Soft Real-time Tasks Research Articles

Real-time scheduling for parallel tasks with resource reclamation

This paper considers the real-time scheduling of a parallel task with reclaiming computing resources, which can be utilized for soft real-time tasks or switching to low-energy mode to save energy. Existing works allocate a rectangular piece of computing resources based on the worst-case characterizations of the task to guarantee the deadline, which inherently incurs severe resource wasting due to coarse-grained resource allocation. To address this resource-wasting problem, this paper proposes the ladder-like resource allocation (i.e., a series of rectangular pieces of computing resources). To characterize the ladder-like resource allocation, we present two concepts called resource distribution and allocation vector, which serve as the interfaces between hard and soft real-time tasks. For the former, we derive schedulability tests under the given two interfaces; for the latter, we discuss the methods of determining the two interfaces to reclaim computing resources. This paper is the first work to fully explore the concept of ladder-like resource allocation and its potential consequences on computing resources, soft real-time tasks, and energy. Experiments demonstrate that the proposed approach can effectively reclaim more computing resources than existing approaches while maintaining hard real-time guarantees.

A Hybrid Scheduling Framework for Mixed Real-Time Tasks in an Automotive System With Vehicular Network

As vehicles integrate more and more autonomous driving functionalities, it becomes more and more important to use vehicular networks to fully guarantee the safety and real-time performance of on-board computing tasks. Current studies on vehicular networks pay much attention to the performance improvement of network communication and resource allocation, while ignoring the fact that automotive on-board computing tasks play a significant role in vehicle safety and need to be elegantly handled in vehicular networks. In this paper, we propose a hybrid scheduling framework for meeting all hard real-time task deadlines while minimizing soft real-time task deadline misses. In particular, the proposed scheduler is composed of some local schedulers and a global scheduler, where the former guarantees the schedulability of all hard real-time tasks, and the latter decides the assignment of soft real-time jobs dynamically online. Depending on the remaining processing capability of a vehicular network, arrival jobs are either assigned for further processing or discarded. An approach combining the utilization-based schedulability test and demand-supply analysis is proposed to effectively assign tasks to processors offline. To meet as many soft real-time task deadlines as possible, tasks' demand and supply bounds are computed at runtime, such that online execution information can be used to compensate for the scheduling loss with the worst-case assumption made by the offline test. Experimental results demonstrate that, compared to a genetic algorithm, our proposed approach needs far less computation to assign more tasks offline. The online scheduling also saves many soft real-time jobs that were to be dropped by the offline algorithm.

Thermal-Aware Standby-Sparing Technique on Heterogeneous Real-Time Embedded Systems

Low power consumption, real-time computing, and high reliability are three key requirements/design objectives of real-time embedded systems. The standby-sparing technique can improve system reliability while it might increase the temperature of the system beyond safe limits. In this paper, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</u> hermal- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> ware <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> tandby- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> paring (TASS) technique that aims at maximizing the Quality of Service (QoS) of soft real-time tasks, which is defined as a function of the finishing time of running tasks. The proposed technique tolerates permanent and transient faults for multicore real-time embedded systems while meeting the Thermal Safe Power (TSP) as the core-level power constraint, which avoids thermal emergencies in on-chip systems. Executing the main and backup tasks on the cores at any power consumption below TSP guarantees that no thermal violation occurs. Our TASS proposed method provides an opportunity to remove the overlaps of the execution of main and backup tasks to prevent extra power consumption due to applying the fault-tolerant technique. Meanwhile, in order to maximize the QoS, we employ a heterogeneous platform to execute the main tasks as soon as possible on high-performance cores with more power budget. The backup tasks are executed on low power cores after finishing the main tasks. In this case, when the main task finishes successfully, the whole of its corresponding backup task can be dropped, resulting in a significant amount of power and temperature reduction. Therefore, in the fault-free scenarios, the spare cores can be powered down, and only the main tasks are scheduled and executed on the primary cores. Experiments show that our proposed method improves QoS up to 39.78% (on average by 18.40%) and reduces the peak power consumption and temperature by up to 40.21% and 15.47˚C (on average 28.31% and 13.60˚C), respectively, at runtime, while keeping the system reliability at the required level.

