Fixed-priority Scheduling Algorithms Research Articles

Application of effective water-energy management based on digital twins technology in sustainable cities construction

This work aims to effectively resolve the problem of waterlogging in cities and manage water resources in sustainable cities. Digital Twins (DTs) technology was applied to the Urban Drainage System (UDS) and solves the modeling and scheduling problems of emergency drainage through the core model construction method of DTs. Firstly, the components of the UDS that are necessary in the process of building the model were listed according to the entity elements of the five-dimensional (5D) DTs model. Then, this work analyzed the essential data of the DTs of UDS and the Data Collection method according to the data elements of the 5D DTs model. Finally, the Multi-Level Dynamic Priority and Importance Scheduling (MDPIS) algorithm was proposed based on the Fixed Priority Scheduling (FPS) algorithm, which was verified by the simulation experiment. The experimental results indicated that the MDPIS algorithm showed significant performance in the rainfall scene with large fluctuations compared with the FPS algorithm. Specifically, the average improvement ratio was the highest, reaching 49.81%; the overall improvement rate was constant at about 48%. The operation results showed an apparent correlation between the catchment parameters and the overflow loss of the pumping station. The improved MDPIS algorithm can effectively utilize the water storage capacity and drainage capacity of the pumping station and reduce overflow losses during rainfall by dynamically adjusting the priority to solve the problem of urban inland inundation. The DTs-based UDS proposed here can effectively mitigate the overflow loss and improve the working efficiency of the pumping station cluster, promoting the development of Substainable Cities.

Determining rate monotonic schedulability of real-time periodic tasks using continued fractions

Fixed-priority scheduling algorithms are widely used for real-time systems with a single processor because of their implementation simplicity and relatively low run-time overheads. However, determining whether a task set is schedulable by a fixed-priority scheduling algorithm has been shown to be NP-hard. There are few exact schedulability test methods such as the time demand analysis or the worst-case response time analysis for Rate Monotonic scheduling, while much research has been done to find a tighter sufficient condition for the fixed-priority schedulability. In this paper, a new approach to analyze the schedulability of fixed-priority scheduling using well-known mathematical tool called continued fractions is presented. With continued fractions, a new schedulability test for Rate Monotonic algorithm is derived. The schedulability test condition is an exact condition that can be applied tasks with total utilization less than 1.

Energy-aware fixed-priority scheduling for periodic tasks with shared resources and IO devices

Many researches have focused on energy management for the processor. However, DPM via I/O device scheduling for real-time periodic tasks with shared resources has drawn little attention. We address the problem of the I/O device energy consumption minimisation for shared resources. Furthermore, we present an energy-aware IO devices fixed-priority scheduling algorithm based on the RM scheduling. It contains two parts: job scheduling and device scheduling. Job scheduling ensures that each task can complete its execution within its deadline. Device scheduling decides to turn off devices to save energy. The experimental results show that it can achieve significant energy savings.

An HSR Data Model for Cyborg Insect Research Experiments

Research on cyborg intelligent insects requires the experiment platform to collect data from the distributed sensing models and process the multi-modal signals in real-time. To overcome these issues, we present a novel data model, naming as the Hybrid-synchronizedrealtime (HSR) data model, which can synchronize the hybrid raw data channels by a meta data integration method and process them with a fixed priority scheduling algorithm. Real animal experiments show that the insect-machine experiment platform based on the HSR data model can fully satisfy the requirements of cyborg intelligent insect research, especially in dealing with the technical challenges of data synchronization, real-time processing and hybrid data integration. It provides an efficient approach to implement the experiment platform and thus aid the frontier research on cyborg insects.

An exact schedulability test for fixed-priority preemptive mixed-criticality real-time systems

The current literature of fixed-priority scheduling algorithms relies on sufficient tests to determine if a set of mixed-criticality sporadic tasks is schedulable on a single processor. The drawback of these safe tests is their pessimism, a matter that could be solved if an exact schedulability analysis is used. However, because of the non-deterministic behavior of tasks in the mentioned setups, exact quantification of worst-case response times, needed for the test, is a difficult problem; more precisely, such a quantification needs evaluation of enormous sequences of job executions. The core problem is thus to merge such sequences to make the analysis practical. This paper, for the first time, gives an algorithm for exact worst-case response time characterization of mixed-criticality sporadic real-time tasks executing according to a given fixed-priority scheduler. We use a set of techniques which carefully consider the task properties and their relation to the worst scenarios to prune the analysis state space. We also show an interesting result that if an exact schedulability test is used, the Audsley's optimal priority assignment algorithm is not applicable to the mixed-criticality case. Accordingly, we need new priority assignment algorithms to work with the exact test; we give a simple task priority assignment algorithm to this aim. The performance of the proposed exact test (in terms of time complexity) is examined and the effectiveness of some heuristic priority assignment algorithms using the test (in terms of the ratio of task sets which are deemed schedulable) are compared.

