Set Of 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.

Battery-aging-aware run-time slack management for power-consuming real-time systems

As many safety- or mission-critical electric systems become equipped with batteries, we need to achieve seemingly conflicting goals: G1) timely execution of power-consuming tasks (for safety or mission) while G2) minimizing battery aging (for system sustainability). To this end, this paper proposes a novel scheduling framework for a set of power-consuming real-time tasks, which efficiently utilizes run-time slack (i.e., system idle time identified at run-time) to find the tasks’ schedule that achieves both G1 and G2. The proposed framework is not only applicable to any existing prioritization policy (e.g., EDF and FP) but has also been proven to reduce battery aging by up to 32.6% without compromising G1.

Estimating Probabilistic Safe WCET Ranges of Real-Time Systems at Design Stages

Estimating worst-case execution time (WCET) is an important activity at early design stages of real-time systems. Based on WCET estimates, engineers make design and implementation decisions to ensure that task executions always complete before their specified deadlines. However, in practice, engineers often cannot provide precise point WCET estimates and prefer to provide plausible WCET ranges. Given a set of real-time tasks with such ranges, we provide an automated technique to determine for what WCET values the system is likely to meet its deadlines and, hence, operate safely with a probabilistic guarantee. Our approach combines a search algorithm for generating worst-case scheduling scenarios with polynomial logistic regression for inferring probabilistic safe WCET ranges. We evaluated our approach by applying it to three industrial systems from different domains and several synthetic systems. Our approach efficiently and accurately estimates probabilistic safe WCET ranges within which deadlines are likely to be satisfied with a high degree of confidence.

Real-Time Scheduling on Hierarchical Heterogeneous Fog Networks

Cloud computing is widely used to support offloaded data processing for applications. However, latency constrained data processing has requirements that may not always be suitable for cloud-based processing. Fog computing brings processing closer to data generation sources, by reducing propagation and data transfer delays. It is a viable alternative for processing tasks with real-time requirements. We propose a scheduling algorithm RTH2S (Real Time Heterogeneous Hierarchical Scheduling) for a set of real-time tasks on a heterogeneous integrated fog-cloud architecture. We consider a hierarchical model for fog nodes, with nodes at higher tiers having greater computational capacity than nodes at lower tiers, though with greater latency from data generation sources. Tasks with various profiles have been considered. For regular profile jobs, we use least laxity first (LLF) to find the preferred fog node for scheduling. For tagged profiles, based on tag values, the jobs are split in order to finish execution before the deadline, or the LLF heuristic is used. Using HPC2N workload traces across 3.5 years of activity, the real-time performance of RTH2S versus comparable algorithms is demonstrated. Our proposed approach is validated using both simulation (to demonstrate scale up) as well as a lab-based testbed.

Tight necessary feasibility analysis for recurring real-time tasks on a multiprocessor

One of the important design issues for time-critical embedded systems is to derive necessary conditions that meet all job deadlines invoked by a set of recurring real-time tasks under a computing resource (called feasibility). To this end, existing studies focused on how to derive a tight lower-bound of execution requirement (i.e., demand) of a target set of real-time tasks. In this paper, we address the following question regarding the supply provided by a multiprocessor resource: is it possible for a real-time task set to always utilize all the provided supply? We develop a systematic approach that i) calculates the amount of supply proven unusable, ii) finds a partial schedule that yields a necessary condition to minimize the amount of unusable supply, and iii) uses the partial schedule to further reclaim unusable supply. While the systematic approach can be applied to most (if not all) recurring real-time task models, we show two examples how the approach can yield tight necessary feasibility conditions for the sequential task model and the gang scheduling model. We demonstrate the proposed approach finds a number of additional infeasible task sets which have not been proven infeasible by any existing studies for the task models.

RASA: Reliability-Aware Scheduling Approach for FPGA-Based Resilient Embedded Systems in Extreme Environments

Field-programmable gate arrays (FPGAs) offer the flexibility of general-purpose processors along with the performance efficiency of dedicated hardware that essentially renders it as a platform of choice for modern-day robotic systems for achieving real-time performance. Such robotic systems when deployed in harsh environments often get plagued by faults due to extreme conditions. Consequently, the real-time applications running on FPGA become susceptible to errors which call for a reliability-aware task scheduling approach, the focus of this article. We attempt to address this challenge using a hybrid offline-online approach. Given a set of periodic real-time tasks that require to be executed, the offline component generates a feasible preemptive schedule with specific preemption points. At runtime, these preemption events are utilized for fault detection. Upon detecting any faulty execution at such distinct points, the reliability-aware scheduling approach, RASA, orchestrates the recovery mechanism to remediate the scenario without jeopardizing the predefined schedule. Effectiveness of the proposed strategy has been verified through simulation-based experiments and we observed that the RASA is able to achieve 72% of task acceptance rate even under 70% of system workloads with high fault occurrence rates.

