Optimal Tasks and Messages Scheduling for Asynchronous Networked Control Systems

Internet of Things (IoT) based healthcare applications are time‐sensitive and any delay can cause alarming situations, including death of patients. The Earliest Deadline First (EDF) scheduling scheme has been proposed for use in IoT‐based healthcare applications. However, the EDF scheme performs poorly under overloaded conditions due to giving highest priority to packets that are close to missing their deadlines. Some studies have proposed the use of Priority EDF to overcome the challenges of EDF; however, Priority EDF still favours higher priority queues which increases the waiting times of lower priority queues. In order to overcome the limitation of EDF and its variants, this paper proposes a system model for a prioritized scheduling (PS) scheme. The PS scheme is an improvement of the Earliest Deadline First (EDF) scheme and its variants for IoT‐based healthcare applications. The PS scheme uses a heterogeneous multi‐server priority queuing system to provide service differentiation by prioritizing short packets over large packets and delay sensitive packets are serviced before delay tolerant packets. Numerical results demonstrate that the PS scheme minimizes the mean slowdown for both delay sensitive short and large packets at low and high load values. Additionally, the PS scheme performs better than the EDF and Priority EDF schemes in terms of reducing mean slowdown of packets and the PS scheme performs better than the EDF in terms of throughput for all packet sizes at both low and high load values. The performance improvement in terms of throughput is more pronounced at high load values. This addresses the challenge of the EDF scheme which performs poorly under overloaded conditions and the challenge of the Priority EDF scheme which favours higher priority queues at the expense of low priority queues.

EDF (Earliest Deadline First) has been proved to be optimal scheduling algorithm for single processor real-time system. It also performs well for multiprocessor system. Limitation of EDF is that its performance decreases exponentially when system becomes slightly overloaded. ACO (Ant Colony Optimization) based scheduling algorithm performs well in both underloaded and overloaded conditions. But its limitation is that it takes more time for execution compared to EDF. In this paper, an adaptive algorithm for multiprocessor real-time system is proposed, which is combination of both of these algorithms. The proposed algorithm along with EDF and ACO based algorithm is simulated for real-time multiprocessor system and the results are obtained. The performance is measured in terms of Success Ratio (SR) and Effective CPU Utilization (ECU). Execution Time taken by each scheduling algorithm is also measured. From analysis and experiments, it reveals that the proposed algorithm is fast as well as efficient in both underloaded and overloaded conditions for real-time multiprocessor systems.

The conventional model of real-time system cannot solve a problem when tasks come earlier than their earliest response time. Fazmah Arif Yulianto solved this problem by purposing a different model which has liveline as new time constraint. This new algorithm is called Earliest Deadline Earliest Liveline First-Fazmah (EDELF-F) which is developed based on EDF-DM algorithm. Then to determine the ability of the algorithm, the comparison between EDELF-F with conventional algorithms EDF (Earliest Deadline First) was performed with 3 scenarios: period > deadline, period = deadline and period < deadline for 10 tasks. After simulated, EDELF-F algorithm has a weakness in order to satisfy task's period. Author has modified EDELF algorithm (called EDELF-S) which is developed based on EDF-C algorithm to overcome weakness of EDELF-F algorithm. This EDELF-S has better response time and feasibility and also can solve the problem related to earliest response time. Both EDELF-F and EDELF-S can solve every case which can be solved by EDF but not vice versa. Both EDELF-F and EDELF-S also has a better feasibility than EDF.

In this paper we show how to extend classical real time feasibility conditions to take into account kernel overheads for mixed preemptive and non- preemptive periodic tasks with Earliest Deadline First (EDF) scheduling policy. The EDF algorithm allows us to schedule some tasks sets which are not valid with FP/FIFO scheduling policy, the classical scheduling implemented for OSEK. That is why we have improved our OSEK by implementing EDF algorithm. First of all, we identify the sources of overheads that influence the response time of the tasks: the context switching, the mechanisms used to activates/terminates and reschedules tasks, and the granularity of the periodic timer used to implement the periodic task model. We then show how to take into account those overheads in the classical EDF feasibility conditions. We compare the response times computed with theoretical feasibility conditions with kernel overhead to the results obtained on a real implementation. We show that the kernel overheads cannot be neglected and that our theoretical results are valid and can be used for a real-time dimensioning of an OSEK system with EDF scheduling policy. Finally, we compare the performances of our EDF implementation to the FP/FIFO native scheduling showing the benefits of implementing EDF in OSEK.

