Scheduling Of Real-time Tasks Research Articles

Enhancing smart shop floor management with ubiquitous augmented reality

This paper describes a framework for implementing Smart manufacturing Shop floor systems based on the Ubiquitous Augmented Reality technology (SSUAR). The proposed system makes use of data sharing between shop floor resources and a sensor network in order to optimise the production schedules for carrying out projects. The optimisation is performed in real-time and the production scheduling responds to new projects as well as the changing status of resources, such as machines and workers. Ubiquitous augmented reality interface has been developed and utilised as a user interface for the shop floor workers to receive information, instructions and guidance from the experts and manufacturing systems, and to update the systems on task parameters, such as estimated completion times, progress and machine status. A review of related work, methodology and implementation of the proposed system, and a case study are presented in this paper. Using this architecture, real-time scheduling of tasks in the smart shop floor can be achieved. The case study demonstrated the ability of SSUAR to integrate task scheduling with two-way communication between the system and the users.

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.

Hybrid flow shop scheduling problem in ubiquitous manufacturing environment

As a new smart manufacturing mode, Internet of manufacturing things could collect information from production line and help to make a timely and accurate decision. However, this new mode poses new challenges to production operation management. This article focuses on the hybrid flow shop scheduling problem in ubiquitous manufacturing environment. Advanced wireless devices such as radio frequency identification are deployed to enable interconnection and perception among workers, materials and equipment in shop floor. The scheduling optimisation models for the planning layer and the scheduling layer are established, respectively. The evolutionary game algorithm is proposed to optimise the assignment of daily production tasks in the planning layer. According to the real-time situations of various production resources, the non-dominated sorted genetic algorithm is utilised to optimise the real-time scheduling of daily tasks in the scheduling layer. Between the two layers, real-time production information is exchanged by advanced wireless devices. Finally, two production scenarios in off-season and peak-season are presented. The experimental results verify the efficiency and the effectiveness of the proposed approach under different production conditions.

Two-level task scheduling with multi-objectives in geo-distributed and large-scale SaaS cloud

With the exploding of data-intensive web applications and requests (tasks), geo-distributed and large-scale data centers (DCs) are widely deployed in Software as a Service (SaaS) cloud, but server failures continue to grow at the same time. In this context, task scheduling problems become more intricate and both scheduling quality and scheduling speed raise further concerns. In this paper, we first propose a virtualized & monitoring SaaS model with predictive maintenance to minimize the costs of fault tolerance. Then with the monitored and predicted available states of servers, we focus on dynamic real-time task scheduling in geo-distributed and large-scale DCs with heterogeneous servers. Multiple objectives, including the long-term performance benefits, energy and communication costs, are taken into consideration in order to improve scheduling quality. For inter-DC and intra-DC task scheduling, two dynamic programming problems are formulated respectively, but there exists the problem that both state and action spaces are too large to be solved by simple iterations. To address this issue, we introduce the idea of reinforcement learning theory into solving traditional stochastic dynamic programming problems in the large-scale SaaS cloud, and put forward a cascaded two-level (inter-DC and intra-DC level) approximate dynamic programming (ADP) task-scheduling algorithm. The computation complexity can be significantly reduced and scheduling speed can be greatly improved. Finally, we conduct experiments with both random simulation data and Google cloud trace-logs. QoS evaluations and comparisons demonstrate that two ADP algorithms can work cooperatively, and our two-level ADP algorithm is more effective under large quantity of bursty requests.

Hybrid Inference Based Scheduling Mechanism for Efficient Real Time Task and Resource Management in Smart Cars for Safe Driving

In recent years, the focus of the smart transportation industry has been shifting towards the research and development of smart cars with autonomous control. Smart cars are considered to be a smart investment, as they promote safe driving while focusing on an alternate transportation fuel resource, making them eco-friendly too. Safe driving is one of the crucial concerns in autonomous smart cars. The major issue for the better provision of safe driving is real time tasks management and an efficient inference system for autonomous control. Real time task management is of huge significance in smart cars control systems. An optimal control system consists of a knowledge base and a control unit; where the knowledge base contains the data and thresholds for rules and the control unit contains the functionality for smart vehicle autonomous control. In this work, we propose a hybrid of an inference engine and a real time task scheduler for an efficient task management and resource consumption. Our proposed hybrid inference engine and task scheduler mechanism provides an efficient way of controlling smart cars in different scenarios such as heavy rainfall, obstacle detection, driver’s focus diversion etc., while ensuring the practices of safe driving. For the performance analysis of our proposed hybrid inference based scheduling mechanism, we have simulated a non-hybrid version with the same system constraints and a basic implementation of inference engine. For performance evaluation, CPU time utilization, tasks’ missing rate, average response time are used as performance metrics.