Real-Time Task Scheduling for Distributed Embedded System using MATLAB Toolboxes

This paper is gives a clear idea about MATLAB based toolboxes for scheduling various real-time problems and to examine the performance of the system prior to its implementation. Two different MATLAB based toolboxes-TORSCHE (Time Optimization of Resources, SCHEduling) and True Time used offers a collection of data structures that allows the user to analyze various issues in resource sharing and scheduling. Further in real-time environment, these simulators are used to obtain an optimum performance by providing a platform to extend the design methodologies and to meet the requirements as demanded by the application with limited resources. Algorithms are implemented either as MATLAB function with fixed structure or as MATLAB function with middle level language constructs. Two modules are designed and simulated for each toolbox. Using True Time Toolbox, fixed priority scheduling algorithm module and CAN networking of four different kernels are simulated and results are analyzed. Using TORSCHE toolbox timing parameter for each task is analyzed for two different scenarios. Finally, a simulation model is built by using True Time toolbox and TORSCHE in MATLAB platform. The toolboxes are intended mainly for research purpose to handle and control scheduling issues in real-time systems. The result indicates that the simulators aids in improving scheduler accuracy and strengthen the real-time character of the system. Using this approach it is possible to evaluate and modify controller designs and hardware platforms to better understand the performance and timing effects.

Overload provisioning in mixed-criticality cyber-physical systems

Cyber-physical systems are an emerging class of applications that require tightly coupled interaction between the computational and physical worlds. These systems are typically realized using sensor/actuator interfaces connected with processing backbones. Safety is a primary concern in cyber-physical systems since the actuators directly influence the physical world. However, unexpected or unusual conditions in the physical world can manifest themselves as increased workload demands being offered to the computational infrastructure of a cyber-physical system. Guaranteeing system safety under overload conditions is therefore a prime concern in developing and deploying cyber-physical systems. In this work, we study this problem in the context of a radar surveillance system, where tasks have different levels of criticality or influence on system safety . In the face of overloads, we observe that the desirable property in such systems is that the more critical tasks continue to meet their timing requirements. We capture this mixed-criticality overload requirement using a formal overload-tolerance metric called ductility . Using this overload-tolerance metric, we first develop our solution in the context of uniprocessor systems, where we show that Zero-Slack scheduling (ZS) algorithms can be used to improve the overload behavior in mixed-criticality cyber-physical systems compared to existing fixed-priority scheduling algorithms like Rate-Monotonic Scheduling (RMS) and Criticality-As-Priority-Assignment (CAPA). Leveraging these results, we then develop a criticality-aware task allocation algorithm called Compress-on-Overload Packing (COP) for dealing with multiprocessor cyber-physical systems. Evaluation results show that COP achieves up to five times better ductility than traditional load balancing bin-packing algorithms like Worst-Fit Decreasing (WFD). Finally, we apply ZS and COP to the radar surveillance system to demonstrate the resulting improvement in system overload behavior. Our implementation of the Zero-Slack scheduler is available as a part of the Linux/RK project, which provides resource kernel extensions for Linux.

Simulation Research on EDF Scheduling Algorithm of CAN Network Based on OPNET

In order to analyze and evaluate real-time performance of CAN network, the OPNET models of CAN network with Fixed Priority Scheduling (FPS) and Earliest Deadline First (EDF) have been set up in this paper. Based on simulation experiments, the performances of average delay and transmission success ratio in the two models are analyzed and the experiments also indicate that under normal load condition the two scheduling algorithm both can send data frame successfully before the deadline. In a slight overload condition FPS algorithm makes the transmission delay of low priority data frame significantly increased, and even occurs the phenomenon that low priority data frame can not be sent at all times; while the EDF algorithm can make all the nodes to complete the data frames in less than 3ms transmission delay, it meets the delay requirements that are delay should less than 10ms in most control systems, it indicates that EDF algorithm has the better scheduling and availability in soft real-time system.

