Real-time Scheduling Algorithm Research Articles

Real-time cyber/physical interplay in scheduling for peak load optimisation in Cyber–Physical Energy Systems

This paper is about reducing the power consumption of Cyber–Physical Energy Systems (CPESs) composed of many loads through the usage of a scheduling technique inspired by the real-time-computing domain; the electric-load coordination guarantees a more efficient operation of the entire system by avoiding unnecessary concurrent activation of loads and thus limiting the peak load. We represent the power loads themselves as “physical” components and the computing devices that coordinate them as “cyber” components and formally derive the relationship between the operations in cyber and physical domains as the interplay between the schedules enforced on each component: indeed, the schedule of the loads – generated by combining a two-dimensional bin-packing and an optimal multi-processor real-time scheduling algorithm – influences the timing of the processing tasks that are dedicated to the activation/deactivation of loads themselves. We also consider non-schedulable loads by introducing a policy to cope with the presence of such loads. Numerical simulations and experiments confirm the good performance of the proposed peak load reduction method. The usage of real-time scheduling in this context provides inherent resource optimisation by limiting the number of concurrent loads that are active at the same time, thus directly reducing the overall peak load; moreover, thanks to the limited computational complexity of the algorithms, it scales to large systems, overcoming the scalability issues of common optimisation methods. Numerical simulations and experiments confirm the good performance of the proposed peak load reduction method.

Enhancing fault tolerance: dual Q-learning with dynamic scheduling

<span>Cloud computing has revolutionized IT delivery by offering scalable on-demand internet services encompassing software, platforms, and infrastructure. However, cloud services face significant performance challenges due to their susceptibility to failures given their vast operational scale. Implementing fault tolerance in dynamic cloud services is a key challenge, with complex configurations and dependencies complicating deployment. This paper introduces an innovative approach that combines double deep Q-learning (DDQL) with a dynamic fault-tolerant real-time scheduling algorithm (DFTRTSA) to enhance fault tolerance in real-time systems. DDQL, an extension of deep Q-learning, optimizes the fault-tolerance decision-making process. The algorithm adjusts scheduling strategies dynamically based on system conditions and errors. The fusion of DDQL and DFTRTSA aims to create a resilient and adaptive fault-tolerant mechanism, ensuring uninterrupted operation while meeting real-time requirements. This adaptive approach efficiently manages resources, meets deadlines, and gracefully handles errors, as demonstrated through experiments. Our DDQL-DFTRTSA method outperforms conventional fault-tolerant mechanisms in defect tolerance, energy efficiency, downtime reduction, and system dependability. It proves to be an ideal solution for real-time systems in dynamic and unpredictable environments.</span>

A multi-skill RCPSP variant for persons with disabilities in sheltered workshops

Sheltered workshops face the challenge of meeting their manufacturing objectives while considering the individual competencies and assistance needs of persons with disabilities. Moreover, work processes in sheltered workshops are weakly structured, allowing for frequent task interruptions and changes based on the preferences of impaired work persons. While the Industry 4.0 literature provides many real-time scheduling algorithms for incorporating multiple objectives and constraints, these algorithms fall short of the characteristics of sheltered workshops. The resource-constrained project scheduling problem (RCPSP) is an optimization problem for computing manufacturing plans considering multiple objectives and resource constraints. Among many different RCPSP variants proposed in the literature, the multi-skill RCPSP (MSRCPSP) variant considers the individual skills of work persons when generating manufacturing plans. With the ongoing digital transformation of enterprises, new assistance systems enter the market, providing individual support to impaired work persons and compensating for lacking skills and abilities.This paper proposes an MSRCPSP variant that incorporates assistance systems and learning tasks compensating for competence gaps in the skill matrix of impaired work persons. Furthermore, it decomposes tasks into individual work objects, which accounts for frequent task interruptions and task preferences of work persons. The algorithm is described and demonstrated using a manufacturing data set from an actual sheltered workshop. In a small evaluation study, the algorithm is tested by scheduling two impaired work persons in the assembly department of a medium-sized manufacturing company in the primary labor market. The evaluation study proves its real-world applicability and the suitability of scheduling algorithms for participation and inclusive work.

