Periodic Tasks Research Articles

Utilization-Tensity Bound for Real-Time DAG Tasks under Global EDF Scheduling

Utilization bound is a well-known concept in real-time scheduling theory for sequential periodic tasks, which can be used both for quantifying the performance of scheduling algorithms and as efficient schedulability tests. However, the schedulability of parallel real time task graphs depends on not only utilization, but also another parameter tensity , the ratio between the longest path length and period. In this paper, we use utilization-tensity bounds to better characterize the schedulability of parallel real-time tasks. In particular, we derive utilization-tensity bounds for parallel DAG tasks under global EDF scheduling, which facilitate significantly more precise schedulability analysis than the state-of-the-art analysis techniques based on capacity augmentation bound and response time analysis. Moreover, we apply the above results to the federated scheduling paradigm to improve the system schedulability by choosing proper scheduling strategies for tasks with different workload and structure features.

Integer Programming Techniques for Static Scheduling of Hard Real-Time Systems

Hard real-time systems focus on obtaining a feasible schedule while satisfying different temporal requirements. In safety-critical applications, this schedule is generated offline. This article explores different integer linear programming techniques (ILP) to schedule uniprocessor hard real-time systems. The goal is to efficiently obtain a static schedule for periodic tasks and partitioned systems where temporal and spatial isolation is crucial. The advantage of the proposed ILP techniques is the possibility of choosing the optimization criteria so that deadlines are met and better performance quality is achieved. The drawback is the time spent finding an optimal solution. We propose an ILP method that reduces by 70% the time needed to obtain an optimal solution compared to basic approaches. This method is called the rolling task MILP approach and the optimization problem is addressed task by task. Experimental results show that our approach also achieves better results than heuristics when trying to reduce temporal parameters such as response times, context switches, and jitter. This makes our solution suitable for control systems and other applications.

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.

Lebesgue-Approximation Model Predictive Control of Nonlinear Sampled-Data Systems

This article studies discrete-time implementation of model predictive control (MPC) algorithms in continuous-time nonlinear sampled-data systems. We present a discrete-time and aperiodic nonlinear MPC algorithm to stabilize continuous-time nonlinear dynamics, based on the Lebesgue approximation model (LAM). In this LAM-based MPC (LAMPC), the sampling instants are triggered by a self-triggered scheme, and the predicted states and transition time instants in the optimal control problem are calculated in an aperiodic manner subject to the LAM. Sufficient conditions are derived on feasibility and stability of the resulting closed-loop systems. According to these conditions, the parameters in LAMPC are designed with the guarantee of exclusion of Zeno behavior. Meanwhile, it is shown that the periodic task model is a special case in our framework with appropriate choice of the parameters in the LAM. Simulation results indicate that LAMPC can dynamically adjust the computation periods and has the potential to reduce the computational costs compared with periodic approaches.

Task engagement as a mediator between the cognitive demands of sustained attention and musculoskeletal complaints: A structural equation modelling approach.

Cognitive demands in the form of sustained attention are prevalent in automated manufacturing where operators undertake monitoring tasks for prolonged periods. Sustained attention tasks are stressful and could have potential health consequences for employees including contributing to Work Related Musculoskeletal Disorders (WRMSDs). The study investigates if lowered task engagement (low task motivation/focus) is a mediator of the relationship between sustained attention and musculoskeletal complaints. A structural equation modelling technique was used to determine if task engagement mediates the relationship between attention demand/attention supply and self-reported musculoskeletal complaints (MSCs) in manufacturing employees (n = 235). Study participants were 5.16 times (OR, odds ratio) more likely to have neck complaints, 7.27 (OR) times more likely to have upper back complaints and 3.9 (OR) times more likely to report lower back complaints (OR 2.05) when attention demands were high and task engagement was low. When task engagement was introduced as a mediator between attention supply and MSCs, odds ratios decreased for neck (from 1.01 to 0.60), shoulder (from 0.95 to 0.47) (p < 0.01), upper back (from 1.01 to 0.70) (p < 0.01) and lower back (from 0.94 to 0.40)*. Task engagement was a significant mediator of the relationship between attention demand and neck, upper back and lower back musculoskeletal complaints for study participants.