Using Fixed Priority Pre-emptive Scheduling in Real-Time Systems

For real-time applications, task scheduling is a problem of paramount importance. Several scheduling algorithms were proposed in the literature, starting from static scheduling or cyclic executives which provide very deterministic yet inflexible behaviour, to the so called best-effort scheduling, which facilitates maximum run-time flexibility but allows only probabilistic predictions of run-time performance presenting a non-predictable and nondeterministic solution. Between these two extremes lies fixed priority scheduling algorithms, such as Rate Monotonic, that is not so efficient for real-time purposes but exhibits a predictable approach because scheduling is doing offline and guarantees regarding process deadlines could be obtained using appropriate analysis methods. This paper investigates the use of Rate Monotonic algorithm by making adjustments in order to make it more suitable for real-time applications. The factors that motivate the interest for fixed priority scheduling algorithms such Rate Monotonic when doing with real-time systems lies in its associated analysis that could be oriented in two directions: schedulability analysis and analysis of process interactions. The analyzing process is carried out using a previously implemented framework that allows modelling, simulation and schedulability analysis for a set of real-time system tasks, and some of the results obtained are presented.

A faster exact schedulability analysis for fixed-priority scheduling

Real-time scheduling for task sets has been studied, and the corresponding schedulability analysis has been developed. Due to the considerable overheads required to precisely analyze the schedulability of a task set (referred to as exact schedulability analysis), the trade-off between precision and efficiency is widely studied. Many efficient but imprecise (i.e., sufficient but not necessary) analyses are discussed in the literature. However, how to precisely and efficiently analyze the schedulability of task sets remains an important issue. The Audsley’s Algorithm was shown to be effective in exact schedulability analysis for task sets under rate-monotonic scheduling (one of the optimal fixed-priority scheduling algorithms). This paper focuses on reducing the runtime overhead of the Audsley’s Algorithm. By properly partitioning a task set into two subsets and differently treating these two subsets during each iteration, the number of iterations required for analyzing the schedulability of the task set can be significantly reduced. The capability of the proposed algorithm was evaluated and compared to related works, which revealed up to a 55.5% saving in the runtime overhead for the Audsley’s Algorithm when the system was under a heavy load.

Undergraduate teaching of real-time scheduling algorithms by developed software tool

Theoretical foundations of scheduling in the course Real-Time Systems of the undergraduate studies program are extended by the usage of the developed visual software tool. The tool enables teaching of simulated fixed-priority scheduling algorithms. The user simply sets the simulation and algorithm properties and loads the prepared input task sets from the database. Results are obtained within the program or from external applications. The paper deals with the problem of scheduling the mixture of periodic and aperiodic tasks in a uniprocessor system. Results are displayed in the form of a flow report, table report, and diagrams of dependencies of evaluation parameters on the change of periodic and aperiodic workload. The flow report explains the algorithm behavior and the table form and dependency diagrams enable a more precise scheduling analysis and mutual comparison, respectively. Some of the evaluation parameters are the following: aperiodic response time, number of tasks with missed deadlines, utilization of resource and server tasks, resource-breakdown utilization. The software tool improves teaching of the course and the final success of students regarding relevant exam exercises.

Engineering and analysis of fixed priority schedulers

Scheduling theory holds great promise as a means to a priori validate timing correctness of real-time applications. However, there currently exists a wide gap between scheduling theory and its implementation in operating system kernels running on specific hardware platforms. The implementation of any particular scheduling algorithm introduces overhead and blocking components which must be accounted for in the timing correctness validation process. This paper presents a methodology for incorporating the costs of scheduler implementation within the context of fixed priority scheduling algorithms. Both event-driven and timer-driven scheduling implementations are analyzed. We show that for the timer-driven scheduling implementations the selection of the timer interrupt rate can dramatically affect the schedulability of a task set, and we present a method for determining the optimal timer rate. We analyzed both randomly generated and two well-defined task sets and found that their schedulability can be significantly degraded by the implementation costs. Task sets that have ideal breakdown utilization over 90% may not even be schedulable when the implementation costs are considered. This work provides a first step toward bridging the gap between real-time scheduling theory and implementation realities. This gap must be bridged for any meaningful validation of timing correctness properties of real-time applications.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A new algorithm for scheduling periodic, real-time tasks

We consider the problem of preemptively scheduling a set of periodic, real-time tasks on a multiprocessor computer system. We give a new scheduling algorithm, the so-called Slack-Time Algorithm, and show that it is more effective than the known Deadline Algorithm. We also give an (exponential-time) algorithm to decide if a task system is schedulable by the Slack-Time or the Deadline Algorithm. The same algorithm can also be used to decide if a task system is schedulable by any given fixed-priority scheduling algorithm. This resolves an open question posed by Leung and Whitehead. Finally, it is shown that the problem of deciding if a task system is schedulable by the Slack-Time, the Deadline, or any given fixed-priority scheduling algorithm is co-NP-hard for each fixedm≥.