LAG-based schedulability analysis for preemptive global EDF scheduling with dynamic cache allocation

In recent years, the maturation of modern multicore processor technology and its increasing adoption in critical industrial domains have posed significant challenges for real-time systems, primarily due to contention for shared cache resources and the resulting uncertainty. To address this issue, contemporary processors employ cache partitioning techniques, enhancing temporal predictability by isolating cache access among processor cores. However, this isolation technique may lead to real-time tasks missing their deadlines due to an insufficient number of cache partitions. Consequently, this paper investigates the schedulability of preemptive global Earliest Deadline First (EDF) real-time scheduling algorithms that support dynamic cache allocation. We propose an innovative LAG-based schedulability analysis method for these algorithms and present a utilization-based schedulability condition that reduces analysis time complexity while improving analysis accuracy. Building upon this foundation, and incorporating task characteristics into our analysis, this paper further introduces an optimization technique aimed at minimizing the pessimism inherent in the schedulability test. Lastly, the performance and efficiency of the proposed schedulability determination method are validated through simulation experiments with randomly generated tasks.

A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors

As multiprocessor (or multicore) real-time systems become popular, there has been much research on multiprocessor real-time scheduling algorithms. This work evaluates EDZL (Earliest Deadline until Zero Laxity), a scheduling algorithm for real-time multiprocessor systems. First, we compare the performance of EDZL schedulability tests. We measure and compare the ratio of task sets admitted by each test. We also investigate the dominance between EDZL schedulability tests and discover that the union of the demand-based test and the utilization-based test is an effective combination. Second, we compare the schedulability of EDZL and EDF(k). We prove that the union of the EDZL schedulability tests dominates the EDF(k) schedulability test, i.e., the union of the EDZL schedulability tests can admit all task sets admitted by the EDF(k) schedulability test. We also compare the schedulability of EDZL and EDF(k) through scheduling simulation by measuring the ratio of successfully scheduled task sets. EDZL can successfully schedule 7.0% more task sets than EDF(k).

Generating Datasets for Real-Time Scheduling on 5G New Radio.

A 5G system is an advanced solution for industrial wireless motion control. However, because the scheduling model of 5G new radio (NR) is more complicated than those of other wireless networks, existing real-time scheduling algorithms cannot be used to improve the 5G performance. This results in NR resources not being fully available for industrial systems. Supervised learning has been widely used to solve complicated problems, and its advantages have been demonstrated in multiprocessor scheduling. One of the main reasons why supervised learning has not been used for 5G NR scheduling is the lack of training datasets. Therefore, in this paper, we propose two methods based on optimization modulo theories (OMT) and satisfiability modulo theories (SMT) to generate training datasets for 5G NR scheduling. Our OMT-based method contains fewer variables than existing work so that the Z3 solver can find optimal solutions quickly. To further reduce the solution time, we transform the OMT-based method into an SMT-based method and tighten the search space of SMT based on three theorems and an algorithm. Finally, we evaluate the solution time of our proposed methods and use the generated dataset to train a supervised learning model to solve the 5G NR scheduling problem. The evaluation results indicate that our SMT-based method reduces the solution time by 74.7% compared to existing ones, and the supervised learning algorithm achieves better scheduling performance than other polynomial-time algorithms.

RTGPU: Real-Time GPU Scheduling of Hard Deadline Parallel Tasks With Fine-Grain Utilization

Many emerging cyber-physical systems, such as autonomous vehicles and robots, rely heavily on artificial intelligence and machine learning algorithms to perform important system operations. Since these highly parallel applications are computationally intensive, they need to be accelerated by graphics processing units (GPUs) to meet stringent timing constraints. However, despite the wide adoption of GPUs, efficiently scheduling multiple GPU applications while providing rigorous real-time guarantees remains a challenge. Each GPU application has multiple CPU execution and memory copy segments, with GPU kernels running on different hardware resources. Because of the complicated interactions between heterogeneous segments of parallel tasks, high schedulability is hard to achieve with conventional approaches. This paper proposes RTGPU, which combines fine-grain GPU partitioning on the system side with a novel scheduling algorithm on the theory side. Through system and theory co-design, RTGPU achieves superior system throughput and real-time schedulability. In this paper, we start by building a model for the CPU and memory copy segments. Leveraging persistent threads, we then implement fine-grained GPU partitioning with improved performance through interleaved execution. To reap the benefits of fine-grained GPU partitioning and schedule multiple parallel GPU applications, we propose a novel real-time scheduling algorithm based on federated scheduling and grid search with uniprocessor fixed-priority scheduling. Our approach provides real-time guarantees to meet hard deadlines, and achieves over 11% improvement in system throughput and up to 57% schedulability improvement compared with previous work. We validate and evaluate RTGPU on NVIDIA GTX1080Ti GPU systems. Our system side techniques can be applied on mainstream NVIDIA GPUs, and the proposed scheduling theory can be used in general heterogeneous computing platforms which have a similar task execution pattern.