Improving the Schedulability of Real-Time Tasks Using Fog Computing

Due to the significant communication delay to user tasks, the cloud is not ideal for executing real-time tasks with stringent deadlines. Fog computing consists of low computation capability fog nodes, or cloudlets located in proximity to the source of the data generation: the users. These cloudlets are ideal for executing tasks that have early deadlines. In this paper, we propose algorithms that schedule a set of real-time tasks on such an embedded-fog-cloud architecture. We consider hard, firm and soft tasks. The execution framework consists of embedded, fog and cloud processors. Tasks are scheduled on appropriate processors based on their deadline requirements. In general, hard real-time tasks are executed on embedded processors, firm real-time tasks on fog processors, and soft real-time tasks on cloud processors. We also propose a sufficient schedulability condition. Simulation results from the CERIT trace as well as test-bed results show that the proposed algorithms offer superior performance as compared to algorithms that do not employ fog processors. Employing an <inline-formula><tex-math notation="LaTeX">$Embedded-fog-cloud$</tex-math></inline-formula> architecture offers an improvement of 62.37 percent for real-time Success Ratio (SR) and 35 percent for Average Response Time as compared to scheduling tasks on the <inline-formula><tex-math notation="LaTeX">$cloud$</tex-math></inline-formula> alone.

Optimized scheduling algorithm for soft Real-Time System using particle swarm optimization technique

Scheduling of tasks in Real-Time Systems is based on static or dynamic priority like earliest deadline first (EDF) and rate monotonic, respectively. The static scheduler does not give assurance of scheduling all tasks during the underload scenario, whereas dynamic scheduler performs poorly during an overload scenario. This paper has proposed a swarm intelligence-based scheduling algorithm that can overcome both the situations. This paper has used particle swarm optimization (PSO) based swarm technique to design the new scheduling approach. It considers each task as a particle and applied modified PSO technique to identify the most critical task to execute. The efficiency of the newly proposed method has been compared with existing EDF and ACO based scheduling algorithms considering two significant parameters, the success ratio and the effective CPU utilization. All three algorithms have been tested on the simulator with a Soft Real-time periodic task set on 500 timelines. It has been observed that during the underload scenario, the proposed algorithm performs equally to EDF and ACO based algorithms. During overload and highly overload situations, the proposed algorithm performs batter compared to EDF and ACO based algorithms.

Tardiness bounds for fixed-priority global scheduling without intra-task precedence constraints

Fixed-priority multiprocessor schedulers are often preferable to dynamic-priority ones because they entail less overhead, are easier to implement, and enable certain tasks to be favored over others. Under global fixed-priority (G-FP) scheduling, as applied to the standard sporadic task model, response times for low-priority tasks may be unbounded, even if the total task system utilization is low. In this paper, it is shown that this negative result can be circumvented if different jobs of the same task are allowed to execute in parallel. In particular, a response-time bound is presented for task systems that allow intra-task parallelism. This bound merely requires that the total utilization does not exceed the overall processing capacity—individual task utilizations need not be further restricted. This result implies that G-FP is optimal for scheduling soft real-time tasks that require bounded tardiness, if intra-task parallelism is allowed.

Statically optimal dynamic soft real-time semi-partitioned scheduling

Semi-partitioned scheduling is an approach to multiprocessor real-time scheduling where most tasks are fixed to processors, while a small subset of tasks is allowed to migrate. This approach offers reduced overhead compared to global scheduling, and can reduce processor capacity loss compared to partitioned scheduling. Prior work has resulted in a number of semi-partitioned scheduling algorithms, but their correctness typically hinges on a complex intertwining of offline task assignment and online execution. This brittleness has resulted in few proposed semi-partitioned scheduling algorithms that support dynamic task systems, where tasks may join or leave the system at runtime, and few that are optimal in any sense. This paper introduces EDF-sc, the first semi-partitioned scheduling algorithm that is optimal for scheduling (static) soft real-time (SRT) sporadic task systems and allows tasks to dynamically join and leave. The SRT notion of optimality provided by EDF-sc requires deadline tardiness to be bounded for any task system that does not cause over-utilization. In the event that all tasks can be assigned as fixed, EDF-sc behaves exactly as partitioned EDF. Heuristics are provided that give EDF-sc the novel ability to stabilize the workload to approach the partitioned case as tasks join and leave the system.

