Utilization Bounds for EDF Scheduling on Real-Time Multiprocessor Systems

The utilization bound for earliest deadline first (EDF) scheduling is extended from uniprocessors to homogeneous multiprocessor systems with partitioning strategies. First results are provided for a basic task model, which includes periodic and independent tasks with deadlines equal to periods. Since the multiprocessor utilization bounds depend on the allocation algorithm, different allocation algorithms have been considered, ranging from simple heuristics to optimal allocation algorithms. As multiprocessor utilization bounds for EDF scheduling depend strongly on task sizes, all these bounds have been obtained as a function of a parameter which takes task sizes into account. Theoretically, the utilization bounds for multiprocessor EDF scheduling can be considered a partial solution to the bin-packing problem, which is known to be NP-complete. The basic task model is extended to include resource sharing, release jitter, deadlines less than periods, aperiodic tasks, non-preemptive sections, context switches, and mode changes.     

A Feasibility Decision Algorithm for Rate Monotonic and Deadline Monotonic Scheduling

Rate monotonic and deadline monotonic scheduling are commonly used for periodic real-time task systems. This paper discusses a feasibility decision for a given real-time task system when the system is scheduled by rate monotonic and deadline monotonic scheduling. The time complexity of existing feasibility decision algorithms depends on both the number of tasks and maximum periods or deadlines when the periods and deadlines are integers. This paper presents a new necessary and sufficient condition for a given task system to be feasible and proposes a new feasibility decision algorithm based on that condition. The time complexity of this algorithm depends solely on the number of tasks. This condition can also be applied as a sufficient condition for a task system using priority inheritance protocols to be feasible with rate monotonic and deadline monotonic scheduling.     

DP-Fair: a unifying theory for optimal hard real-time multiprocessor scheduling

We consider the problem of optimal real-time scheduling of periodic and sporadic tasks on identical multiprocessors. A number of recent papers have used the notions of fluid scheduling and deadline partitioning to guarantee optimality and improve performance. This article develops a unifying theory with the DP-Fair scheduling policy and examines how it overcomes problems faced by greedy scheduling algorithms. In addition, we present DP-Wrap, a simple DP-Fair scheduling algorithm which serves as a least common ancestor to other recent algorithms. The DP-Fair scheduling policy is extended to address the problem of scheduling sporadic task sets with arbitrary deadlines.     

Schedulability analysis of periodic and aperiodic tasks with resource constraints

In this paper, we address the problem of scheduling hybrid task sets consisting of hard periodic and soft aperiodic tasks that may share resources in exclusive mode in a dynamic environment, where tasks are scheduled based on their deadlines. Bounded blocking on exclusive resources is achieved by means of a dynamic resource access protocol which also prevents deadlocks and chained blocking. Aperiodic responsiveness is enhanced by an efficient servicing technique which assigns each aperiodic request a suitable deadline. Feasibility conditions are extended to handle tasks with deadlines different from periods and a reclaiming technique is presented to deal with early completions.     

Priority-Driven Scheduling of Periodic Task Systems on Multiprocessors

The scheduling of systems of periodic tasks upon multiprocessor platforms is considered. Utilization-based conditions are derived for determining whether a periodic task system meets all deadlines when scheduled using the earliest deadline first scheduling algorithm (EDF) upon a given multiprocessor platform. A new priority-driven algorithm is proposed for scheduling periodic task systems upon multiprocessor platforms: this algorithm is shown to successfully schedule some task systems for which EDF may fail to meet all deadlines.     

Bounding and shaping the demand of generalized mixed-criticality sporadic task systems

We generalize the commonly used mixed-criticality sporadic task model to let all task parameters (execution-time, deadline and period) change between criticality modes. In addition, new tasks may be added in higher criticality modes and the modes may be arranged using any directed acyclic graph, where the nodes represent the different criticality modes and the edges the possible mode switches. We formulate demand bound functions for mixed-criticality sporadic tasks and use these to determine EDF-schedulability. Tasks have different demand bound functions for each criticality mode. We show how to shift execution demand between different criticality modes by tuning the relative deadlines. This allows us to shape the demand characteristics of each task. We propose efficient algorithms for tuning all relative deadlines of a task set in order to shape the total demand to the available supply of the computing platform. Experiments indicate that this approach is successful in practice. This new approach has the added benefit of supporting hierarchical scheduling frameworks.     

