Thermal Analysis of Fair Scheduling in Real-time Embedded Systems

Thermal issues in microprocessors have become a major design constraint because of their adverse effects on the reliability, performance and cost of the system. This article proposes an improvement in earliest deadline first, a uni-processor scheduling algorithm, without compromising its optimality in order to reduce the thermal peaks and variations. This is done by introducing a factor of fairness to earliest deadline first algorithm, which introduces idle intervals during execution and allows uniform distribution of workload over the time. The technique notably lowers the number of context switches when compare with the previous thermal-aware scheduling algorithm based on the same amount of fairness. Although, the algorithm is proposed for uni-processor environment, it is also applicable to partitioned scheduling in multi-processor environment, which primarily converts the multi-processor scheduling problem to a set of uni-processor scheduling problem and thereafter uses a uni-processor scheduling technique for scheduling. The simulation results show that the proposed approach reduces up to 5% of the temperature peaks and variations in a uni-processor environment while reduces up to 7% and 6% of the temperature spatial gradient and the average temperature in multi-processor environment, respectively.

Due to recent advances in multimedia technology, a real-time capability is needed to a certain extent even in information systems, in addition to control systems. In general, information systems are classified as soft real-time systems, which keep operating even if a deadline is missed. In soft real-time systems, it is more important to provide statistical guarantees (probabilistic analysis) than absolute guarantees (deadline guarantees). In current real-time systems, real-time scheduling such as EDF (Earliest Deadline First) is used to meet timing constraints. EDF scheduling simply assigns higher priorities in the order of earlier deadlines. It can be applied to aperiodic tasks without prior knowledge of their starting times. In this paper, it is assumed that each attribute of the task follows a probabilistic distribution. The system performance when a nonpreemptive EDF scheduling is applied is then mathematically analyzed. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 89(2): 13–23, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20177

Packet networks are currently enabling the integration of traffic with a wide range of characteristics that extend from video traffic with stringent QoS requirements to the best-effort traffic requiring no guarantees. QoS guarantees can be provided in conventional packet networks by the use of proper packet scheduling algorithms. As a computer revolution, many scheduling algorithms have been proposed to provide different schemes of QoS guarantees with Earliest Deadline First (EDF) as the most popular one. With EDF scheduling, all flows receive the same miss rate regardless of their traffic characteristics and deadlines. This makes the standard EDF algorithm unsuitable for situations in which the different flows have different miss rate requirements since in order to meet all miss rate requirements it is necessary to limit admissions so as to satisfy the flow with the most stringent miss rate requirements. In this paper, we propose a new priority assignment scheduling algorithm, Hierarchal Diff-EDF (Differentiate Earliest Deadline First), which can meet the real-time needs of these applications while continuing to provide best effort service to non-real time traffic. The Hierarchal Diff-EDF features a feedback control mechanism that detects overload conditions and modifies packet priority assignments accordingly. To examine our proposed scheduler model, we introduced our attempt to provide an exact analytical solution. The attempt showed that the solution was apparently very complicated due to the high interdependence between the system queues' service. Hence, the use of simulation techniques seems inevitable. The simulation results showed that the Hierarchical Diff-EDF achieved the minimum packet average delay when compared with both EDF and Diff-EDF schedulers.

Shared caches in multicore processors introduce serious difficulties in providing guarantees on the real-time properties of embedded software due to the interaction and the resulting contention in the shared caches. To address this problem, we develop a new schedulability analysis for real-time multicore systems with shared caches, globally scheduled by Earliest Deadline First (EDF) and Fixed Priority (FP) algorithms. We construct an integer programming formulation, which can be transformed to an integer linear programming formulation, to calculate an upper bound on cache interference exhibited by a task within a given execution window. Using the integer programming formulation, an iterative algorithm is presented to obtain the upper bound on cache interference a task may exhibit during one job execution. The upper bound on cache interference is subsequently integrated into the schedulability analysis to derive a new schedulability condition. A range of experiments is performed to investigate how the schedulability is degraded by shared cache interference. We also evaluate the schedulability performance of EDF against FP scheduling over randomly generated tasksets. Our empirical evaluations show that EDF is better than FP scheduling in terms of the number of task sets deemed schedulable.

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.

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.