HEARS: A heterogeneous energy-aware real-time scheduler

Developing energy-efficient schedulers for real-time heterogeneous platforms executing periodic tasks is an onerous as well as a computationally challenging problem. As a result, today we are confronting a scarcity of real-time energy-aware scheduling techniques which are applicable to heterogeneous platforms. Hence, this research proposes a heuristic strategy called, HEARS, for DVFS enabled energy-aware scheduling of a set of periodic tasks executing on a heterogeneous multicore system having an arbitrary number of core types. The presented scheme first applies deadline-partitioning to acquire a set of distinct time-intervals called frames. At any frame boundary, the following two-phase hierarchical operation is applied to obtain schedule for the next frame: First, it computes execution requirements for each task on every processing core of the platform. For any task, its execution requirement on a core within a frame depends upon the following criteria: (i) Length of the ensuing frame, (ii) Total execution requirements of the instance of the task on different cores (as a single task may have different execution requirement on different cores) and, (iii) Deadline of the instance of the task. Next, it simultaneously allocates each task on one or more cores and selects operating frequencies for the concerned cores such that the total execution demand of all the allocated tasks are satisfied as well as there is minimum change in energy consumption for the system. Experimental results show that our proposed strategy is not only able to achieve appreciable energy savings with respect to state-of-the-art MaxMin (2% to 37% on average) but also enables significant improvement in resource utilization (as high as 57%).

Online Slack-Stealing Scheduling with Modified laEDF in Real-Time Systems

In hard real-time task systems where periodic and aperiodic tasks coexist, the object of task scheduling is to reduce the response time of the aperiodic tasks while meeting the deadline of periodic tasks. Total bandwidth server (TBS) and advanced TBS (ATBS) are used in dynamic priority systems. However, these methods are not optimal solutions because they use the worst-case execution time (WCET) or the estimation value of the actual execution time of the aperiodic tasks. This paper presents an online slack-stealing algorithm called SSML that can make significant response time reducing by modification of look-ahead earliest deadline first (laEDF) algorithm as the slack computation method. While the conventional slack-stealing method has a disadvantage that the slack amount of each frame must be calculated in advance, SSML calculates the slack when aperiodic tasks arrive. Our simulation results show that SSML outperforms the existing TBS based algorithms when the periodic task utilization is higher than 60%. Compared to ATBS with virtual release advancing (VRA), the proposed algorithm can reduce the response time up to about 75%. The performance advantage becomes much larger as the utilization increases. Moreover, it shows a small performance variation of response time for various task environments.

ACO Based Scheduling Method for Soft RTOS with Simulation and Mathematical Proofs

The Ant Colony Optimization (ACO) algorithm is a mathematical model enlivened by the system searching conduct of ants. By taking a gander at the qualities of ACO, it is most suitable for scheduling of tasks in soft real-time systems. In this paper, the ACO based scheduling method for the soft real-time operating system (RTOS) has been profound with mathematical and practical proof. In Mathematical proof, three different Propositions and two Theorems have been given, which prove the correctness of the proposed algorithm. Practical experiments also support mathematical proofs. During the investigation, observations are gathered with different periodic task set. Algorithms have been observed regarding Success Ratio (SR) and Effective CPU utilization (ECU). ACO based scheduling algorithm has been compared with the Earliest Deadline First (EDF) algorithm with parameter SR and ECU. The EDF is dynamic scheduling algorithm and it is most suitable in RTOS when task set is preemptable. It is noted that the new algorithm is equally efficient during under loaded conditions when CPU load is less than one. ACO based scheduling algorithm performs superior during the overloaded conditions when CPU load is more than one where as EDF algorithm performance degraded in overload condition. Empirical study has been executed with a hefty Dataset consist of more than 7500 task set, and a set contains different one to nine processes where CPU load is dynamic for each process set and differ from 0.5 to 5. Algorithms have been executed on five-hundred-time unit for each process set to authenticate the accuracy of both algorithms.