Energy-efficient Tasks Scheduling Heuristics with Multi-constraints in Virtualized Clouds

Reducing energy consumption has become an important task in cloud datacenters. Many existing scheduling approaches in cloud datacenters try to consolidate virtual machines (VMs) to the minimum number of physical hosts and hence minimize the energy consumption. VM live migration technique is used to dynamically consolidate VMs to as few PMs as possible; however, it introduces high migration overhead. Furthermore, the cost factor is usually not taken into account by existing approaches, which will lead to high payment cost for cloud users. In this paper, we aim to achieve energy reduction for cloud providers and payment saving for cloud users, and at the same time, without introducing VM migration overhead and without compromising deadline guarantees for user tasks. Motivated by the fact that some of the tasks have relatively loose deadlines, we can further reduce energy consumption by proactively postponing the tasks without waking up new physical machines (PMs). A heuristic task scheduling algorithm called Energy and Deadline Aware with Non-Migration Scheduling (EDA-NMS) algorithm is proposed, which exploits the looseness of task deadlines and tries to postpone the execution of the tasks that have loose deadlines in order to avoid waking up new PMs. When determining the VM instant types, EDA-NMS selects the instant types that are just sufficient to guarantee task deadline to reduce user payment cost. The results of extensive experiments show that our algorithm performs better than other existing algorithms on achieving energy efficiency without introducing VM migration overhead and without compromising deadline guarantees.     

An efficient dynamic scheduling algorithm for multiprocessorreal-time systems

Many time-critical applications require predictable performance and tasks in these applications have deadlines to be met. In this paper, we propose an efficient algorithm for nonpreemptive scheduling of dynamically arriving real-time tasks (aperiodic tasks) in multiprocessor systems. A real-time task is characterized by its deadline, resource requirements, and worst case computation time on p processors, where p is the degree of parallelization of the task. We use this parallelism in tasks to meet their deadlines and, thus, obtain better schedulability compared to nonparallelizable task scheduling algorithms. To study the effectiveness of the proposed scheduling algorithm, we have conducted extensive simulation studies and compared its performance with the myopic scheduling algorithm. The simulation studies show that the schedulability of the proposed algorithm is always higher than that of the myopic algorithm for a wide variety of task parameters     

A real-time scheduling framework for embedded systems with environmental energy harvesting

Real-time scheduling refers to the problem in which there is a deadline associated with the execution of a task. In this paper, we address the scheduling problem for a uniprocessor platform that is powered by a renewable energy storage unit and uses a recharging system such as photovoltaic cells. First, we describe our model where two constraints need to be studied: energy and deadlines. Since executing tasks require a certain amount of energy, classical task scheduling like earliest deadline is no longer convenient. We present an on-line scheduling scheme, called earliest deadline with energy guarantee (EDeg), that jointly accounts for characteristics of the energy source, capacity of the energy storage as well as energy consumption of the tasks, and time. In order to demonstrate the benefits of our algorithm, we evaluate it by means of simulation. We show that EDeg outperforms energy non-clairvoyant algorithms in terms of both deadline miss rate and size of the energy storage unit.     

Reliability improvement of the dual-priority protocol under unreliable transmission

The dual-priority is a scheduling policy providing the guarantees needed by periodic or sporadic hard real-time tasks while decreasing the response time for aperiodic soft real-time tasks. This scheduling policy can be applied to message scheduling and its performance on controller area network (CAN) will be assessed. Nevertheless, when used in an electromagnetic stressed environment (e.g. automotive communication) leading to transmission errors, this scheduling strategy could lead to serious disappointments. It will be explained why the hard real-time traffic is highly sensitive to transmission errors. The risks of deadline failure will be quantified and a simple mechanism that provides probabilistic guarantees to prevent hard real-time frames from missing their deadlines, will be proposed. This mechanism is compared in terms of performance to the original dual-priority strategy. The chosen performance metrics are the deadline failure probability for hard real-time traffic, the average response time and the variance in response time for soft real-time traffic.     

实时异构系统的动态分批优化调度算法

提出了一种实时异构系统的动态分批优化调度算法,该算法采用的是在每次扩充当前局部调度时,按一定规则在待调度的任务集中选取一批任务,对该批任务中的每项任务在每个处理器上的运行综合各种因素构造目标函数,将问题转化为非平衡分配问题,一次性为这些任务都分配一个处理器或为每个处理器分配一项任务,使得这种分配具有最好的“合适性”,以增大未被调度任务的可行性.这种方法有效地提高了算法调度成功率.同时,为了评估该算法的性能,对其进行了大量的模拟,分析了一些任务参数的变化对算法调度成功率的影响,并与老算法的调度成功率进行了比较.模拟结果显示,新算法优于老算法.     

