An optimal algorithm for scheduling soft aperiodic tasks indynamic-priority preemptive systems

The paper addresses the problem of jointly scheduling tasks with both hard and soft real time constraints. We present a new analysis applicable to systems scheduled using a priority preemptive dispatcher, with priorities assigned dynamically according to the EDF policy. Further, we present a new efficient online algorithm (the acceptor algorithm) for servicing aperiodic work load. The acceptor transforms a soft aperiodic task into a hard one by assigning a deadline. Once transformed, aperiodic tasks are handled in exactly the same way as periodic tasks with hard deadlines. The proposed algorithm is shown to be optimal in terms of providing the shortest aperiodic response time among fixed and dynamic priority schedulers. It always guarantees the proper execution of periodic hard tasks. The approach is composed of two parts: an offline analysis and a run time scheduler. The offline algorithm runs in pseudopolynomial time O(mn), where n is the number of hard periodic tasks and m is the hyperperiod/min deadline     

面向物联网终端的任务相关性调度策略

对于具有相关性的任务,调度顺序不合理将影响任务的执行时间和实时性。结合物联网终端任务间依赖关系复杂的特点提出了一种利用任务相关性的调度策略。该策略设计了以作业轮询组为主体的任务模型,根据任务时限建立了优先级因子矩阵作为任务调度的凭据,对于周期任务,在每个任务执行完毕后生成,以任务相关性为参数的增量矩阵用以动态修改任务优先级,使前驱任务能优先执行;对于非周期任务采用了构建临时作业轮询组的方式进行抢占调度。测试结果表明,该策略能够有效减少具有相关性的周期任务集执行时间和调度失败次数,缩短非周期任务响应时间。     

Near‐optimal dynamic priority scheduling strategy for instance‐intensive business workflows in cloud computing

Utilization of cloud computing resources has made a fast growth in e‐business. Business and government agencies often need to handle large volume of service requests, the so‐called instance‐intensive business processes in a constrained period. On‐time completion for instance‐intensive business processes within the constrained time is a very important issue. In the past few years, traditional optimal task scheduling has been well researched and proven to be a nondeterministic polynomial (NP) time–complete problem. So many heuristic and metaheuristic algorithms are put forward to solve the issue with near‐optimal solutions. However, most of them just treat a single workflow instance as a multistep task without considering that steps within a task can be different types of activities. To explain multistep features of business workflows, a typical motivating instance‐intensive business example of security exchange and a multistep scheduling model for business workflows are introduced in this paper. Then our near‐optimal dynamic priority scheduling (DPS) strategy is proposed on the basis of the idea of Min‐Min heuristic algorithm and greedy philosophy. Compared to the first come first served and constrained Min‐Min by makespan and standard deviation, DPS can make a more optimized choice in each round of scheduling towards overall outcome. To show the effectiveness of DPS, theoretical minimum execution time (MET_theory) is used as a benchmark for evaluation based on simulation. The results show that the ratios between MET_theory and DPS are more than 98.5% by scheduling different orders of magnitude tasks from 1000 to 1 000 000. In particular, the ratio between MET_theory and DPS is nearly 99.9% with 1 000 000 tasks, which means that our DPS can get the near‐optimal result when scheduling large number of tasks.     

基于动态优先级策略的最优软非周期任务调度算法

周期任务与非周期任务的混合调度是实时调度研究的一个重要方向 通过定义“调度”和“逆调度” ,对实时周期任务集在使用EDF算法调度时的可挪用时间进行分析 ,求出了周期任务集在使用EDF调度时的最大可挪用时间 在此基础上 ,提出用于缩短非周期任务响应时间和周转时间的调度算法———ISA(idlestealingalgorithm) ISA算法充分使用最大可挪用时间 ,在保证周期任务满足最后期限的同时能取得非周期任务的最优响应时间和周转时间 证明了ISA算法的最优性 ,并使用仿真实验进行了性能验证     

EDF统一调度硬实时周期任务和偶发任务的可调度性判定算法

现有的硬实时周期任务和非周期任务的混合调度方法都没有保证非周期任务的实时性,所以不适合调度具有强实时要求的偶发任务.通过分析和计算EDF算法调度偶发任务所占用的空闲时间和挪用时间,以及调度后对空闲时间和最大可挪用时间的影响,提出一种采用EDF算法统一调度硬实时周期任务和偶发任务时的可调度性充分判定算法.最后用仿真实验得出了该算法在不同系统负载下的判定准确率和偶发任务的平均响应时间.     