Minimizing Makespan in Embedded Real-Time Systems with Failure Rate Requirements

Scheduling of real-time tasks in embedded real-time systems with quantitative reliability requirements, such as failure rate, is one of the important issues in system design. In this paper, we present a real-time scheduling heuristic that minimise makespan and satisfy reliability requirements for systems that are subject to processor and communication faults. The heuristic is based on a cost function to minimise makespan and on task redundancy to meet reliability requirements. Our approach is dedicated to heterogeneous architectures with multiple processors linked by several shared buses. It is based on active redundancy and data fragmentation strategies, which allow fast error detection and error handling. Thanks to the above-mentioned strategies, we are able to show with simulation results that our approach can generally reduce the run-time overhead.

Dynamic Scheduling of Real-Time Tasks in Heterogeneous Multicore Systems

The shift from homogeneous multicore to heterogeneous multicore introduces challenges in scheduling the tasks to the appropriate cores maintaining the time deadline. This letter studies the existing scheduling schemes in a heterogeneous multicore system and finds an approach to enhance the homogeneous system model to heterogeneous scheduling architecture. The proposed model increases the overall system utilization by accommodating almost all the tasks (low power task and high power task) into appropriate cores (big high power and small low power). It further enhances the system performance by allocating rejected jobs from small cores into the big cores through a dispatcher.

Schedulability analysis of DAG tasks with arbitrary deadlines under global fixed-priority scheduling

One of the major sources of pessimism in the response time analysis (RTA) of globally scheduled real-time tasks is the computation of an upper-bound on the inter-task interference. This problem is further exacerbated when intra-task parallelism is permitted because of the complex internal structure of parallel tasks. This paper considers the global fixed-priority (G-FP) scheduling of sporadic real-time tasks when each task is modeled by a directed acyclic graph (DAG) of concurrent subtasks. We present a RTA based on the concept of problem window, a technique that has been extensively used to study the schedulability of sequential task in multiprocessor systems. The problem window approach of RTA usually categorizes interfering jobs in three different groups: carry-in, carry-out and body jobs. In this paper, we propose two novel techniques to derive less pessimistic upper-bounds on the workload produced by the carry-in and carry-out jobs of the interfering tasks. Those new bounds take into account the precedence constraints between subtasks pertaining to the same DAG. We show that with this new characterization of the carry-in and carry-out workload, the proposed schedulability test offers significant improvements on the schedulability of DAG tasks for randomly generated task sets in comparison to state-of-the-art techniques. In fact, we show that, while the state-of-art analysis does not scale with an increasing number of processors when tasks have constrained deadlines, the results of our analysis are barely impacted by the processor count in both the constrained and the arbitrary deadline case.

Workload-aware harmonic partitioned scheduling for fixed-priority probabilistic real-time tasks on multiprocessors

Abstract Multiprocessor platforms bring the probabilistic characteristic to real-time systems because of the performance variations of complex chips. We present a harmonic partitioned scheduling scheme with workload awareness for periodic probabilistic real-time tasks on multiprocessors under the fixed-priority scheduling policy. A harmonic index is defined to quantify the harmonicity among probabilistic real-time tasks. The proposed scheme first sorts tasks with respect to the workload, then packs them to processors one by one aiming at minimizing the increase of harmonic index caused by the task deployment. Evaluation shows that our proposed scheme can significantly outperform the existing harmonic partitioned probabilistic real-time scheduling algorithms.

Towards the Design of a Formal Verification and Evaluation Tool of Real-Time Tasks Scheduling of IoT Applications

Real-Time Internet of Things (RT-IoT) is a newer technology paradigm envisioned as a global inter-networking of devices and physical things enabling real-time communication over the Internet. The research in Edge Computing and 5G technology is making way for the realisation of future IoT applications. In RT-IoT tasks will be performed in real-time for the remotely controlling and automating of various jobs and therefore, missing their deadline may lead to hazardous situations in many cases. For instance, in the case of safety-critical and mission-critical IoT systems, a missed task could lead to a human loss. Consequently, these systems must be simulated, as a result, and tasks should only be deployed in a real scenario if the deadline is guaranteed to be met. Numerous simulation tools are proposed for traditional real-time systems using desktop technologies, but these relatively older tools do not adapt to the new constraints imposed by the IoT paradigm. In this paper, we design and implement a cloud-based novel architecture for the formal verification of IoT jobs and provide a simulation environment for a typical RT-IoT application where the feasibility of real-time remote tasks is perceived. The proposed tool, to the best of our knowledge, is the first of its kind effort to support not only the feasibility analysis of real-time tasks but also to provide a real environment in which it formally monitors and evaluates different IoT tasks from anywhere. Furthermore, it will also act as a centralised server for evaluating and tracking the real-time scheduled jobs in a smart space. The novelty of the platform is purported by a comparative analysis with the state-of-art solutions against attributes which is vital for any open-source tools in general and IoT in specifics.

