over traditional fixed-priority scheduling schemes.

Energy efficient scheduling of real-time tasks on multi-core processors with voltage islands

This paper studies energy efficient scheduling of periodic real-time tasks on multi-core processors with voltage islands, in which cores are partitioned into multiple blocks (termed voltage islands) and each block has its own power source to supply voltage. Cores in the same block always operate at the same voltage level, but can be adjusted by using Dynamic Voltage and Frequency Scaling (DVFS). We propose a Voltage Island Largest Capacity First (VILCF) algorithm for energy efficient scheduling of periodic real-time tasks on multi-core processors. It achieves better energy efficiency by fully utilizing the remaining capacity of an island before turning on more islands or increasing the voltage level of the current active islands. We provide detailed theoretical analysis of the approximation ratio of the proposed VILCF algorithm in terms of energy efficiency. In addition, our experimental results show that VILCF significantly outperforms the existing algorithms when there are multiple cores in a voltage island.

Enhanced fixed-priority real-time scheduling on multi-core platforms by exploiting task period relationship

One common approach for multi-core partitioned scheduling problem is to transform this problem into a traditional bin-packing problem, with the utilization of a task being the “size” of the object and the utilization bound of a processing core being the “capacity” of the bin. However, this approach ignores the fact that some implicit relations among tasks may significantly affect the feasibility of the tasks allocated to each local core. In this paper, we study the problem of partitioned scheduling of periodic real-time tasks on multi-core platforms under the Rate Monotonic Scheduling (RMS) policy. We present two effective and efficient partitioned scheduling algorithms, i.e. PSER and HAPS, by exploiting the fact that the utilization bound of a task set increases as task periods are closer to harmonic on a single-core platform. We formally prove the schedulability of our partitioned scheduling algorithms. Our extensive experimental results demonstrate that the proposed algorithms can significantly improve the scheduling performance compared with the existing work.

Energy-Efficient Scheduling of Periodic Real-Time Tasks on Lightly Loaded Multicore Processors

For lightly loaded multicore processors that contain more processing cores than running tasks and have dynamic voltage and frequency scaling capability, we address the energy-efficient scheduling of periodic real-time tasks. First, we introduce two energy-saving techniques for the lightly loaded multicore processors: exploiting overabundant cores for executing a task in parallel with a lower frequency and turning off power of rarely used cores. Next, we verify that if the two introduced techniques are supported, then the problem of minimizing energy consumption of real-time tasks while meeting their deadlines is NP-hard on a lightly loaded multicore processor. Finally, we propose a polynomial-time scheduling scheme that provides a near minimum-energy feasible schedule. The difference of energy consumption between the provided schedule and the minimum-energy schedule is limited. The scheme saves up to 64 percent of the processing core energy consumed by the previous scheme that executes each task on a separate core.

Predictive OS Modeling for Host-Compiled Simulation of Periodic Real-Time Task Sets

With the increasing complexity of embedded software, host-compiled simulators have been introduced to address the need for a fast simulation environment. However, designers pay the price for higher performance with a loss in timing accuracy. In this letter, we introduce a novel predictive OS model to provide fast software simulation with accurate scheduling of periodic real-time tasks. The OS model predicts the next preemption point by monitoring system state, and automatically and optimally adjusting the granularity of back-annotated delays. We evaluated our simulator on a range of periodic task sets. Our observations show that we can achieve the same 99% accuracy as a simulation at 1 μs granularity with an average 230× speedup.

Exact schedulability tests for real-time scheduling of periodic tasks on unrelated multiprocessor platforms

In this paper, we study the global scheduling of periodic task systems on unrelated multiprocessor platforms. We first show two general properties which are well known for uniprocessor platforms and which are also true for unrelated multiprocessor platforms: (i) under few and not so restrictive assumptions, we prove that feasible schedules of periodic task systems are periodic starting from some point in time with a period equal to the least common multiple of the task periods and (ii) for the specific case of synchronous periodic task systems, we prove that feasible schedules repeat from their origin. We then present our main result: we characterize, for task-level fixed-priority schedulers and for asynchronous constrained or arbitrary deadline periodic task models, upper bounds of the first time-instant where the schedule repeats. For task-level fixed-priority schedulers, based on the upper bounds and the predictability property, we provide exact schedulability tests for asynchronous constrained or arbitrary deadline periodic task sets. Finally, we provide an exact schedulability test as well for the job-level fixed-priority Earliest Deadline First (EDF) scheduler, for which such an upper bound is unknown.

T–L plane-based real-time scheduling for homogeneous multiprocessors

We consider optimal real-time scheduling of periodic tasks on multiprocessors—i.e., satisfying all task deadlines, when the total utilization demand does not exceed the utilization capacity of the processors. We introduce a novel abstraction for reasoning about task execution behavior on multiprocessors, called T–L plane and present T–L plane-based real-time scheduling algorithms. We show that scheduling for multiprocessors can be viewed as scheduling on repeatedly occurring T–L planes, and feasibly scheduling on a single T–L plane results in an optimal schedule. Within a single T–L plane, we analytically show a sufficient condition to provide a feasible schedule. Based on these, we provide two examples of T–L plane-based real-time scheduling algorithms, including non-work-conserving and work-conserving approaches. Further, we establish that the algorithms have bounded overhead. Our simulation results validate our analysis of the algorithm overhead. In addition, we experimentally show that our approaches have a reduced number of task migrations among processors when compared with a previous algorithm.