Maximizing Utilization and Minimizing Migration in Thermal-Aware Energy-Efficient Real-Time Multiprocessor Scheduling

This work proposes CAlECs, a clustered scheduling system for MPSoCs subject to thermal and energy constraints. It calculates off-line a cyclic executive honoring temporal and thermal constraints, for a hard real-time (HRT) task set at minimum frequency to reduce consumed energy, minimizing context switches and migrations. It also provides an on-line controller able to manage system and task parametric variations and soft real-time (SRT) tasks, always meeting the HRT task set constraints and the system thermal bound. CAlECS maximizes CPU utilization to help avoid overprovisioning contributing to a low SWaP factor. Its modular design allows the utilization of different modeling and scheduling approaches, and makes the off-line and on-line components independent from each other to better suit the requirements of a specific system. We experimentally show that the cyclic executive provided by CAlECS for HRT task sets outperforms RUN, a reference off-line algorithm in terms of optimal number of context switches.

Zygarde

We propose Zygarde --- which is an energy- and accuracy-aware soft real-time task scheduling framework for batteryless systems that flexibly execute deep learning tasks1 that are suitable for running on microcontrollers. The sporadic nature of harvested energy, resource constraints of the embedded platform, and the computational demand of deep neural networks (DNNs) pose a unique and challenging real-time scheduling problem for which no solutions have been proposed in the literature. We empirically study the problem and model the energy harvesting pattern as well as the trade-off between the accuracy and execution of a DNN. We develop an imprecise computing-based scheduling algorithm that improves the timeliness of DNN tasks on intermittently powered systems. We evaluate Zygarde using four standard datasets as well as by deploying it in six real-life applications involving audio and camera sensor systems. Results show that Zygarde decreases the execution time by up to 26% and schedules 9% -- 34% more tasks with up to 21% higher inference accuracy, compared to traditional schedulers such as the earliest deadline first (EDF).

ReMap: Reliability Management of Peak-Power-Aware Real-Time Embedded Systems Through Task Replication

Increasing power densities in future technology nodes is a crucial issue in multicore platforms. As the number of cores increases in them, power budget constraints may prevent powering all cores simultaneously at full performance level. Therefore, chip manufacturers introduce a power budget constraint as Thermal Design Power (TDP) for chips. Meanwhile, multicore platforms are suitable for implementation of fault-tolerance techniques to achieve high reliability. Task Replication is a known technique to tolerate transient faults. However, careless task replication may lead to significant peak power consumption. In this paper, we consider the problem of achieving a given reliability target while keeping the total power consumption under the chip TDP for a set of periodic soft real-time tasks. For this purpose, we propose a method for mapping and scheduling periodic soft real-time tasks in multicore embedded systems. The proposed method consists of three phases: (i) Reliability-Aware Lowest Utilization Mapping, (ii) Maximum-Power-Aware EDF Scheduling, and (iii) Reliability-and-Peak-Power-Aware Dynamic-Voltage-Frequency-Scaling. Our experiments show that our proposed method provides up to 38.4% (on average by 25%) peak power reduction compared to state-of-the-art methods.

EDF scheduling of real-time tasks on multiple cores

This paper presents a novel migration algorithm for real-time tasks on multicore systems, based on the idea of migrating tasks only when strictly needed to respect their temporal constraints and a combination of this new algorithm with EDF scheduling. This new "adaptive migration" algorithm is evaluated through an extensive set of simulations showing good performance when compared with global or partitioned EDF: our results highlight that it provides a worst-case utilisation bound similar to partitioned EDF for hard real-time tasks and an empirical tardiness bound (like global EDF) for soft real-time tasks. Therefore, the proposed scheduler is effective for dealing with both hard and soft real-time workloads.