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.

Multi-objective league championship algorithm for real-time task scheduling

League championship algorithm is a recently proposed population-based evolutionary algorithm for finding global optimal solutions in continuous optimization problems. The proposed work adopts the algorithm by modifying the team formation step for solving real-time task scheduling problem in heterogeneous multiprocessors. Two different objectives: tardiness and energy consumption were considered for scheduling. Our proposed algorithm is implemented using Java and tested using the graphs generated by the benchmark tools: task graph for free and task graph generator. Simulation results prove the performance of the proposed algorithm is better in terms of the objective functions over the other existing metaheuristic algorithms such as genetic algorithm, ant colony optimization and particle swarm optimization.

DPVFS: a dynamic procrastination cum DVFS scheduler for multi-core hard real-time systems

Optimising energy consumption has become the primary focus of research in recent years. Static and dynamic energy optimisation during task scheduling is one of the most prominent measures available. This is achieved mainly by shutdown and slowdown techniques. In uni-processor real-time systems, the most widely used shutdown and slowdown techniques are dynamic procrastination (DP) and dynamic voltage and frequency scaling (DVFS). This paper proposes DPVFS a hard real-time task scheduler for multi-core (MC) system to optimise overall energy consumption without deadline misses. DPVFS combines DP and DVFS for MC systems to save overall energy consumption. DPVFS shuts the processor down whenever possible with the help of procrastination. If shutdown is not possible, it adjusts the voltage and frequency to reduce dynamic energy consumption. The experimental evaluation of DPVFS with synthetically generated benchmark program suites shows savings of 18.8% and 33.2% of overall energy over DP based schedulers and DVFS based schedulers respectively.

Energy-Efficient Scheduling of Real-Time Tasks in Reconfigurable Homogeneous Multicore Platforms

This paper deals with real-time scheduling of homogeneous multicore platforms powered by a battery to be periodically recharged. A system is composed of reconfigurable real-time dependent and periodic tasks to be assigned to different cores interconnected by a network-on-chip (NoC). The system is subject to reconfigurations, which are automatic operations allowing the addition and/or removal of tasks as well as their exchanged messages on the NoC. Consequently, any reconfiguration can violate real-time and energy constraints on cores as well as the NoC when the energy is unavailable until the next recharge. A novel periodic task model based on elastic coefficients and a new scheduling strategy are proposed to compute useful temporal parameters allowing for tasks and messages to meet the related constraints while controlling the communication cost on the NoC. This strategy is compared with an integer linear programming-based optimal solution, and a tool named OptimalMappingTasks is developed to run different simulations that prove the originality of this paper’s contribution.

Hierarchical scheduling of real-time tasks over Linux-based virtual machines

Virtualization has made feasible the full isolation of virtual machines (VMs) among each other. When applications running within VMs have real-time constraints, threads implementing the virtual cores must be scheduled in a predictable manner over the physical cores. In this paper, we propose a possible implementation of such a predictable VM scheduling based on Linux and kvm (a hosted hypervisor). The proposed implementation is based on vanilla Linux kernels and standard qemu/kvm, and does not require to apply any patch or to use custom software. We also show that previous work makes some assumptions that are unrealistic in practical situations. Motivated by these considerations, we finally propose a principled methodology to practically implement hierarchical scheduling with Linux. Finally, an extensive set of experiments based on Linux and kvm illustrates how the VMs and host scheduler can be set-up to match theoretical results with experiments.

Execution-Efficient Response Time Analysis on Global Multiprocessor Platforms

Response time analysis (RTA) is an important and fundamental tool for analyzing the schedulability of real-time tasks on multiprocessor platforms, and many promising techniques have been developed during the past few years. However, most of the existing researches focus on improving the analysis precision, while less has been done on enhancing the execution efficiency. In this paper, we take, to our best knowledge, the first effort towards improving the efficiency of the state-of-the-art RTA methods for sporadic tasks under both global fixed-priority (G-FP) and global earliest deadline first (G-EDF) scheduling. Specifically, three factors that impact the efficiency of existing global RTA tests are identified: 1) pessimistic initial value when computing the worst-case response time (WCRT) of tasks under both G-FP and G-EDF; 2) conservative interference upper bound under both G-FP and G-EDF; and 3) unnecessary recalculation of WCRTs when there is update of any task WCRT under G-EDF. By addressing these three limitations, we propose two efficient RTA methods for G-FP and G-EDF scheduling, respectively, which achieve better run-time performance but without sacrificing any analysis precision. Experimental evaluations with randomly generated task sets show that the proposed methods exhibit remarkable performance improvements and can save on average 60 and 61 percent run time, as compared to the state-of-the-art technologies under G-FP and G-EDF scheduling, respectively.