To minimize battery consumption for portable devices, the prescheduling policy of battery-aware scheduling was improved by optimizing slack distribution. A battery-aware compound task scheduling (BACTS) algorithm considering various aspects including task deadline, current and execution time was proposed and evaluated with the previously prevailing earliest deadline first (EDF) algorithm. The results indicate the proposed BACTS algorithm manages to figure out a feasible schedule (if available) in battery-aware task scheduling even for disorganized connected task graphs beyond the solving ability of EDF. Its schedule achieves better performance with lower charge consumption after prescheduling, and also lower or equal optimum charge consumption after voltage scaling.

Deriving deadlines and periods for update transactions so as to maintain timeliness and data freshness while minimizing imposed workload has long been recognized an important problem in real-time database research. Despite years of active research, the state-of-the-art still has much room for improvement, particularly for periodic transactions scheduled by the Earliest Deadline First (EDF) algorithm. In this paper, we propose a practical and efficient two-phase algorithm, GEneral EDF (GE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EDF</sub> ), for assigning periods and deadlines to EDF-scheduled update transactions. Phase 1 of GE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EDF</sub> aims at finding solutions for most inputs in linear time, based on the observation that the execution times of update transactions are relatively small compared to the validity interval lengths of real-time data objects in many real-time applications. In the remaining cases for which Phase 1 fails to derive solutions, Phase 2 is invoked by employing an existing deadline-monotonic-based algorithm, which we show is also applicable to our problem. Meanwhile, we have devised several techniques which significantly reduce the cost of schedulability test, and hence greatly improve time efficiency. Our experimental results demonstrate that GE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EDF</sub> outperforms existing approaches in terms of generated workloads. Although Phase 2 has a pseudopolynomial time complexity, our experimental study shows that it runs much faster than other solutions with comparable quality.

We propose a new scheme for multiclass packet scheduling which is a variant of EDF (earliest deadline first). The model corrects EDF domino effect in a nearly-overloaded system. Simulations results show that the proposed model performs better than classical EDF by enhancing QoS for soft-real time traffic without significant degradation of less constrained traffic.

Cache partitioning is a technique to reduce interference among tasks running on the processors with shared caches. To make this technique effective, cache segments should be allocated to tasks that will benefit the most from having their data and instructions stored in the cache. The requests for cached data and instructions can be retrieved faster from the cache memory instead of fetching them from the main memory, thereby reducing overall execution time. The existing partitioning schemes for real-time systems divide the available cache among the tasks to guarantee their schedulability as the sole and primary optimization criterion. However, it is also preferable, particularly in systems with power constraints or mixed criticalities where low- and high-criticality workloads are executing alongside, to reduce the total cache usage for real-time tasks. Cache minimization as part of design space exploration can also help in achieving optimal system performance and resource utilization in embedded systems. In this paper, we develop optimization algorithms for cache partitioning that, besides ensuring schedulability, also minimize cache usage. We consider both preemptive and non-preemptive scheduling policies on single-processor systems with fixed- and dynamic-priority scheduling algorithms (Rate Monotonic (RM) and Earliest Deadline First (EDF), respectively). For preemptive scheduling, we formulate the problem as an integer quadratically constrained program and propose an efficient heuristic achieving near-optimal solutions. For non-preemptive scheduling, we combine linear and binary search techniques with different fixed-priority schedulability tests and Quick Processor-demand Analysis (QPA) for EDF. Our experiments based on synthetic task sets with parameters from real-world embedded applications show that the proposed heuristic: (i) achieves an average optimality gap of 0.79% within 0.1× run time of a mathematical programming solver and (ii) reduces average cache usage by 39.15% compared to existing cache partitioning approaches. Besides, we find that for large task sets with high utilization, non-preemptive scheduling can use less cache than preemptive to guarantee schedulability.