SecMonQ: An HSM based security monitoring approach for protecting AUTOSAR safety-critical systems

Many attacks on vehicle systems that result in a safety hazard follow a general pattern in which ECU firmware is modified, or code is injected in order to send spoofed CAN messages to safety-critical components causing an unsafe driving situation. There is a general consensus that protecting vehicles requires a defense in depth approach where protections are added at each layer of the vehicle data architecture. At the vehicle CAN bus layer, two powerful countermeasures exist: message authentication/encryption and network intrusion detection systems. The two approaches assume that an attacker has already managed to reach the CAN bus and therefore attempt to limit his impact. To defend against CAN injection attacks, we propose an alternative approach which aims at stopping a CAN injection attack before it reaches the vehicle bus. The proposed approach, working at the ECU level, leverages the embedded hardware security module (HSM), available in modern automotive ECUs, to implement four security monitors (SecMonQ) that run within the HSM firmware. SecMonQ performs continuous monitoring activities of the ECU firmware integrity, communication peripherals, periodic task timing, and flow sequence of certain critical functions. It is designed to detect an active attack and bring the system back to a safe state within the safety defined fault tolerant time. We implement SecMonQ on an automotive development environment which consists of an Elektrobit AUTOSAR stack and a Renesas RH850 F1KM micro-controller. We evaluate SecMonQ against the CAN masquerading attack to demonstrate efficacy while maintaining compatibility with AUTOSAR.

Optimization of Big Data Scheduling in Social Networks

In social network big data scheduling, it is easy for target data to conflict in the same data node. Of the different kinds of entropy measures, this paper focuses on the optimization of target entropy. Therefore, this paper presents an optimized method for the scheduling of big data in social networks and also takes into account each task’s amount of data communication during target data transmission to construct a big data scheduling model. Firstly, the task scheduling model is constructed to solve the problem of conflicting target data in the same data node. Next, the necessary conditions for the scheduling of tasks are analyzed. Then, the a periodic task distribution function is calculated. Finally, tasks are scheduled based on the minimum product of the corresponding resource level and the minimum execution time of each task is calculated. Experimental results show that our optimized scheduling model quickly optimizes the scheduling of social network data and solves the problem of strong data collision.

Energy Efficient Data Forwarding Scheme in Fog-Based Ubiquitous System With Deadline Constraints

Ubiquitous Computing (UbiComp) is a computational paradigm that enhances the use of computing devices by making them available to the user anywhere and anytime. From the energy perspective, it is often very important to compute the entire UbiComp task within a specific deadline with minimum energy. The literature on determining the energy consumption of the system for computing the task does not consider periodic tasks and different sampling rate of the sensors, which eliminates the deadline constraints in the analysis. Since the period of the tasks is not fixed, the estimated delay without considering the fixed period is lower than the actual value. In this paper, we assume that an Edge, Fog, and Cloud layers based UbiComp system computes the periodic task within the specific deadline. We derive the expressions of total delay and energy consumption of the UbiComp system. Using the derived expressions, we estimate fractions of the task that are computed at each layer to reduce the energy consumption such that the task is computed within a specific deadline. Our numerical and prototype results demonstrate the impact of the data size, network topologies, deadline, and characteristics of the sensors on the energy consumption, delay, and accuracy of the system.

Hybrid scheduling to enhance reliability of real-time tasks running on reconfigurable devices

Reconfigurable devices (RDs) are extremely advantageous when employed in real-time embedded systems. Nonetheless, they are susceptible to soft errors. In a broad sense, the present research addresses the challenge of improving the reliability of independent periodic real-time hardware tasks in RDs by utilizing hybrid fault-tolerant scheduling. The current paper combines static and dynamic real-time scheduling techniques to improve the reliability of the system. First, the proposed algorithm statically schedules primary tasks and preserves area and time for possible backup tasks on the RD. The overlapping of passive backup tasks is possible. Next, at the run time, event-triggered dispatcher dynamically determines which candidate backup copy should be selected for configuration on the overloaded preserved areas. Reliability, task deadline, and RD area limitations are the determining factors of backup overloading in the static phase. On the other hand, in the dynamic phase, the execution result of the primary tasks—in this case, success or failure—is the deciding factor based on which the dispatcher configures the true backup task on the preserved area. Experimental results show that the hybrid scheduling technique enhances the mean-time-to-failure of the system by an average factor of 1.22 in comparison with a similar state-of-the-art study.