基于Linux的多核实时任务调度算法改进

嵌入式实时系统通常被实现为多任务系统,以满足多个外部输入的响应时间的最后期限约束。Linux内核中已经实现了基于EDF(Earliest Deadline First)调度算法的DL调度器,使得实时任务能在截止期限内运行完成。但对于多核处理器,由于实时任务在EDF算法下会出现Dhall效应,论文对 Linux内核中实时任务调度算法进行了改进。在EDF算法的基础上,实现LLF(Least Laxity First)调度算法并对其加以改进,通过降低任务上下文切换频率以及减少松弛度的计算来减小调度过程中的颠簸现象。实验证明该方法既避免了Dhall效应,又减少了任务上下文切换带来的系统开销,并使得任务能在截止期限内完成调度,取得了较好的调度性能。     

Aperiodic servers in a deadline scheduling environment

A real-time system may have tasks with soft deadlines, as well as hard deadlines. While earliest-deadline-first scheduling is effective for hard-deadline tasks, applying it to soft-deadline tasks may waste schedulable processor capacity or sacrifice average response time. Better average response time may be obtained, while still guaranteeing hard deadlines, with an aperiodic server. Three scheduling algorithms for aperiodic servers are described, and schedulability tests are derived for them. A simulation provides performance data for these three algorithms on random aperiodic tasks. The performances of the deadline aperiodic servers are compared with those of several alternatives, including background service, a deadline polling server, and rate-monotonic servers, and with estimates based on the M/M/1 queueing model. This adds to the evidence in support of deadline scheduling,versus fixed priority scheduling.     

Parameter adaptation for generalized multiframe tasks: schedulability analysis,case study,and applications to self-suspending tasks

The generalized multiframe task model (GMF) extends the sporadic task model and multiframe task model. Each frame in the GMF model contains an execution time, a relative deadline, and a minimum inter-arrival time. These parameters are fixed after task specification time in the GMF model. However, multimedia and adaptive control systems may be overloaded and no longer stabilized when the task parameters in such systems are not flexible. In order to address this problem, deadlines and periods of frames may change to alleviate temporal overload, e.g., in the parameter adaptation and elastic scheduling model. In this paper, we propose a new model GMF-PA (the GMF model with parameter adaptation). This model allows task parameters to be flexible in arbitrary-deadline systems. A necessary schedulability test based on mixed-integer linear programming is given to check the schedulability under EDF scheduling and optimally assign frame deadlines and periods at the same time. We also prove that the test is a sufficient and necessary schedulability test when frame deadlines and periods must be integers. An approximation algorithm is also deployed to reduce computational running time and indicates a sufficient schedulability test in general. The speed-up factor of our approximation algorithm is \(1+\epsilon \) where \(\epsilon \) can be arbitrarily small, with respect to the exact schedulability test of GMF-PA tasks under EDF. We also apply the GMF model to self-suspending tasks. By extending recent work on scheduling self-suspending tasks, we remove the assumption that frame deadlines are equally assigned in self-suspending tasks, and the system is extended from constrained-deadline systems to arbitrary-deadline systems. We have done extensive experiments to show that the schedulability ratio is improved using our techniques in our GMF-PA model.     

A load index and a transfer policy for global scheduling tasks with deadlines

Two important components of a global scheduling algorithm are its transfer policy and its location policy. While the transfer policy determines whether a task should be transferred, the location policy determines where it should be transferred. Many global scheduling algorithms have been proposed to schedule tasks with deadline constraints. These algorithms try to transfer tasks only when task's deadlines cannot be met locally or local load is high (i.e. they take only corrective measures). However, a scheduling algorithm that takes preventive measures in addition to corrective measures can reduce potential deadline misses substantially. In this paper we present: (a) a load index which characterizes the system state and is more conducive to preventive and corrective measures; (b) a new transfer policy which takes preventive measures by doing anticipatory task transfers in addition to corrective measures. The proposed transfer policy adapts better to the workload by availing of the accurate system state made available by the proposed load index. An algorithm making use of the new transfer policy and the new load index is shown to reduce the number of deadline misses significantly when compared to algorithms taking only corrective measures.     

Hartstone Uniprocessor Benchmark: Definitions and experiments for real-time systems