As computing devices and the Internet technology advances, real-time on-line services are emerging. Different from traditional real-time applications for which the scheduling objective is to meet task deadlines, the optimization goal for on-line service systems is to maximize profit obtained through providing timely services. For this class of applications, there are two distinctive characteristics: (1) tasks, i.e., client requests, are associated with a pair of unimodal time functions, representing system accrued profit when a task is completed before its deadline, or accrued penalty if otherwise; and (2) requests execution times vary in a wide range. The paper presents a new scheduling algorithm, i.e., the Profit and Penalty aware (PP-aware) scheduling algorithm, with an objective to maximize system's total accrued profit. Our simulation results have empirically shown the advantages, in respect of system total accrued profit, of the proposed algorithm over other commonly used scheduling algorithms, such as Earliest Deadline First (EDF) and Utility Accrual (UA) algorithms.

Different from a general-purpose system, a real-time system requires stringent timing guarantees. While existing offline analysis techniques can provide timing guarantees using the worst-case execution time (WCET) of individual tasks, a variation of actual execution time makes it difficult to build covert timing channel. In this paper, we first present a novel covert timing channel, which considers actual execution time distribution of tasks and controls execution time to leak data between conspirators; we demonstrate that it is possible to leak data in real-time systems. Second, we suggest two enhancing techniques called S-R LCM (sender-receiver least common multiple) and noise area to reduce noise in communication. Through simulations, we demonstrate that our covert timing channel can serve trade-off between transmission speed and accuracy; that is, it shows average 50.2%, 54.6% and 51.3% accuracy for 100 test cases with thresholds 0, 1.4 and 2.8. Average 58.4% accuracy is accomplished with best threshold values for 100 test cases, and the maximum accuracy for a single test case is recorded 100.0%.

This paper is concerned with the finite-horizon H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> quantized control problem for a class of discrete time-varying nonlinear networked control systems with earliest deadline first (EDF) schedule. EDF schedule is employed in the channels both from sensor to controller and controller to actuator. Moreover, the measurement output signal and control signal are quantized by a logarithmic quantizer before they enter the controller and system, respectively. The main purpose of the problem addressed is to design a dynamical output-feedback controller such that the prespecified H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance of the system is guaranteed over a given finite time-horizon. To this end, a sufficient condition is firstly established for the existence of the desired output-feedback controller. Then, the controller parameters are characterized by solving a recursive linear matrix inequality. Finally, a numerical example is given to illustrate the effectiveness of the proposed control approach.

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.

This paper develops a framework for statistically guaranteeing end-to-end delay bounds to envelope-constrained connections transporting real-time traffic in a network of switches employing the earliest deadline first (EDF) packet scheduling scheme in conjunction with per-hop traffic shaping. The performance of an isolated EDF scheduler fed by Markovian sources has been studied earlier in the literature; we extend the analysis to the multi-node setting in the presence of per-node traffic shaping. By making conservative approximations, we show that the analysis is tractable, and offers large performance gains over frameworks which offer deterministic services (the difference is as large as 25% for reasonably realistic traffic mixes). We also investigate the advantages of smoothing the traffic at the ingress to the network. Our framework, we believe, could be of great use in networks which statistically guarantee end-to-end delay bounds to real-time applications.

This paper presents a system level approach for the synthesis of hard real-time multitask application specific systems. The algorithm takes into account task precedence constraints among multiple hard real-time tasks and targets a multiprocessor system consisting of a set of heterogeneous off-the-shelf processors. The optimization goal is to select a minimal cost multi-subset of processors while satisfying all the required timing and precedence constraints. There are three design phases: resource allocation, assignment, and scheduling. Since the resource allocation is a search for a minimal cost multi-subset of processors, we adopted an A* search based technique for the first synthesis phase. A variation of the force-directed optimization technique is used to assign a task to an allocated processor. The final scheduling of a hard-real time task is done by the task level scheduler which is based on Earliest Deadline First (EDF) scheduling policy. Our task level scheduler incorporates force-directed scheduling methodology to address the situations where EDF is not optimal. The experimental results on a variety of examples show that the approach is highly effective and efficient.

The EDF (Earliest Deadline First) scheduling policy can take advantage of the existence of a synchronization clock mechanism to reach higher utilizations in distributed real-time systems, as indicated by theoretical research. In this work, we give the first steps to enable the usage of EDF and a global clock in a distributed system. To this end, our proposal relies on Linux to provide the global clock and on a Real-Time Operating System (RTOS) to provide the global EDF policy. The objective is the evaluation of global EDF to verify to what extent the theoretical results are met.