Energy-efficient offloading of real-time tasks using cloud computing

With the increasing number of sophisticated power-intensive applications, embedded systems require energy-efficient computing devices. Most real-time applications of embedded systems are subject to timing constraints that should be met for the applications to run properly. Unfortunately, local computational devices have limited computation and storage capacity. Moreover, the real-time applications that perform complex computations consume large amounts of power. Therefore, offloading the power-intensive computational tasks to a more powerful entity is an efficient technique to overcome the limited computational resources of local devices and reduces the overall power consumption. In this paper, we propose two algorithms for making an efficient offloading decision for soft and weakly hard (firm) real-time applications while guaranteeing the schedulability of tasks. We have performed different experiments and investigated the technical and economic feasibility of using offloading to perform various processes that require different computational power. Experimental results show that significant power can be saved by offloading the resources of the power-intensive applications into the cloudlet for weakly hard real-time tasks and and cloud for soft real-time tasks.

A Novel On-Chip Task Scheduler for Mixed-Criticality Real-Time Systems

This paper presents a novel design of a coprocessor that performs hardware-accelerated task scheduling for embedded real-time systems consisting of mixed-criticality real-time tasks. The proposed solution is based on the Robust Earliest Deadline (RED) algorithm and previously developed hardware architectures used for scheduling of real-time tasks. Thanks to the HW implementation of the scheduler in the form of a coprocessor, the scheduler operations (i.e., instructions) are always completed in two clock cycles regardless of the actual or even maximum task amount within the system. The proposed scheduler was verified using simplified version of UVM and applying billions of randomly generated instructions as inputs to the scheduler. Chip area costs are evaluated by synthesis for Intel FPGA Cyclone V and for 28-nm TSMC ASIC. Three versions of real-time task schedulers were compared: EDF-based scheduler designed for hard real-time tasks only, GED-based scheduler and the proposed RED-based scheduler, which is suitable for tasks of various criticalities. According to the synthesis results, the RED-based scheduler consumes LUTs and occupies larger chip area than the original EDF-based scheduler with equivalent parameters used. However, the RED-based scheduler handles variations of task execution times better, achieves higher CPU utilization and can be used for the scheduling of hard real-time, soft real-time and nonreal-time tasks combined in one system, which is not possible with the former algorithms.

Energy-Aware Design of Stochastic Applications With Statistical Deadline and Reliability Guarantees

Energy efficiency, reliability, and real-time are three key requirements of mission-critical embedded systems. Existing approaches over emphasize the worst case design of real-time embedded systems, which will lead to serious waste of resources. In this paper, we aim at the energy-efficient design of soft real-time and reliable applications on uniprocessor embedded systems. We consider soft real-time tasks with stochastic execution durations regarding certain distributions. Thereby, we provide real-time guarantee with probability consideration. We utilize dynamic voltage and frequency scaling (DVFS) for saving energy, and also take into account the impact of DVFS on reliability. Our objective is to minimize the expected energy consumption of the system subject to statistical reliability and deadline constraints. The design optimization problem is a typical multidimensional multiple-choice knapsack problem, which is NP-hard. We first propose a dynamic programming-based optimal algorithm to solve the problem. To reduce the time complexity, we then develop a ( $1+{\beta }$ )-approximation algorithm based on a binary search approach, where ${\beta }$ is the approximating factor. The approximation algorithm can obtain the near-optimal solution with at most ( $1{+\beta }$ ) times of optimal energy cost under given real-time and reliability constraints and has fully polynomial time complexity. Extensive experiments and a real-life synthetic application are conducted to evaluate the performance of the proposed techniques. Compared with existing approaches, the approximation approach can save much energy with low time overhead while guaranteeing the statistical deadline and reliability constraints.