The Execution Efficiency of the Response Time Analysis under Different Task Sequencing Strategies

Tasks in real-time systems have strict time requirements and need to be completed before their deadlines. Whenever the real-time task set changes, the real-time system reruns the schedulability analysis method to determine whether the tasks in the new task set can be completed before their deadlines. Since new tasks can not be executed until the execution of the schedulability analysis method is completed, the execution efficiency of the schedulability analysis method directly affects the actual performance of the real-time system. Response time analysis (RTA) is a typical method for schedulability analysis. It calculates the response times of tasks and tests the schedulability of tasks by comparing the calculation results and the relative deadlines of tasks. We found that the execution efficiency of existing RTAs can be improved by reasonably sorting the calculation order of the tasks. A task sequencing strategy is proposed to improve the execution efficiency of existing RTAs.

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.

A Novel Feasibility Test for Energy Minimization of Real-Time Mixed Task Sets for DVS-Enabled Uniprocessor System

With advancements in computing and communication technologies on mobile devices, the performance requirements of embedded processors have significantly increased, resulting in a corresponding increase in its energy consumption. Dynamic scaling of operating voltage and operating frequency has a strong correlation to energy minimization in CMOS real-time circuits. Simultaneous optimization of ([Formula: see text], [Formula: see text] pairs under dynamic activity levels is thus extensively investigated over several years. The supply voltage is tuned dynamically during runtime (DVS), with a fixed threshold voltage, to achieve energy minimization. This work addresses the issue of maximizing the energy efficiency of real-time periodic, aperiodic and mixed task sets, in a uniprocessor system, by developing a novel task feasibility methodology, with a novel processor performance-based constraint, to generate the optimal operating supply voltage to the individual task of task sets. The energy minimization of real-time mixed task sets is formulated as Geometric Programming (GP) problem, by varying frequency for periodic tasks sets and keeping fixed frequency for aperiodic tasks set, over a range of task sets and hence computing optimal operating voltages. Simulation experiments show energy savings on the cumulative basis of 50%, 38% and 29% for periodic, aperiodic and mixed task sets, respectively, based on the processing timing constraints of task sets.

Thermal-aware Adaptive Platform Management for Heterogeneous Embedded Systems

Recent trends in real-time applications have raised the demand for high-throughput embedded platforms with integrated CPU-GPU based Systems-On-Chip (SoCs). The enhanced performance of such SoCs, however, comes at the cost of increased power consumption, resulting in significant heat dissipation and high on-chip temperatures. The prolonged occurrences of high on-chip temperature can cause accelerated in-circuit ageing, which severely degrades the long-term performance and reliability of the chip. Violation of thermal constraints leads to on-board dynamic thermal management kicking-in, which may result in timing unpredictability for real-time tasks due to transient performance degradation. Recent work in adaptive software design have explored this issue from a control theoretic stand-point, striving for smooth thermal envelopes by tuning the core frequency. Existing techniques do not handle thermal violations for periodic real-time task sets in the presence of dynamic events like change of task periodicity, more so in the context of heterogeneous SoCs with integrated CPU-GPUs. This work presents an OpenCL runtime extension for thermal-aware scheduling of periodic, real-time tasks on heterogeneous multi-core platforms. Our framework mitigates dynamic thermal violations by adaptively tuning task mapping parameters, with the eventual control objective of satisfying both platform-level thermal constraints and task-level deadline constraints. We consider multiple platform-level control actions like task migration, frequency tuning and idle slot insertion as the task mapping parameters. To the best of our knowledge, this is the first work that considers such a variety of task mapping control actions in the context of heterogeneous embedded platforms. We evaluate the proposed framework on an Odroid-XU4 board using OpenCL benchmarks and demonstrate its effectiveness in reducing thermal violations.

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.

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.

Dynamic partitioned scheduling of real-time tasks on ARM big.LITTLE architectures

This paper presents Big-LITTLE Constant Bandwidth Server (BL-CBS), a dynamic partitioning approach to schedule real-time task sets in an energy-efficient way on multi-core platforms based on the ARM big.LITTLE architecture. BL-CBS is designed as an on-line and adaptive scheduler, based on a push/pull architecture that is suitable to be incorporated in the current SCHED_DEADLINE code base in the Linux kernel. It employs a greedy heuristic to dynamically partition the real-time tasks among the big and LITTLE cores aiming to minimize the energy consumption and the migrations imposed on the running tasks. The new approach is validated through the open-source RT-Sim simulator, which has been extended integrating an energy model of the ODROID-XU3 board, fitting tightly the power consumption profiles for the big and LITTLE cores of the board. An extensive set of simulations have been run with randomly generated real-time task sets, leading to promising results.

SEAMERS: A Semi-partitioned Energy-Aware scheduler for heterogeneous MulticorE Real-time Systems

Abstract Over the years, the nature of processing platforms is witnessing a significant shift in most of the battery supported real-time systems, which now support a combination of specialized multicores to meet the demands of modern applications. Devising energy-efficient schedulers has become a critical issue for such kinds of devices. Hence, this research presents a low-overhead heuristic strategy named SEAMERS, for DVFS based energy-aware scheduling for a set of real-time periodic tasks on a heterogeneous multicore platform. The presented strategy operates in four phases, namely Deadline Partitioning, Core Clustering, Task Allocation and Energy-Aware Scheduling. Our experimental analysis shows that the presented strategy improves upon the state-of-the-art in terms of energy savings (16% to 47% on average) and enables significant improvement in resource utilization.