The purpose of this paper is to define a series of requirements and associated experiments called the Hartstone Uniprocessor Benchmark (HUB), to be used in testing the ability of a uniprocessor system to handle certain types of hard real-time applications. The benchmark model considers the real-time system as a set of periodic, aperiodic (sporadic), and synchronization (server) tasks. The tasks are characterized by their execution times (workloads), and deadlines. There are five series of experiments defined. They are, in order of increasing complexity, PH (Periodic Tasks, Harmonic Frequencies), PN (Periodic Tasks, Nonharmonic Frequencies), AH (Periodic Tasks with Aperiodic Processing), SH (Periodic Tasks with Synchronization), and SA (Periodic Tasks with Aperiodic Processing and Synchronization). The general stopping criteria of the experiments is defined as follows: Change one of the following four task set parameters: number of tasks, execution time(s), blocking time(s), or deadline(s) until a given task set is no longer schedulable, i.e., a deadline is missed. The derivation of the Hartstone experiments from one static scheduling algorithm (Rate Monotonic) and one dynamic scheduling algorithm (Earliest Deadline First) is presented. Because of its high-level application view of the underlying hardware and real-time system software the Hartstone experiments can be used for fast prototyping of real-time applications. Implementation of such benchmarks is useful in evaluating scheduling algorithms, scheduling protocols, and design paradigms, as well as evaluating real-time languages, the tasking system of compilers, real-time operating systems, and hardware configurations.     

一种新的异构实时分布式系统的容错调度算法

一般来说,异构分布式实时系统中任务的周期并不完全相同且任务的时限不等于它们的周期,同时系统中还有一些无容错需求的任务.因此现有的任务调度算法一般不能满足这些要求.针对这类系统,在结合基版本/副版本技术和EDF算法的基础上,给出了一种新的容错调度算法.该算法由两部分组成：任务分配调度算法和单处理器调度算法.对于单处理器调度算法,本文采用了EDF算法;在此基础上,给出一种启发式静态任务分配算法.分析了系统的可调度性,给出了任务可调度条件和基版本/副版本时限的设置方法.仿真结果表明,这种算法是有效的.     

Overload management in real-time control applications using (m,k)-firm guarantee

Tasks in a real-time control application are usually periodic and they have deadline constraints by which each instance of a task is expected to complete its computation, even in the adverse circumstances caused by component failures. Techniques to recover from processor failures often involve a reconfiguration in which all tasks are assigned to fault-free processors. This reconfiguration may result in processor overload where it is no longer possible to meet the deadlines of all tasks. In this paper, we discuss an overload management technique which discards selected task instances in such a way that the performance of the control loops in the system remain satisfactory even after a failure. The technique is based on the rationale that real-time control applications can tolerate occasional misses of the control law updates, especially if the control law is modified to account for these missed updates. The paper devises a scheduling policy which deterministically guarantees when and where the misses will occur. The paper also proposes a methodology for modifying the control law to minimize the deterioration in the control system behavior as a result of these missed control law updates     

Robust Adaptive Metrics for Deadline Assignment in Distributed Hard Real-Time Systems

Distributed real-time applications usually consist of several component tasks and must be completed by its end-to-end (E-T-E) deadline. As long as the E-T-E deadline of an application is met, the strategy used for dividing it up for component tasks does not affect the application itself. One would therefore like to slice each application E-T-E deadline and assign the slices to component tasks so as to maximize the schedulability of the component tasks, and hence the application. Distribution of the E-T-E deadline over component tasks is a difficult and important problem since there exists a circular dependency between deadline distribution and task assignment. We propose a new deadline-distribution scheme which has two major improvements over the best scheme known to date. It can distribute task deadlines prior to task assignment and relies on new adaptive metrics that yield significantly better performance in the presence of high resource contention. The deadline-distribution problem is formulated for distributed hard real-time systems with relaxed locality constraints, where schedulability analysis must be performed at pre-run-time, and only a subset of the tasks are constrained by pre-assignment to specific processors. Although it is applicable to any scheduling policy, the proposed deadline-distribution scheme is evaluated for a non-preemptive, time-driven scheduling policy. Using extensive simulations, we show that the proposed adaptive metrics deliver much better performance (in terms of success ratio and maximum task lateness) than their non-adaptive counterparts. In particular, the simulation results indicate that, for small systems, the adaptive metrics can improve the success ratio by as much as an order of magnitude. Moreover, the new adaptive metrics are found to exhibit very robust performance over a large variety of application and architecture scenarios.     

提高软非周期任务响应性能的调度算法

实时环境中常常既包含硬周期任务,又包含软非周期任务,引入一种改进软非周期实时任务响应时间的算法.已有的解决混合任务调度问题的方法都是基于速率单调（Rate Monotonic）策略的,其中从周期任务“挪用时间”的算法被证明优于其他所有算法.但是,速率单调算法限制了处理器的使用率,从而使周期任务的可“挪用”时间受到限制.最后期限驱动（Deadline Driven）策略DD可使潜在的处理器利用率达到100%.新算法正是在周期任务的调度中适当加入了DD策略,从而使非周期任务的响应时间得以缩短.仿真实验的结果表明,这种算法的性能优于已有的所有算法,而由它所带来的额外开销却不算很高.