Scheduling Of Real-time Tasks Research Articles

On the degree of parallelism for parallel real-time tasks

The degree of parallelism, which measures how a task can execute concurrently, is an important characterization in scheduling. This paper studies the degree of parallelism in the domain of real-time scheduling of parallel tasks, including the DAG task model and the conditional DAG task model. The definition of the degree of parallelism for DAG tasks is clarified; the definition and computing algorithm of the degree of parallelism for conditional DAG tasks are proposed. By leveraging the degree of parallelism, new response time bounds are derived and simple but effective real-time scheduling approaches are presented. This research is the first work to study the degree of parallelism for conditional DAG tasks and explore its benefits in real-time scheduling. Experimental results demonstrate that the proposed scheduling approaches significantly outperform existing state-of-the-art methods.

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.

Real-time scheduling of power grid digital twin tasks in cloud via deep reinforcement learning

As energy demand continues to grow, it is crucial to integrate advanced technologies into power grids for better reliability and efficiency. Digital Twin (DT) technology plays a key role in this by using data to monitor and predict real-time operations, significantly enhancing system efficiency. However, as the power grid expands and digitization accelerates, the data generated by the grid and the DT system grows exponentially. Effectively handling this massive data is crucial for leveraging DT technology. Traditional local computing faces challenges such as limited hardware resources and slow processing speeds. A viable solution is to offload tasks to the cloud, utilizing its powerful computational capabilities to support the stable operation of the power grid. To address the need, we propose GD-DRL, a task scheduling method based on Deep Reinforcement Learning (DRL). GD-DRL considers the characteristics of computational tasks from the power grid and DT system and uses a DRL agent to schedule tasks in real-time across different computing nodes, optimizing for processing time and cost. We evaluate our method against several established real-time scheduling techniques, including Deep Q-Network (DQN). Our experimental results show that the GD-DRL method outperforms existing strategies by reducing response time, lowering costs, and increasing success rates.

Algorithmic approach leveraging a real-time task scheduler with fan-out strategy

Algorithmic approach leveraging a real-time task scheduler with fan-out strategy

Energy efficient Security Driven Scheduling for Real-Time Tasks through DVS-enabled Fog Networks

The issue of task scheduling for a multi-core processor in Fog networks, with a focus on security and energy efficiency is of great importance in real-time systems. Currently, scheduling algorithms designed for cluster computing environments utilize dynamic voltage scaling (DVS) to decrease CPU power consumption, albeit at the expense of performance. This problem becomes more pronounced when a real-time task requires robust security, resulting in heavily overloaded nodes (CPUs or computing systems) in a cluster computing environment. To address such challenges, a solution called “Energy efficient Security Driven Scheduling of Real-Time Tasks using DVS-enabled Fog Networks (ESDS)” has been proposed. The primary goal of ESDS is to dynamically adjust CPU voltages or frequencies based on the workload conditions of nodes in Fog networks, thereby achieving optimal trade-offs between security, scheduling, and energy consumption for real-time tasks. By dynamically reducing voltage or frequency levels, ESDS conserves energy while still meeting deadlines for both running and new tasks, especially during periods of high system workload. Comprehensive experiments have been carried out to compare the ESDS algorithm with established baseline algorithms, including MEG, MELV, MEHV, and AEES. These experiments affirm the originality and effectiveness of the ESDS algorithm.

Service-Aware Hierarchical Fog–Cloud Resource Mappingfor e-Health with Enhanced-Kernel SVM

Fog–cloud-based hierarchical task-scheduling methods are embracing significant challenges to support e-Health applications due to the large number of users, high task diversity, and harsher service-level requirements. Addressing the challenges of fog–cloud integration, this paper proposes a new service/network-aware fog–cloud hierarchical resource-mapping scheme, which achieves optimized resource utilization efficiency and minimized latency for service-level critical tasks in e-Health applications. Concretely, we develop a service/network-aware task classification algorithm. We adopt support vector machine as a backbone with fast computational speed to support real-time task scheduling, and we develop a new kernel, fusing convolution, cross-correlation, and auto-correlation, to gain enhanced specificity and sensitivity. Based on task classification, we propose task priority assignment and resource-mapping algorithms, which aim to achieve minimized overall latency for critical tasks and improve resource utilization efficiency. Simulation results showcase that the proposed algorithm is able to achieve average execution times for critical/non-critical tasks of 0.23/0.50 ms in diverse networking setups, which surpass the benchmark scheme by 73.88%/52.01%, respectively.