Energy-efficient thermal-aware multiprocessor scheduling for real-time tasks using TCPN

We present an energy-efficient thermal-aware real-time global scheduler for a set of hard real-time (HRT) tasks running on a multiprocessor system. This global scheduler fulfills the thermal and temporal constraints by handling two independent variables, the task allocation time and the selection of clock frequency. To achieve its goal, the proposed scheduler is split into two stages. An off-line stage, based on a deadline partitioning scheme, computes the cycles that the HRT tasks must run per deadline interval at the minimum clock frequency to save energy while honoring the temporal and thermal constraints, and computes the maximum frequency at which the system can run below the maximum temperature. Then, an on-line, event-driven stage performs global task allocation applying a Fixed-Priority Zero-Laxity policy, reducing the overhead of quantum-based or interval-based global schedulers. The on-line stage embodies an adaptive scheduler that accepts or rejects soft RT aperiodic tasks throttling CPU frequency to the upper lowest available one to minimize power consumption while meeting time and thermal constraints. This approach leverages the best of two worlds: the off-line stage computes an ideal discrete HRT multiprocessor schedule, while the on-line stage manage soft real-time aperiodic tasks with minimum power consumption and maximum CPU utilization.

Self-Adaptive QoS Management of Computation and Communication Resources in Many-Core SoCs

Providing quality of service (QoS) for many-core systems with dynamic application admission is challenging due to the high amount of resources to manage and the unpredictability of computation and communication events. Related works propose a self-adaptive QoS mechanism concerned either in communication or computation resources, lacking, however, a comprehensive QoS management of both. Assuming a many-core system with QoS monitoring, runtime circuit-switching establishment, task migration, and a soft real-time task scheduler, this work fills this gap by proposing a novel self-adaptive QoS management. The contribution of this proposal comes with the following features in the QoS management: ( i ) comprehensiveness, by covering communication and computation resources; ( ii ) online, adopting the ODA (Observe, Decide, Act) runtime closed-loop adaptation; and ( iii ) reactive and proactive decisions, by using a dynamic application profile extraction technique, which enables the QoS management to be aware of the profile of running applications, allowing it to take proactive decisions based on a prediction analysis. The proposed QoS management adopts a decentralized organization by partitioning the system in clusters, each one managed by a dedicated processor, making the proposal scalable. Results show that the proactive feature accurately extracts the applications’ profile, and can prevent future QoS violations. The synergy of reactive and proactive decisions was able to sustain QoS, reducing the deadline miss rate by 99.5% with a severe disturbance in communication and computation levels, and avoiding deadline misses up to 70% of system utilization.

A General Analysis Framework for Soft Real-Time Tasks

Much recent work has been conducted on supporting soft real-time tasks on multiprocessors due to the multicore revolution. While most earlier works focus on the traditional sporadic task model with deterministic worst-case specification, several recent works investigate the stochastic nature of many workloads seen in practice, specifying task execution times using average-case provisioning instead of the worst case. Unfortunately, all the existing work on supporting soft real-time workloads ignores a simple practical fact that the job inter-arrival time (or task period) is also stochastic for many real-world applications. Adopting a fixed worst-case period to model all the arriving pattern is rather pessimistic and may result in significant capacity loss in practice. Based on these observations, we present a general soft real-time multiprocessor schedulability analysis framework in this paper for practical sporadic task systems specified by stochastic period and execution demand, following probability distributions. Our analysis can be generally applied to global tunable priority-based schedulers, which allow any job's priority to be changed dynamically at runtime within a priority window of constant length. We have extensively evaluated the analysis framework using a MPEG video decoding case study and simulation-based experiments. Experimental results demonstrate significant advantages of our analysis, which yields over 200 and 50 percent improvements compared to existing analysis assuming worst-case task periods in terms of schedulability and magnitude of the derived tardiness bound, respectively.