The Scheduling Research of a Wind-Solar-Hydro Hybrid System Based on a Sand-Table Deduction Model at Ultra-Short-Term Scales

Establishing a wind-solar-hydro hybrid generation system is an effective way of ensuring the smooth passage of clean energy into the grid, and its related scheduling research is a complex and real-time optimization problem. Compared with the traditional scheduling method, this research investigates and improves the accuracy of the scheduling model and the flexibility of the scheduling strategy. The paper innovatively introduces a sand-table deduction model and designs a real-time adaptive scheduling algorithm to evaluate the source-load matching capability of the hybrid wind-solar-hydro system at ultra-short-term scales, and verifies it through arithmetic examples. The results show that the proposed adaptive sand-table scheduling model can reflect the actual output characteristics of the hybrid wind-solar-hydro system, track the load curve, and suppress the fluctuation of wind and solar energy, with good source-load matching capability.

A systematic review on security aware real-time task scheduling

Nowadays, security and scheduling are the most crucial aspects of each distributed real-time application due to the rapidly enhanced use of such applications. This study presents a systematic literature review (SLR) on security and scheduling techniques for real-time applications in homogeneous and heterogeneous environments and to identify areas that need more attention for a wide range of applicability of real-time applications. The search procedure identified 3162 articles, and 103 best relevant articles are selected based on quality assessment scores for a brief review and discussions with the area experts or academicians. This study considers articles with a quality assessment score of more than five for a brief review. Hence, the applications related to security, scheduling, load balancing, fixed priority, task, job, and packet scheduling, and deadline constraints of soft and hard real-time systems in homogeneous and heterogeneous environments as per the state-of-the-art have been discussed. This discussion provides a better understanding of the real-time applications. The survey thus, aims to present the improvements in real-time task scheduling with security constraints in different distributed computing environments. The SLR also highlights various real-time task scheduling algorithms/techniques including the role of security. This SLR also provides meaningful and valuable study to the researchers by providing brief studies of various real-time task scheduling algorithms and security needs in tabular form.

A New Algorithm for Real-Time Scheduling and Resource Mapping for Robot Operating Systems (ROS)

A new version of a robot operating system (ROS-2) has been developed to address the real-time and fault constraints of distributed robotics applications. However, current implementations lack strong real-time scheduling and the optimization of response time for various tasks and applications. This may lead to inconsistent system behavior and may affect system performance. This article presents a new and efficient heuristic scheduling algorithm for the ROS-2 framework to improve resource mapping and system scheduling for robotics applications. The proposed scheduling design includes grouping callbacks into executors, assigning executor priority, and sequencing callbacks inside executors. The timing constraints and functional properties are expressed in the formal design model, and real-time scheduling is performed with respect to the timing constraints. The benefits of the proposed solution are demonstrated by a set of experimental results. Using this algorithm, it is shown that the number of executors needed to schedule a set of callbacks is minimized and the system schedulability is maximized for loaded and overloaded cases.

A Whale Optimization Algorithm Based Resource Allocation Scheme for Cloud-Fog Based IoT Applications