On arrival scheduling of real-time precedence constrained tasks on multi-processor systems using genetic algorithm

This paper models the problem of precedence based real-time task scheduling as a dynamic constraint problem. It presents a new scheduling approach, termed as ’Dynamic Genetic Algorithm for Real-Time Scheduling’ (dGA-RTS). The significant feature of dGA-RTS is that it can handle dynamic as well as static scheduling of inter-dependent tasks for real-time systems. In parallel or distributed systems, the main aim of dynamic task scheduling is to allocate processors to a given set of tasks to execute them within optimized completion times without violating the task dependencies, if any. The dGA-RTS schedules the tasks in the waiting list when any new task arrives in the scheduler and minimizes the overall schedule length of the task set ensuring deadline compliance. We also illustrate an implementation technique to deal with synchronization problems in multi-processor systems. In order to exhibit the applicability of our approach, we perform experiments with suitable benchmark as well as synthetic test cases. Further, we conduct extensive simulations and compare the results with different performance metrics. The comparative study of the results with existing approaches indicates that our proposed approach is more efficient in generating feasible solutions.

Hierarchical scheduling for real-time agile satellite task scheduling in a dynamic environment

The imaging processes of optical satellites can be easily affected by unexpected environmental changes, such as changes in cloud coverage. Given the difficulty to predict environmental uncertainties, traditional offline scheduling methods need a follow-up re-scheduling process that responds to real-time environmental information. This repetitive scheduling processes make the offline fine scheduling process a waste of computational resources. Additionally, the offline scheduling method is quite complex owing to its lack of a hierarchy mechanism. To solve these problems, we propose a hierarchical scheduling method for the real-time scheduling problem. This method divides the scheduling process into three steps: pre-assignment, rough scheduling, and fine scheduling. A hierarchical scheduling algorithm based on ant colony algorithm is proposed. Tests with 36 scenarios show that the calculation time is efficiently reduced with this new mechanism. With this consideration of the dynamic environment, the re-scheduling process becomes unnecessary, meaning the wasting of computational resources is avoided and the solution profit is improved.

Slack clustering for scheduling frame-based tasks on multicore embedded systems

Adopting multicore platforms is a general trend in real-time embedded systems. However, integrating tasks with different real-time constraints into a single platform presents new design challenges. While it must be guaranteed that hard real-time tasks are able to meet their deadline even in worst case scenarios, firm real-time tasks should be scheduled in a way to achieve high system utilization in order to provide a better quality of service. In this paper, we propose a scheduling scheme for frame-based tasks on real-time multicore embedded systems which is able to guarantee the schedulability of the hard real-time tasks, while it improves the number of executed firm real-time tasks. Considering intrinsic variation of execution time of tasks and with the help of a clustering algorithm, hard real-time tasks are scheduled in a way that their expected slack time could be exploited more efficiently for the execution of firm tasks. The extensive simulations show that our proposed scheme can improve the system utility up to 60% compared to a state-of-the-art scheduling technique.

An Adaptive Approach Based on Resource-Awareness Towards Power-Efficient Real-Time Periodic Task Modeling on Embedded IoT Devices

Embedded devices are gaining popularity day by day due to the expanded use of Internet of Things applications. However, these embedded devices have limited capabilities concerning power and memory. Thus, the applications need to be tailored in such a way to perform the specified tasks within the constrained resources with the same accuracy. In Real-Time task scheduling, one of the challenging factors is the intelligent modelling of input tasks in such a way that it produces not only logically correct output within the deadline but also consumes minimum CPU power. Algorithms like Rate Monotonic and Earliest Deadline First compute hyper-period of input tasks for periodic repetition of the same set of tasks on CPU. However, at times when the tasks are not adequately modelled, they lead to an enormously high value of hyper-period which result in more CPU cycles and power consumption. Many state-of-the-art solutions are presented in this regard, but the main problem is that they limit tasks from having all possible period values; however, with the vision of Industry 4.0, where most of the tasks will be doing some critical manufacturing activities, it is highly discouraged to prevent them of a certain period. In this paper, we present a resource-aware approach to minimise the hyper-period of input tasks based on device profiles and allows tasks of every possible period value to admit. The proposed work is compared with similar existing techniques, and results indicate significant improvements regarding power consumptions.