Task Scheduling of Real-Time Traffic Information Processing Based on Digital Twins

The Intelligent Transportation System under Digital Twins can provide accurate data sources for traffic control. The present work focuses on the real-time information processing and task scheduling problems of the Internet of Vehicles (IoV) system based on Virtual Reality. They are the Quality/Distance Algorithm (QDA), Task Density Algorithm, Distance Balance Algorithm (DBA), and Bionic-DBA (B-DBA). The simulation experiment analysis suggests that the DBA algorithm takes the balance of travel distance into account and effectively improves task quality. The Utility Function in B-DBA and the Biological Heuristic Search Algorithm in Pareto Ant Colony Optimization play a critically important role in enhancing the overall task quality. In addition, a Transmission based on Privacy Protection (TPP) algorithm is designed to protect the attribute-based privacy information in the traffic information transmission system. This algorithm ensures that the real-time traffic information processing system resists various attacks from malicious nodes. It has been verified that when the number of selfish nodes accounts for 30%, the transmission efficiency of the TPP algorithm reaches 0.77. The research content has a practical reference value for providing users with continuous and high-quality IoV network services.

NASLMRP: Design of a Negotiation Aware Service Level Agreement Model for Resource Provisioning in Cloud Environments

Cloud resource provisioning requires examining tasks, dependencies, deadlines, and capacity distribution. Scalability is hindered by incomplete or complex models. Comprehensive models with low-to-moderate QoS are unsuitable for real-time scenarios. This research proposes a Negotiation Aware SLA Model for Resource Provisioning in cloud deployments to address these challenges. In the proposed model, a task-level SLA maximizes resource allocation fairness and incorporates task dependency for correlated task types. This process's new tasks are processed by an efficient hierarchical task clustering process. Priority is assigned to each task. For efficient provisioning, an Elephant Herding Optimization (EHO) model allocates resources to these clusters based on task deadline and make-span levels. The EHO Model suggests a fitness function that shortens the make-span and raises deadline awareness. Q-Learning is used in the VM-aware negotiation framework for capacity tuning and task-shifting to post-process allocated tasks for faster task execution with minimal overhead. Because of these operations, the proposed model outperforms state-of-the-art models in heterogeneous cloud configurations and across multiple task types. The proposed model outperformed existing models in terms of make-span, deadline hit ratio, 9.2% lower computational cycles, 4.9% lower energy consumption, and 5.4% lower computational complexity, making it suitable for large-scale, real-time task scheduling.

Efficient and fair PPO-based integrated scheduling method for multiple tasks of SATech-01 satellite

SATech-01 is an experimental satellite for space science exploration and on-orbit demonstration of advanced technologies. The satellite is equipped with 16 experimental payloads and supports multiple working modes to meet the observation requirements of various payloads. Due to the limitation of platform power supply and data storage systems, proposing reasonable mission planning schemes to improve scientific revenue of the payloads becomes a critical issue. In this article, we formulate the integrated task scheduling of SATech-01 as a multi-objective optimization problem and propose a novel Fair Integrated Scheduling with Proximal Policy Optimization (FIS-PPO) algorithm to solve it. We use multiple decision heads to generate decisions for each task and design the action mask to ensure the schedule meeting the platform constraints. Experimental results show that FIS-PPO could push the capability of the platform to the limit and improve the overall observation efficiency by 31.5% compared to rule-based plans currently used. Moreover, fairness is considered in the reward design and our method achieves much better performance in terms of equal task opportunities. Because of its low computational complexity, our task scheduling algorithm has the potential to be directly deployed on board for real-time task scheduling in future space projects.

Real-Time Scheduling of Conditional DAG Tasks With Intra-Task Priority Assignment

The conditional DAG task model can represent the conditional execution flows that commonly exist in many real-time parallel applications. Previous work has shown that by properly assigning the priority among vertices inside a non-conditional DAG task, we can reduce the task response time and achieve better system schedulability. This paper studies how to apply intra-task priority assignment to conditional DAG tasks. We develop a response time bound that theoretically dominates the state-of-the-art and present a novel algorithm to compute the bound in polynomial time. We further extend the proposed approach to the general setting of multiple conditional DAG tasks. Experiments with one conditional DAG task and multiple conditional DAG tasks demonstrate that our method consistently outperforms the state-of-the-art by a considerable margin.