Fog computing has been prioritized over cloud computing in terms of latency-sensitive Internet of Things (IoT) based services. We consider a limited resource-based fog system where real-time tasks with heterogeneous resource configurations are required to allocate within the execution deadline. Two modules are designed to handle the real-time continuous streaming tasks. The first module is task classification and buffering (TCB), which classifies the task heterogeneity using dynamic fuzzy c-means clustering and buffers into parallel virtual queues according to enhanced least laxity time. The second module is task offloading and optimal resource allocation (TOORA), which decides to offload the task either to cloud or fog and also optimally assigns the resources of fog nodes using the whale optimization algorithm, which provides high throughput. The simulation results of our proposed algorithm, called whale optimized resource allocation (WORA), is compared with results of other models, such as shortest job first (SJF), multi-objective monotone increasing sorting-based (MOMIS) algorithm, and Fuzzy Logic based Real-time Task Scheduling (FLRTS) algorithm. When 100 to 700 tasks are executed in 15 fog nodes, the results show that the WORA algorithm saves 10.3% of the average cost of MOMIS and 21.9% of the average cost of FLRTS. When comparing the energy consumption, WORA consumes 18.5% less than MOMIS and 30.8% less than FLRTS. The WORA also performed 6.4% better than MOMIS and 12.9% better than FLRTS in terms of makespan and 2.6% better than MOMIS and 4.3% better than FLRTS in terms of successful completion of tasks.

The BH-mixed scheduling algorithm for DAG tasks with constrained deadlines

The development of real-time systems has stimulated studies of real-time scheduling algorithms and schedulability analysis methods for parallel tasks, whose number has increased from year to year. For parallel tasks that cannot be converted to sequential tasks, the existing scheduling strategies mostly use one scheduling strategy. From the experiment results when we use different scheduling strategies to schedule the same tasks, we can observe these strategies have advantages for tasks with specific parameter characteristics. We combine the strengths of three algorithms: the partitioned algorithm, the federated scheduling algorithm and the GFP algorithm. A BH-Mixed scheduling algorithm for directed acyclic graph tasks with constrained deadlines is proposed in this paper. We design a classification method according to the feature of these three algorithms. The tasks are divided into three groups and scheduled by the partitioned algorithm, the federated scheduling algorithm and the GFP algorithm respectively. Experimental results show that the BH-Mixed scheduling algorithm outperforms other algorithms tested in this paper.

Real-Time Charging Scheduling and Optimization of Electric Buses in a Depot

To improve the air quality in urban areas, diesel buses are getting replaced by battery electric buses (BEBs). This conversion introduces several challenges, such as the proper control of the charging process and a reduction in the operational costs, which can be addressed by introducing smart charging concepts for BEB fleets. Therefore, this paper proposes a real-time scheduling and optimization (RTSO) algorithm for the charging of multiple BEBs in a depot. The algorithm assigns a variable charging current to the different time slots the charging process of each BEB is divided to provide an optimal charging schedule that minimizes the charging cost, while satisfying the power limitations of the distribution network and maintaining the operation schedule of the BEBs. A genetic algorithm is used to solve the formulated cost function in real time. Several charging scenarios are tested in simulation, which show that a reduction in the charging cost up to 10% can be obtained under a dynamic electricity price scheme. Furthermore, the RTSO is implemented in a high-level charging management system, a new feature required to enable smart charging in practice, to test the developed algorithm with existing charging infrastructure. The experimental validation of the RTSO algorithm has proven the proper operation of the entire system.

Real-Time Data Transmission Scheduling Algorithm for Wireless Sensor Networks Based on Deep Q-Learning

In the industrial environment, the data transmission of Wireless Sensor Networks (WSNs) usually has strict deadline requirements. Improving the reliability and real-time performance of data transmission has become one of the critical issues in WSNs research. One of the main methods to improve the network performance of WSNs is to schedule the transmission process. An effective scheduling algorithm can meet the requirements of a strict industrial environment for network performance, which is of great research significance. Aiming at the problem of concurrent data transmission in WSNs, a real-time data transmission scheduling algorithm based on deep Q-learning is proposed. The algorithm comprehensively considers the influence of the remaining deadline, remaining hops, and unassigned time-slot nodes in the data transmission process, defines the reward function and action selection strategy of Q-learning, and guides the system state information transfer process. At the same time, deep learning and Q-learning are combined to solve the problem of disaster maintenance caused by the large scale of the system state. A multi-layer Stacked Auto Encoder (SAE) network model establishes the state-action mapping relationship, and the Q-learning algorithm updates it. Finally, according to the trained SAE network model, the data transmission scheduling strategy of the system in different states is obtained. The network performance of the proposed data transmission scheduling algorithm is analyzed and evaluated by simulation experiments. The simulation results show that compared with the commonly used heuristic algorithms, the proposed algorithm improves real-time performance and can better meet the data transmission requirements of high reliability and real-time WSNs.