Automotive applications are typical cyber-physical systems, which perform real-time continuous data processing using a variety of onboard sensors and communications from outside the vehicle. However, outside-the-vehicle data transmissions often introduce significant data rate fluctuations, where arrival times can vary or may not be guaranteed. In this study, we investigate real-time data stream processing for automotive applications based on earliest deadline first (EDF) scheduling. When sensor data with an early deadline arrive late to a data stream management system (DSMS), the EDF scheduler enqueues the late data as if they had arrived earlier. As a result, data streams are preemptable, and the stream queues do not satisfy FIFO because they are out-of-order. However, existing real-time scheduling of data streams cannot handle out-of-order queues, and searching of the out-of-order queues based on EDF degrades the performance owing to frequent accessing of the queues. In this study, we present efficient EDF scheduling for the out-of-order stream queues (i.e., Preemptable data streams) in the DSMS. The main contributions of this study are: (1) a seamless definition of EDF scheduling for preemptable data streams (EDF-PStream), which is based on the definition of general data stream processing, (2) a proposal of a reasonable task design for EDF-PStream by merging operators, and (3) a runtime evaluation of EDF-PStream using automotive applications, this includes a comparison with data stream scheduling methods.

Fault-tolerant scheduling of real-time (RT) tasks in multiprocessor environment is essentially a NP-hard problem. This basically involves allocating a set of tasks to a set of processors so as to minimize the makespan and ensure tasks to meet their timing constraints. Many traditional heuristic approaches, such as earliest deadline first (EDF) and least laxity first (LLF) have been adopted to find optimal solution to this scheduling problem. However, conventional approach to achieve fault-tolerance (FT) in scheduling RT tasks based on traditional heuristic approach suffers from poor performance and results in inefficient processor utilization. Nature-inspired heuristic algorithms are gaining increased acceptance among researcher for solving real world NP-hard combinatorial optimization problems. This paper presents a comparative study of the novel primary-backup (PB) based fault-tolerant scheduling (PBFTS) technique for RT tasks in multiprocessor environment using two popular nature-inspired heuristic algorithms: the Ant Colony Optimization (ACO) and the Genetic Algorithm (GA). Exhaustive simulation reveals that the PBFTS algorithm based on GA and ACO both outperform the traditional PBFTS schemes in terms of performance, system utilization and efficiency. However, the comparative study also shows that the ACO based scheme surpasses the GA based scheme in terms of speed of execution whereas GA based scheme displays superior convergence with respect to ACO counterpart.

Statistical admission control for real-time services under earliest deadline first scheduling

Real-time scheduling is the theoretical basis of real-time systems engineering. Earliest deadline first (EDF) is an optimal scheduling algorithm for uniprocessor real-time systems. Existing results on an exact schedulability test for EDF task systems with arbitrary relative deadlines need to calculate the processor demand of the task set at every absolute deadline to check if there is an overflow in a specified time interval. The resulting large number of calculations severely restricts the use of EDF in practice. In this paper, we propose new results on necessary and sufficient schedulability analysis for EDF scheduling; the new results reduce, exponentially, the calculation times, in all situations, for schedulable task sets, and in most situations, for unschedulable task sets. For example, a 16-task system that in the previous analysis had to check 858,331 points (deadlines) can, with the new analysis, be checked at just 12 points. There are no restrictions on the new results: each task can be periodic or sporadic, with relative deadline, which can be less than, equal to, or greater than its period, and task parameters can range over many orders of magnitude.

Several objectives are aimed when scheduling traffic for transmission on heterogeneous networks. Traffic flows might belong and required to satisfy different set of requirements for a diverse set of applications, end users and networks. Schedulers have several goals to achieve in order to meet the needs of all stakeholders involved. Some schedulers are built to ensure fairness in bandwidth allocation, others try to prioritize certain flows and increase their share in the network resources, typically available bandwidth, and last but not least some are built to meet the stringent and time critical delay constraint of certain flows. Weighted fair queuing (WFQ) is a very well known scheduling algorithm that fairly allocates bandwidth between a set of flows contending for available output bandwidth while taking their weights into its scheduling calculation. Earliest deadline first (EDF), on the other hand, is a scheduling mechanism that favors flow packets that are about to expire and gives them preferential treatment and priority to be transmitted first. Neither WFQ nor EDF achieves both fairness and deadline goals simultaneously. For this reason, we introduced our fair and delay adaptive scheduler (FDAS) to bridge the gap and balance between fairness and delay requirements of incoming traffic flows. In this paper, we present a detailed simulation comparative study to manifest the difference between our proposed scheduler and the two popular schedulers and how it outperforms both to relatively achieve both bandwidth allocation fairness and traffic delay bound objectives simultaneously.