HEALERS: a heterogeneous energy‐aware low‐overhead real‐time scheduler

Devising energy-efficient scheduling strategies for real-time periodic tasks on heterogeneous platforms is a challenging as well as a computationally demanding problem. This study proposes a low-overhead heuristic strategy called, HEALERS, for dynamic voltage and frequency scaling (DVFS)-cum-dynamic power management (DPM) enabled energy-aware scheduling of a set of periodic tasks executing on a heterogeneous multi-core system. The presented strategy first applies deadline-partitioning to acquire a set of distinct time-slices. At any time-slice boundary, the following three-phase operations are applied to obtain a schedule for the next time-slice: first, it computes the fragments of the execution demands of all tasks onto each of the different processing cores in the platform. Next, it generates a schedule for each task on one or more processing cores such that the total execution demand of all tasks is satisfied. Finally, HEALERS applies DVFS and DPM on all processing cores so that energy consumption within the time-slice may be minimized while not jeopardising execution requirements of the scheduled tasks. Experimental results show that the proposed scheme is not only able to achieve appreciable energy savings with respect to state-of-the-art (5–42% on average) but also enables a significant improvement in resource utilisation (as high as 58%).

Energy aware mixed tasks scheduling in real-time systems

We address the problem of scheduling mixed tasks which consist of n hard real time periodic tasks with shared resources and soft aperiodic tasks and consider two conflicting goals: reducing energy consumption and decreasing response time of aperiodic tasks. Firstly, we compute the optimal speed of periodic tasks and present a novel static mixed task scheduling (SMTS) algorithm. It schedules periodic tasks with the optimal speed and aperiodic tasks with maximum processor speed. Secondly, a dynamic mixed task scheduling (DMTS) algorithm which can reclaim dynamic slack time generated from periodic tasks and the constant bandwidth server to reduce energy consumption is proposed. In addition, it combines dynamic voltage scaling and dynamic power management techniques. Finally, we prove that the DMTS algorithm is feasible. The experimental results show that the DMTS algorithm reduces average 7.18% and 53.66% energy consumption × response time compared with SMTS algorithm and baseline algorithm, respectively.

DCW

Real-time systems continuously interact with the physical environment and often have to satisfy stringent timing constraints imposed by their interactions. Those systems involve two main properties: reactivity and predictability. Reactivity allows the system to continuously react to a non-deterministic external environment, while predictability guarantees the deterministic execution of safety-critical parts of applications. However, with the increase in software complexity, traditional approaches to develop real-time systems make temporal behaviors difficult to infer, especially when the system is required to address non-deterministic aperiodic events from the physical environment. In this article, we propose a reactive and predictable programming framework, Distributed Clockwerk (DCW), for distributed real-time systems. DCW introduces the Servant, which is a non-preemptible execution entity, to implement periodic tasks based on the Logical Execution Time (LET) model. Furthermore, a joint schedule policy, based on the slack stealing algorithm, is proposed to efficiently address aperiodic events with no violated hard-time constraints. To further support predictable communication among distributed nodes, DCW implements the Time-Triggered Controller Area Network (TTCAN) to avoid collisions while accessing the shared communication medium. Moreover, a programming framework implements to provide a set of programming APIs for defining timing and functional behaviors of concurrent tasks. An example is further implemented to illustrate the DCW design flow. The evaluation results demonstrate that our proposal can improve both periodic and aperiodic reactivity compared with existing work, and the implemented DCW can also ensure the system predictability by achieving extremely low overheads.