An Adaptive Emergency First Intelligent Scheduling Algorithm for Efficient Task Management and Scheduling in Hybrid of Hard Real-Time and Soft Real-Time Embedded IoT Systems

Industrial revolution is advancing, and the augmented role of autonomous technology and embedded Internet of Things (IoT) systems is at its vanguard. In autonomous technology, real-time systems and real-time computing are of core importance. It is crucial for embedded IoT devices to respond in real-time; along with fulfilling all the constraints. Many combinations for existing approaches have been proposed with different trade-offs between the resources constraints and tasks dropping rate. Hence, it highlights the significance of a task scheduler which not only takes care of complex nature task input; but also maximizes the CPU throughput. A complex nature task input is when combinations of hard real-time tasks and soft real-time tasks, with different priorities and urgency measures, arrive at the scheduler. In this work, we propose a custom tailored adaptive and intelligent scheduling algorithm for the efficient execution and management of hard and soft real time tasks in embedded IoT systems. The proposed scheduling algorithm aims to distribute the CPU resources fairly to the possibly starving, in overloaded cases, soft real-time tasks while focusing on the execution of high priority hard real-time tasks as its primary objective. The proposal is achieved with the help of two intelligent measures; Urgency Measure (UM) and Failure Measure (FM). The proposed mechanism reduces the rate of tasks missed and the rate of tasks starved, by utilizing the free CPU units for maximum CPU utilization and quick response times. We have performed comparisons of our proposed scheme based on performance metrics as percentage of task instances missed, number of tasks with missed instances, and tasks starvation rate to evaluate the CPU utilization. We first compare our proposed approach with multiple traditional and combined scheduling approaches, and then we evaluate the effect of intelligent modules by comparing the intelligent FEF with non-intelligent FEF. We also evaluate the proposed algorithm in contrast to the most commonly-used hybrid scheduling scheme in embedded systems. The results show that the proposed algorithm out performs the other algorithms, by significantly reducing the task starvation rate and increasing the CPU utilization.

Evaluation of the Proposed Hybrid Multiprocessor Real-Time Scheduling Approach with Partitioned and Global Approaches

Generally, multiprocessor real-time scheduling algorithm fall into two basic approaches, partitioned and global. The hybrid solution that we proposed applies the partitioned scheduling approach to the task set until all processors have been filled. The remaining tasks are then scheduled using the global scheduling approach. The idea of a hybrid scheduling approach to ameliorate limitations of partitioned and global approaches. Studies have shown that most prior research on hybrid multiprocessor real-time scheduling has been confined to hard and soft real-time tasks. In fact, the implementation of hybrid approach and the performance of such algorithms in comparison to partitioned and global approaches have not been fully answered by previous studies. This paper performs experimental evaluation of our proposed hybrid multiprocessor scheduling approach, R-BOUND-MP-NFRNS and RM-US (m/3m-2) with multiprocessor response time test, with one of the best scheduling approach from partitioned and global scheduling approaches. The evaluation is based on simulation experiments using videophone application and Cruise Control with Collision Avoidance (CCCA) case studies that mixes real-time tasks of different criticality. The experimental results have presented in terms of three metrics or in other words, performance measurement parameters, namely deadline satisfaction ratio, least system utilization and preemption density (overhead ratio). Based on these three metrics, the proposed hybrid scheduling approach achieved higher percentage compare to the other two approaches. This indicates that such a hybrid scheduling approach is a viable alternative to use in multiprocessor systems and for best scheduled constrained deadline periodic tasks where timing constraints known as weakly hard.

Dynamic Soft Real-Time Scheduling with Preemption Threshold for Streaming Media

Over the last decades, the advancements in networking technology and new multimedia devices have motivated the research on efficient video streaming mechanisms under wireless. We consider combing soft real-time video streaming scheduling with threshold to minimize the ineffective preemption. Based on the value density and urgency of soft real-time task, the dynamic scheduling with preemption threshold strategy (DSPT) is proposed in the paper. By analyzing the response time and preemption relationship of tasks, the preemption thresholds are assigned. Simulation results show that the DSPT strategy achieves improvements about success rate, delay time, and benefit of the system.