Since the first results published in 1973 by Liu and Layland on the Rate Monotonic (RM) and Earliest Deadline First (EDF) algorithms, a lot of progress has been made in the schedulability analysis of periodic task sets. Unfortunately, many misconceptions still exist about the properties of these two scheduling methods, which usually tend to favor RM more than EDF. Typical wrong statements often heard in technical conferences and even in research papers claim that RM is easier to analyze than EDF, it introduces less runtime overhead, it is more predictable in overload conditions, and causes less jitter in task execution.Since the above statements are either wrong, or not precise, it is time to clarify these issues in a systematic fashion, because the use of EDF allows a better exploitation of the available resources and significantly improves system's performance.This paper compares RM against EDF under several aspects, using existing theoretical results, specific simulation experiments, or simple counterexamples to show that many common beliefs are either false or only restricted to specific situations.

Since the first results published in 1973 by Liu and Layland on the Rate Monotonic (RM) and Earliest Deadline First (EDF) algorithms, a lot of progress has been made in the schedulability analysis of periodic task sets. Unfortunaltey, many misconceptions still exist about the properties of these two scheduling methods, which usually tend to favor RM more than EDF. Typical wrong statements often heard in technical conferences and even in research papers claim that RM is easier to analyze than EDF, it introduces less runtime overhead, it is more predictable in transient overload conditions, and causes less jitter in task execution. Since the above statements are either wrong, or not precise, it is time to clarify these issues in a systematic fashion, because the use of EDF allows a better exploitation of the available resources and significantly improves system’s performance. This paper compares RM against EDF under several aspects, using existing theoretical results or simple counterexamples to show that many common beliefs are either false or only restricted to specific situations.

As Hadoop is progressively becoming widespread technology in large-scale data analysis, there is a growing need for providing inevitable services to users who have strict requirements on job completion times, which are typically capture in service level agreements (i.e., SLA comprising an initial start time, a required execution time, priority and an end-to-end deadline). While earliest deadline first scheduling (EDF) like algorithms are trendy in guarantee job deadlines in real-time systems, they are not successful in a dynamic Hadoop environment. The problem of resource allocation and scheduling is modeled using constraint programming. We develop a distinctive Map Reduce constraint programming based matchmaking and scheduling algorithm (CP-RM) that can handle MapReduce jobs with deadlines and achieve high system performance. The MRCP-RM algorithm is integrated into Hadoop, which is a widely used open source implementation of the MapReduce programming model, as a new scheduler called the CP-RM Scheduler. We analyze the CP-RM Scheduler's performance as a comparison with an earliest deadline first (EDF) Hadoop scheduler, which is implemented by extending Hadoop's default FIFO scheduler. We analyze the CP-Scheduler's performance as a comparison with an earliest deadline first (EDF) Hadoop scheduler, which is implemented by extending Hadoop's default FIFO scheduler. The results of the performance evaluation demonstrate the effectiveness of CP-RM in generating a schedule that leads to a low proportion of jobs missing their deadlines and also provide insights into system behavior and performance. In the experiments performed on a Hadoop cluster deployed on local system, it is observed that CP-RM achieved on average a 56% lower p compared to an EDF-Scheduler for a different of workload.

Schedulability analysis is a very important part in real-time system research. Because the scenarios faced by real-time systems are very complicated, the functional characteristics must be combined with the predictability of response time. It is necessary to ensure the correctness of the calculation results and meet the real-time requirements. To solve this problem, we propose the IEDF (Improved Earliest Deadline First) algorithm, which is combined with the queuing theory model. The IEDF algorithm is based on the EDF (Earliest Deadline First) algorithm, which is more suitable for the scheduling of real-time embedded system. Scheduling of non-periodic tasks that arrive randomly. There are two types of tasks in the task set, tasks with a high static priority are executed first. In the ready queue of the same priority task, the deadline and execution time are considered. The comparison of simulation experiments shows that: the sum of waiting time in the execution of IEDF with enough deadline is much less than that of ordinary queuing algorithm; the number of errors in the execution of IEDF algorithm with deadline is much less than that of ordinary queuing algorithm. These results demonstrate the feasibility of the IEDF algorithm.