A Blockchain-Based Multi-Unmanned Aerial Vehicle Task Processing System for Situation Awareness and Real-Time Decision

With the rapid advancement of Unmanned Aerial Vehicle (UAV) technology, UAV swarms are being extensively applied in various fields, such as intelligent transportation, search and rescue, logistics delivery, and aerial mapping. However, the utilization of UAV swarms in sustainable transportation also presents some challenges, such as inefficient task allocation and data transmission security issues, highlighting the importance of privacy protection in this context. To address these issues, this study applies blockchain technology to multi-UAV tasks and proposes a blockchain-based multi-UAV task processing system for situation awareness and real-time decisions. The primary objective of this system is to enhance the efficiency of UAV swarm task scheduling, bolster data transmission security, and address privacy protection concerns. Utilizing the highly secure features of blockchain technology, the system constructs a distributed task processing network. System tasks are stored in the blockchain through smart contracts, ensuring the immutability and verifiability of task information. Smart contracts have an automatic execution capability, whereby the system can efficiently coordinate tasks and maintain the consistency of task execution information through consensus mechanisms. Additionally, adopting the Pointer Network structure for intelligent path planning based on task allocation results leads to the attainment of the shortest service routes, consequently expanding the service coverage of sustainable transportation systems while reducing energy consumption. This further advances the realization of urban sustainable transportation. Through experimental results, we verify that the proposed system enables real-time task scheduling and collaborative processing for multiple UAVs, significantly enhancing the efficiency, security, and privacy protection level of UAV swarm task execution in the context of sustainable transportation. It makes a positive contribution to building more sustainable urban transportation systems.

Energy-efficient scheduling of imprecise mixed-criticality real-time tasks based on genetic algorithm

Previous work focuses on the problem of energy for Imprecise Mixed-Criticality Systems on multiprocessor platforms, which provides degraded services for low criticality tasks in the high criticality mode. However, the proposed solution based on the partitioning heuristic Criticality-Unaware Worst Fit Decreasing method (CU-WFD) may consume more energy. In this paper, we propose a novel partitioning algorithm named EEAA based on the genetic algorithm and explore various parameter combinations, aiming to find a task-to-processor assignment method that can further reduce energy consumption with the best parameter combinations. In addition, we conduct experiments to evaluate EEAA and the experimental result shows that EEAA achieves energy savings of 12.56% compared to CU-WFD.

Energy-Minimized Scheduling of Intermittent Real-Time Tasks in a CPU-GPU Cloud Computing Platform

Energy-Minimized Scheduling of Intermittent Real-Time Tasks in a CPU-GPU Cloud Computing Platform

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.

Segment based power-efficient scheduling for real-time DAG tasks on edge devices

Smart Mobile Devices (SMDs) are crucial for the edge computing paradigm’s real-world sensing. Real-time applications, which are computationally intensive and periodic with strict time constraints, can typically be used to replicate real-world sensing. Such applications call for increased processing speed, memory capacity, and battery life on SMDs, which are typically resource-constrained due to physical size restrictions. As a result, scheduling real-time applications for SMDs that are power efficient is crucial for the regular operation of edge computing platforms, and downstream decision-making tasks like computation offloading require the prediction of power consumption using power-saving approaches like DVFS. The main question is how to swiftly develop a better solution to the NP-Hard power efficient scheduling problem with DVFS. Thus, by segmenting the aligned tasks on an SMD, we present a segment-based analysis approach. Additionally, we offer a segment-based scheduling algorithm (SEDF) that draws inspiration from the segment-based analysis approach to achieve power-efficient scheduling for these real-time workloads. This segment-based approach yields a power consumption bound (PB), and a computation offloading use case is developed to demonstrate the application of PB in the subsequent decision-making processes. Both simulations and actual device tests are used to confirm the PB, SEDF, and the effectiveness of offloading decision-making. We demonstrate empirically that PB can be utilized to make approximative optimal decisions in decision-making problems involving computation offloading. SEDF is a straightforward and effective scheduling approach that can cut the power consumption of a multi-core SMD by roughly 30%.