Improving patient satisfaction and outpatient diagnostic center efficiency using novel online real-time scheduling

We develop a novel online real-time scheduling algorithm with applications for healthcare diagnostic centers to deal with walk-in patients based on a set of constraints on the sequence of tests and resources. The problem is especially significant at healthcare centers in developing and emerging nations, such as India, where appointment schedules do not work. Within this realistic context, our objective is to improve patient satisfaction by reducing waiting time and improve diagnostic center performance through better utilization of the constrained resources. We propose a Mixed Integer Linear Programming (MILP) formulation to represent diagnostic centers as a Flow and Open Shop, to capture the system dynamics of the Flexible Hybrid Shop Scheduling Problem. We then develop a novel Online Genetic Algorithm (OGA) capable of solving real life large scale problems, as Open Shop scheduling problems are NP-hard. The developed OGA is first validated for small instances against a theoretical lower bound and the MILP model using CPLEX solver for flow time and makespan. The OGA is then empirically validated with data collected from two diagnostic centers of different sizes and configurations. For both centers, the developed OGA shows significant improvement compared to the simulation model. This research offers an important contribution to both literature and practice as it is one of the first to model the patient scheduling problem as an online real-time process. Implementing the developed OGA would help diagnostic centers significantly improve time estimates, thus reducing actual patient time and improving the efficiency of the system. Most importantly, the OGA is generalizable beyond healthcare to a broad range of environments that share Hybrid Shop characteristics.

Real-time scheduling simulation optimisation of job shop in a production-logistics collaborative environment

ABSTRACT In a complex and dynamic job shop containing logistics factor, schedule needs to be generated rapidly, so the real-time scheduling method is more suitable for such scenario. Such method takes advantage of local information within a short time due to the rapid changes of information under uncertain environment. Therefore, how to make use of the future information by prediction while ensuring the robustness of schedule is a valuable problem. To solve it, firstly, a new real-time scheduling model and algorithm is proposed. There is a new kind of release moment of task information which can give AGVs the longest time to prepare for the task than existing research. Secondly, a real-time information update mechanism is designed to increase schedule’s robustness. Finally, a large-scale and dynamic job shop simulation experimental platform is developed. Dynamic factors include the random insertion of orders and failures of equipment. Results show that the method proposed outperforms existing research in terms of customer satisfaction, equipment utilisation and energy consumption. The robustness of schedule can also be acceptable. This paper also finds a rule that in job shop with the large proportion of logistics transportation time, the above method can achieve more competitive results.

Toward Energy-efficient Communication Protocol in WBAN: A Dynamic Scheduling Policy Approach

Wireless body area network is an emerging technology that has been able to provide a better experience of mobility and flexibility for humans using tiny and low power sensors inside, outside, or around the body compared to the traditional wired monitoring systems. Due to numerous constraints in size, energy consumption, and security of implant devices in the human body, it is still a significant research challenge to design these systems in a reliable and energy-efficient fashion. To provide quality of service, timely and secure delivery of real-time data needs be done without any loss. This paper attempts to provide a communication protocol in order to upgrade QoS levels in WBANs and reduce energy consumption in sensor nodes. To do so, the EDF real-time scheduling algorithm and its combination with the LLF scheduling algorithm are employed to prioritize sensor nodes for sending data packets. The proposed method could optimize the system performance when it is in the event of an overload and tasks miss their deadlines in a row. The OMNET++ simulation environment is used to evaluate the proposed solution's efficiency which checks packet delivery rate and mean-power consumption evaluation criteria in the sink and sensor nodes. This is done with different numbers of nodes in the network. The results show that the proposed strategy could provide an appropriate improvement in sending and receiving packets for body area networks.