Deadline-Constrained Tasks’ Scheduling in Multi-core Systems Using Harmonic-Aware Load Balancing

In highly advanced real-time critical applications, the demand of multi-core processing is increasing as the tasks are assigned to cores in the most balanced manner. The balancing of tasks on the cores requires an efficient load balancing algorithm. To balance the loads amongst the cores, all harmonic tasks can be grouped together and shall be assigned on a single core. The concept of harmonic task set is effective as it tends to reach a utilization of one, and thus, the system achieves a higher utilization. The proposed paper is focused on harmonic-aware load balancing algorithm for a set of periodic real-time tasks having constrained deadlines, where the deadline of the tasks is less than the period of the tasks. The extensive analysis was performed to establish the superiority of harmonic task sets in real-time applications. The results of this analysis are reported in this paper. It was observed that by considering the tasks’ harmonic relationship, the proposed load balancing approach greatly improves the feasibility of real-time tasks on a multi-core processor to meet their deadlines.

Energy efficient backup overloading schemes for fault tolerant scheduling of real-time tasks

Efficient energy management and fault tolerance are two key issues that demand judicious handling while scheduling real-time task-sets. Standby-sparing technique is available in literature for fault tolerance, while dynamic power management (DPM) and dynamic voltage scaling (DVS) schemes are exploited for energy management. However, usage of auxiliary processor for running backup jobs leads to increased energy consumption making fault tolerant scheduling energy inefficient. In the current work, overloading of backup jobs in overlapping time intervals is proposed, implemented and investigated for reducing energy consumption on auxiliary processor for scheduling real-time periodic tasks. Two energy efficient fault tolerant scheduling algorithms FEED-O and FEED-OD are proposed and extensive performance analysis through simulations is carried out indicating their usefulness especially at higher task-set utilization in comparison to other state-of-the-art algorithms.

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.

Reliability Aware Energy Optimized Scheduling of Non-Preemptive Periodic Real-Time Tasks on Heterogeneous Multiprocessor System

Higher reliability and lower energy consumption are conflicting, yet among the most important design objectives for the real-time systems. Moreover, in the domain of real-time systems, non-preemptive scheduling is relatively unexplored with objectives such as reliability and energy. Thus we propose an active replication based framework to schedule a set of periodic real-time tasks in the non-preemptive heterogeneous environment such that the given reliability and timing constraints are satisfied whereas the energy consumption is minimized. First, we formulate the problem as a constraint optimization problem that provides an optimal solution; however, it does not scale well. Thus, we also propose heuristics which apply reservation of processors and reallocation of jobs, to compute suboptimal solution efficiently in terms of energy consumption as well as schedulability. Heuristics make use of the interplay of task-level reliability target, reliability of replicas, number of replicas, reliability of tasks, and energy consumption. We perform an experimental study on the test cases generated by extending UUnisort algorithm [1] and observe the effect of various simulation parameters on energy consumption and schedulability.

Improved Schedulability Test for Non-Preemptive Fixed-Priority Scheduling on Multiprocessors

Non-preemptive scheduling is essential to tasks that inherently disallow any preemption and useful for tasks that exhibit extremely large preemption/migration overhead; however, studies of non-preemptive scheduling have not matured for real-time tasks subject to timing constraints. In this letter, we propose an improved schedulability test for non-preemptive fixed-priority scheduling (NP-FP), which offers timing guarantees for a set of real-time tasks executed on a multiprocessor platform. To this end, we first carefully investigate the NP-FP properties, and present an observation why the existing technique is pessimistic in calculating interference from higher-priority tasks. We then develop a new technique that tightly upper bounds the amount of the interference, and show how to incorporate the technique into the existing schedulability test. Via simulations, we demonstrate that our proposed test improves schedulability performance of NP-FP up to 18%, compared with the state-of-the-art existing tests.

A solution to drawbacks in capturing execution requirements on heterogeneous platforms

Real-time embedded systems are increasingly being implemented on heterogeneous multiprocessor platforms in which the same piece of software may require different amounts of time to execute on different processors. Computation of optimal schedules for such systems is non-trivial. Recently, Zhang et al. proposed linear and dynamic programming algorithms for real-time task scheduling for heterogeneous platforms. The authors have formulated a linear programming problem which is then iteratively solved by the linear programming algorithm (LPA) to produce a feasible schedule. Further, they compared the performance of LPA against their proposed dynamic programming algorithm (DPA) and claimed that LPA is superior to DPA, in terms of scalability. In this paper, we show that their linear programming problem does not correctly capture the execution requirement of real-time tasks on heterogeneous platforms. Consequently, LPA fails to produce valid execution schedules for most task sets presented to it. We first illustrate this flaw and strengthen our claim theoretically using a counterexample. Then, we present necessary modifications to their linear programming formulation to address the identified flaw. Finally, we show that our proposed algorithm can be used to find a feasible schedule for real-time task sets, using a real-world case study and experiments.