Processor Bounding for an Efficient Non-preemptive Task Scheduling Algorithm

The scheduling problem, which is the core of all approaches related to real-time systems, has received proper attention from the research community. However, while preemptive scheduling has benefited from most of the results to date, the more difficult case of non-preemptive scheduling is still lacking similar achievements. This paper is approaching non-preemptive scheduling from two different angles. First, the number of processors that would allow a feasible schedule for a given task set is analyzed, yielding both lower and upper limits which can be determined in polynomial time. Second, a hybrid scheduling algorithm, combining two widely known techniques, namely EDF and LLF, is proposed and tested. A common feature of both objectives is the transition from a single-instance task to a periodic task. The relationships between these two cases are investigated, resulting in a better understanding of periodic behavior.

Integrating Lowest Priority Approach with Largest Point Scheme for Faster Feasibility Analysis

Recently many solutions have been proposed to lower the computational cost of feasibility analysis for real-time systems. The computational cost of feasibility tests can be lowered by strategies such as lowering the number of scheduling points needed during analysis, starting feasibility analysis from lowest priority, or starting schedulability tests for a task with larger scheduling point. All these techniques significantly reduce the computation time of feasibility analysis for fixed priority systems. The computation time of such tests can be further reduced by combining various solutions for efficient feasibility analysis of periodic task sets. In this work, we integrate both lowest priority first with largest points first solution to derive a faster feasibility analysis test for fixed priority system. Our experimental evaluations suggest that the proposed technique significantly lowers the computational cost of the test when system utilization is in the range of 80% or when the ratio between the task period of a lower priority task and the highest priority task is large.

Feasibility analysis of periodic real-time systems with energy harvesting capabilities

Energy harvesting now appears as a feasible solution to power wireless devices. Such ones including sensor nodes have to be energy neutral i.e. be able to never consume more energy than available. Energy can be drawn from many ambient sources including solar, vibration, etc. Most of wireless devices perform periodic sampling and computations using a microprocessor for real-time monitoring. The software tasks that typically take place in these systems are time critical and cyclic with specified deadlines attached to finishing times. In this paper, we explore the problem of feasibility analysis of hard real-time periodic task sets on a single processor powered by renewable energy. Firstly, we investigate the notion of robustness as the capability of the system to remain stable even in case of positive changes in its operating environment. We prove that our feasibility test is robust with respect to the profile of the energy source. Secondly, we prove that for asynchronous tasks whose initial release times are not known in advance, feasibility can be checked considering the synchronous activation pattern which corresponds to the worst-case scenario. Finally, whenever accurate prediction of the incoming environmental energy is impossible, a sufficient feasibility test with high implementation efficiency is proposed.

Feasibility Interval for Fixed-Priority Scheduling of Mixed-Criticality Periodic Tasks With Offsets

This letter considers dual-criticality systems having periodic tasks with offsets scheduled by a given fixed-priority scheduler. We are interested to formally derive some feasibility interval for such systems. We prove that such an interval exists and that it is bounded by the size of four hyperperiods plus the largest task offset.

BIM-Tracker: A model-based visual tracking approach for indoor localisation using a 3D building model

This article presents an accurate and robust visual indoor localisation approach that not only is infrastructure-free, but also avoids accumulation error by taking advantage of (1) the widespread ubiquity of mobile devices with cameras and (2) the availability of 3D building models for most modern buildings. Localisation is performed by matching image sequences captured by a camera, with a 3D model of the building in a model-based visual tracking framework. Comprehensive evaluation of the approach with a photo-realistic synthetic dataset shows the robustness of the localisation approach under challenging conditions. Additionally, the approach is tested and evaluated on real data captured by a smartphone. The results of the experiments indicate that a localisation accuracy better than 10 cm can be achieved by using this approach. Since localisation errors do not accumulate the proposed approach is suitable for indoor localisation tasks for long periods of time and augmented reality applications, without requiring any local infrastructure. A MATLAB implementation can be found on https://github.com/debaditya-unimelb/BIM-Tracker.