A Lightweight Optimal Scheduling Algorithm for Energy-Efficient and Real-Time Cloud Services

To support ever-chainging user needs such as large storage volumes, web search, and high-performance computing, numerous companies have expanded their systems to cloud computing servers. Cloud environment systems generally consume large amounts of electrical power, leading to tremendously high operational costs. In addition, they require computing infrastructures to run various real-time applications such as financial analysis, cloud gaming, and web-based real-time services. To represent performance guarantees, the negotiated agreements in real-time computing, expressed as deadline (or latency), can be specified by <i>service level agreements</i> of cloud services between users and cloud server providers. Thus, a number of research works have started focusing on reducing the energy consumption and simultaneously satisfying the temporal constraint in a cloud environment. Although we previously proposed an optimal real-time scheduling algorithm for multiprocessors, it is difficult to use it for cloud environments handling a large number of cloud services because of the high computational complexity of <inline-formula> <tex-math notation="LaTeX">$\Omega (N^{3}logN)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is the number of tasks. Thus, we introduce a real-time task scheduling algorithm for cloud computing servers, which alleviates the computational complexity of <inline-formula> <tex-math notation="LaTeX">$O(N^{2})$ </tex-math></inline-formula> from the complexity of the previous algorithm using a novel flow network-based optimization method. To the best of our knowledge, our scheduling algorithm in a cloud environment, which ensures optimality for real-time tasks and achieves energy savings using dynamic power management simultaneously, is the first in the problem domain. We show that the proposed scheduling algorithm guarantees an optimal schedule for real-time tasks and achieves energy savings simultaneously. Our experimental results show that the proposed algorithm outperforms the latest existing algorithms in terms of both time complexity and energy efficiency.

Towards sustainable industry 4.0: A green real-time IIoT multitask scheduling architecture for distributed 3D printing services

As the keystones of the personalized manufacturing, the Industrial Internet of Things (IIoT) consolidated with 3D printing pave the path for the era of Industry 4.0 and smart manufacturing. By resembling the age of craft manufacturing, Industry 4.0 expedites the alteration from mass production to mass customization. When distributed 3D printers (3DPs) are shared and collaborated in the IIoT, a promising dynamic, globalized, economical, and time-effective manufacturing environment for customized products will appear. However, the optimum allocation and scheduling of the personalized 3D printing tasks (3DPTs) in the IIoT in a manner that respects the customized attributes submitted for each model while satisfying not only the real-time requirements but also the workload balancing between the distributed 3DPs is an inevitable research challenge that needs further in-depth investigations. Therefore, to address this issue, this paper proposes a real-time green-aware multi-task scheduling architecture for personalized 3DPTs in the IIoT. The proposed architecture is divided into two interconnected folds, namely, allocation and scheduling. A robust online allocation algorithm is proposed to generate the optimal allocation for the 3DPTs. This allocation algorithm takes into consideration meeting precisely the customized user-defined attributes for each submitted 3DPT in the IIoT as well as balancing the workload between the distributed 3DPs simultaneously with improving their energy efficiency. Moreover, meeting the predefined deadline for each submitted 3DPT is among the main objectives of the proposed architecture. Consequently, an adaptive real-time multi-task priority-based scheduling (ARMPS) algorithm has been developed. The built ARMPS algorithm respects both the dynamicity and the real-time requirements of the submitted 3DPTs. A set of performance evaluation tests has been performed to thoroughly investigate the robustness of the proposed algorithm. Simulation results proved the robustness and scalability of the proposed architecture that surpasses its counterpart state-of-the-art architectures, especially in high-load environments.

Enhanced real-time scheduling algorithm for energy management in a renewable-integrated microgrid

The day-ahead energy management scheme (EMS) of a microgrid faces different uncertainties due to the inaccuracy of predictions. Moreover, offline EMS assumes that renewable generation and load demand predictions are perfect, which is practically difficult to achieve. Although real-time EMS (RT-EMS) can overcome these uncertainties by employing online measurements, utilization of storage units in RT-EMS needs further investigation because the solution does not achieve the global optimal. In this study, a multi-objective RT-EMS is proposed by considering the costs and life cycle environmental impacts of energy sources. The suggested RT-EMS is designed using Lyapunov optimization method and then enhanced through the proposed adaptive utilization of the storage units. The charging and discharging of the storage units are scheduled adaptive to the time-of-use market price, resulting in lower environmental impacts because the life cycle environmental impact of energy storage is significant. Therefore, the proposed RT-EMS is a multi-objective problem, and a Pareto front is derived from compromising between the operational cost and the environmental impacts. The proposed market price-based RT-EMS is evaluated using numerical studies on the modified IEEE 33-bus test system and real-world data.