Real-time scheduling of periodic tasks with processing times and deadlines as parametric fuzzy numbers

Task scheduling is very important in real-time systems as it accomplishes the crucial goal of devising a feasible schedule of the tasks. However, the uncertainty associated with the timing constrains of the real-time tasks makes the scheduling problem difficult to formulate. This motivates the use of fuzzy numbers to model task deadlines and completion times. In this paper a method for intuitively defining smooth membership functions (MFs) for deadlines and execution times has been proposed using mixed cubic-exponential Hermite interpolation parametric curves. The effect of changes in parameterized MFs on the task schedulability and task priorities are also reported. A new technique is proposed based on the concept of dynamic slack calculation to make the existing model more practical and realistic. Examples are given to demonstrate the more satisfactory performance of the new technique.

An EDF schedulability test for periodic tasks on reconfigurable hardware devices

In this paper, we consider the scheduling of periodic real-time tasks on reconfigurable hardware devices. Such devices can execute several tasks in parallel. All executing tasks share the hardware resource, which makes the scheduling problem differ from single- and multiprocessor scheduling. We adapt the global EDF multiprocessor scheduling approach to the reconfigurable hardware execution model and define two preemptive scheduling algorithms, EDF-First-k-Fit and EDF-Next-Fit . For these algorithms, we present a novel linear-time schedulability test and give a proof based on a resource augmentation technique. Then, we propose a task placement and relocation scheme utilizing partial device reconfiguration. This scheme allows us to extend the schedulability test to include reconfiguration time overheads. Experiments with synthetic workloads compare the scheduling test with the actual scheduling performance of EDF-First-k-Fit and EDF-Next-Fit . The main evaluation result is that the reconfiguration overhead is acceptable if the task computation times are one order of magnitude higher than the device reconfiguration time.

Procrastination for leakage-aware rate-monotonic scheduling on a dynamic voltage scaling processor

As the dynamic voltage scaling (DVS) technique provides system engineers the flexibility to trade-off the performance and the energy consumption, DVS has been adopted in many computing systems. However, the longer a job executes, the more energy in the leakage current the device/processor consumes for the job. To reduce the energy consumption resulting from the leakage current, a system might enter the dormant mode. This paper targets energy-efficient rate-monotonic scheduling for periodic real-time tasks on a uniprocessor DVS system with non-negligible leakage power consumption. An on-line simulated scheduling strategy and a virtually blocking time strategy are developed for procrastination scheduling to reduce energy consumption. The proposed algorithms derive a feasible schedule for real-time tasks with worst-case guarantees for any input instance. Experimental results show that our proposed algorithms could derive energy-efficient solutions.

Power-aware scheduling for periodic real-time tasks

We address power-aware scheduling of periodic tasks to reduce CPU energy consumption in hard real-time systems through dynamic voltage scaling. Our intertask voltage scheduling solution includes three components: 1) a static (offline) solution to compute the optimal speed, assuming worst-case workload for each arrival, 2) an online speed reduction mechanism to reclaim energy by adapting to the actual workload, and 3) an online, adaptive and speculative speed adjustment mechanism to anticipate early completions of future executions by using the average-case workload information. All these solutions still guarantee that all deadlines are met. Our simulation results show that our reclaiming algorithm alone outperforms other recently proposed intertask voltage scheduling schemes. Our speculative techniques are shown to provide additional gains, approaching the theoretical lower-bound by a margin of 10 percent.

Optimal reward-based scheduling for periodic real-time tasks

Reward-based scheduling refers to the problem in which there is a reward associated with the execution of a task. In our framework, each real-time task comprises a mandatory and an optional part. The mandatory part must complete before the task's deadline, while a nondecreasing reward function is associated with the execution of the optional part, which can be interrupted at any time. Imprecise computation and Increased-Reward-with-Increased-Service models fall within the scope of this-framework. In this paper, we address the reward-based scheduling problem for periodic tasks. An optimal schedule is one where mandatory-parts complete in a timely manner and the weighted average reward is maximized. For linear and concave reward functions, which are most common, we 1) show the existence of an optimal schedule where the optional service time of a task is constant at every instance and 2) show how to efficiently compute this service time. We also prove the optimality of Rate Monotonic Scheduling (with harmonic periods), Earliest Deadline First, and Least Laxity First policies for the case of uniprocessors when used with the optimal service times we computed. Moreover, we extend our result by showing that any policy which can fully utilize all the processors is also optimal for the multiprocessor periodic reward-based scheduling. To show-that our optimal solution is pushing the limits of reward-based scheduling, we further prove that, when the reward functions are convex, the problem becomes NP-Hard. Our static optimal solution, besides providing considerable reward improvements over the previous suboptimal strategies, also has a major practical benefit. Run-time overhead is eliminated and existing scheduling disciplines may be used without modification with the computed optimal service times.