Mobility and Security Aware Real-Time Task Scheduling in Fog-Cloud Computing for IoT Devices: A Fuzzy-Logic Approach

Abstract This paper aims to improve the overall task processing time of mobile real-time Internet of Things (IoT) applications in fog-cloud computing, considering the various resource and security requirements along with the time constraints of the task. Fog computing extends the cloud resources to serve the IoT devices at the network edge. In such a scenario, deciding whether the tasks should be processed at the fog layer or submitted to the cloud is critical. Moreover, for real-time applications, the mobility of IoT devices and the limited bandwidth available at the edge devices endanger the low processing time of the task. Besides, the security demands of some IoT applications (i.e. healthcare) require processing the tasks by specific fog or cloud servers to assure confidentiality of information, which may also delay the task processing time. Therefore, we first address the mobility issue by proposing three different algorithms that work on allocating the mobile IoT device to the appropriate edge device (i.e. fog gateway), considering the distance and bandwidth load factors. Then, we offer a novel task scheduling algorithm that uses fuzzy logic to optimize the distribution of tasks between the fog and cloud layers, considering the task security requirements. The algorithm selects the proper processing unit to execute the task in the fog layer by exploiting the task demands (i.e. computation, storage, bandwidth, security) and deadline. Results demonstrate that considering the factors of distance and available bandwidth load while allocating the IoT device to the fog gateway improves the task processing time better than adopting one aspect. Results also show that our proposed scheduling algorithm outperforms other existing algorithms regarding makespan, turnaround time, success ratio and processing time metrics.

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.

Energy-Constrained Online Scheduling for Satellite-Terrestrial Integrated Networks

In satellite-terrestrial integrated networks, it is a common practice to schedule real-time tasks from low Earth orbit (LEO) satellites to ground stations (GSs) for data processing. However, the joint task scheduling and resource allocation under unknown environment dynamics (e.g., transmission latency) remains to be a challenging problem. First, the tradeoff between task latencies and energy consumption should be carefully considered when making decisions to minimize task latencies under time-averaged energy consumption constraints. Second, to learn the environment uncertainties and minimize the system performance loss (i.e., regret) in terms of task latencies, both online feedback and offline history should be leveraged efficiently, and the accompanying exploration-exploitation tradeoff should be dealt with in a proper way. In this article, we formulate the joint task scheduling and resource allocation problem as a constrained combinatorial multi-armed bandit (CMAB) problem. To solve the problem, by integrating online learning, online control, and offline historical information, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Task scheduling and Resource allocation scheme with Data-driven Bandit Learning</i> called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TRDBL</i> . Our theoretical and numerical results show that TRDBL achieves a sublinear time-averaged regret while satisfying the time-averaged energy consumption constraints.

Supporting single and multi-core resource access protocols on object-oriented RTOSes

Real-time resource access protocols are fundamental to bound the maximum delay a task can suffer due to priority inversions. Several real-time protocols have been proposed, for both static and dynamic scheduling approaches in single and multi-core processors. One of the main factors for performance efficiency in such protocols is the way they are implement within a real-time operating system (RTOS). In this paper we present an object-oriented design of real-time access protocols considering single and multi-core systems and also suspension- and spin-based protocols (7 protocols in total). Our design aims at reducing the run-time overhead and increasing code re-usability. By implementing the proposed design in an RTOS and running the protocols in a modern multi-core processor, we provide an analysis regarding the memory footprint, run-time overhead, and the impact of the overhead into the schedulability analysis of synthetically generated task sets. Our results indicate that proper implementation provides low run-time overhead (up to 6.1 $$\upmu \hbox {s}$$ ) and impact on the schedulability of real-time tasks.

DVFS-based energy-aware scheduling of imprecise mixed-criticality real-time tasks

Energy-aware mixed-criticality (MC) real-time scheduling has attracted the attention of many researchers. However, they only focus on the conventional MC task model, in which all low-criticality (LO) tasks are discarded in the high-criticality (HI) mode. In this work, we focus on an imprecise mixed-criticality (IMC) task model that provides degraded service for LO tasks in HI mode. Firstly, we address the energy minimization problem of scheduling IMC task sets with dynamic voltage and frequency scaling (DVFS). Secondly, we present an energy-aware IMC scheduling algorithm (EA-IMC) to solve this problem while it schedules tasks with the energy-efficient speed of the LO mode SLO, and the maximum processor speed Smax in HI mode. Finally, the experiments are applied to evaluate the EA-IMC algorithm and the experimental results show that it can achieve on average 24.55% energy reduction compared with the existing algorithms.