云环境下周期和非周期混合实时任务双容错调度算法

云环境中的处理机故障已成为云计算不可忽视的问题,容错成为设计和发展云计算系统的关键需求。针对一些容错调度算法在任务调度过程中调度效率低下以及任务类型单一的问题,提出一种处理机和任务主副版本分组的容错调度方法;并给出了副版本可重叠执行的判定方法,以及任务最坏响应时间的计算公式。通过实验和分析表明,和以前算法相比,将处理机分成两组分别执行任务主版本和任务副版本,减少了任务调度所需进行可调度测试的时间,增加了副版本重叠执行的机会,减少了所需的处理机个数,对提高系统处理机的利用率和容错调度的效率具有重要的意义。     

随机任务在云计算平台中能耗的优化管理方法

针对云计算系统在运行过程中由于计算节点空闲而产生大量空闲能耗,以及由于不匹配任务调度而产生大量“奢侈”能耗的能耗浪费问题,提出一种通过任务调度方式的能耗优化管理方法.首先,用排队模型对云计算系统进行建模,分析云计算系统的平均响应时间和平均功率,建立云计算系统的能耗模型.然后提出基于大服务强度和小执行能耗的任务调度策略,分别针对空闲能耗和“奢侈”能耗进行优化控制.基于该调度策略,设计满足性能约束的最小期望执行能耗调度算法ME3PC(minimum expectation execution energy with performance constraints).实验结果表明,该算法在保证执行性能的前提下,可大幅度降低云计算系统的能耗开销.     

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.     

Data‐aware task scheduling on heterogeneous hybrid memory multiprocessor systems

In this paper, we propose a method about task scheduling and data assignment on heterogeneous hybrid memory multiprocessor systems for real‐time applications. In a heterogeneous hybrid memory multiprocessor system, an important problem is how to schedule real‐time application tasks to processors and assign data to hybrid memories. The hybrid memory consists of dynamic random access memory and solid state drives when considering the performance of solid state drives into the scheduling policy. To solve this problem, we propose two heuristic algorithms called improvement greedy algorithm and the data assignment according to the task scheduling algorithm, which generate a near‐optimal solution for real‐time applications in polynomial time. We evaluate the performance of our algorithms by comparing them with a greedy algorithm, which is commonly used to solve heterogeneous task scheduling problem. Based on our extensive simulation study, we observe that our algorithms exhibit excellent performance and demonstrate that considering data allocation in task scheduling is significant for saving energy. We conduct experiments on two heterogeneous multiprocessor systems. Copyright © 2016 John Wiley & Sons, Ltd.     

强实时环境下调度非周期任务的时限寻优方法

文章提出了强实时环境下调度弱时限非周期任务的时限寻优方法（ＤＯＡ）,该方法在保证周期任务和偶发性任务满足时限要求的前提下,使非周期任务的响应时间达到最优。它还可根据实时应用的需要对算法的执行性能和计算复杂度进行折衷调整。仿真实验表明,ＤＯＡ与现有的动态调度算法相比,使非周期任务响应时间更短,同时它收敛快,额外开销小,计算复杂度低,实现方便,因此是强实时环境下对周期任务与非周期任务进行混合调度的一种较好的方法。     

基于(m,k)-firm约束规范的混合任务调度算法研究

讨论了在准实时环境下,包括准实时周期任务和准实时非周期任务在内的混合任务调度算法HTSF.HTSF算法是在满足周期任务(m,k)-firm 约束规范的前提下提高非周期任务可调度性,同时合理利用可用空闲时间,提高整个系统的服务质量.HTSF算法给出了非周期任务的可调度性分析方法,同时采用静态调度与动态调度相结合的方法调度周期任务和非周期任务.模拟测试结果显示,系统对非周期任务的接收率比同类相关算法的接收率高.     

一种实时系统中的多任务可预测调度算法

通过研究实时系统中可能出现的任务类型，提出了一种实时系统任务模型和基于这个模型的多任务调度算法，分析了该算法下所有周期任务和非周期任务可调度的条件。该算法结合了DMS(Dcadlinc Monotonic Scheduling)和DD(Deadline Driven)算法，在保证系统中周期任务执行的前提下,也使非周期任务获得了，较好的响应时间。     

Dynamic Real-time Scheduling of Firm Periodic Tasks with Hard and Soft Aperiodic Tasks

In this paper, we address the problem of the dynamic scheduling of skippable periodic task sets (i.e., period tasks allowing occasional skips of instances), together with aperiodic tasks. Scheduling of tasks is handled thanks to the merging of two existing approaches: the Skip-Over task model and the EDL (Earliest Deadline as Late as possible) aperiodic task server. The objective is to provide two on-line scheduling algorithms, namely EDL-RTO and EDL-BWP, in order to minimize the average response time of soft aperiodic requests, while ensuring that the QoS (Quality of Service) of periodic tasks will never be less than a specified bound. We also extend our results to the acceptance of sporadic tasks (i.e., aperiodic tasks with deadlines). We show that these novel scheduling algorithms have better performance compared to related algorithms regarding aperiodic response time and acceptance ratio. Audrey Marchand guaduated in Computer Engineering at the Ecole polytechnique of the University of Nantes (France), in 2002. She is currently a PhD student at the University of Nantes. Her research interests include real-time scheduling theory, aperiodic service mechanisms, quality of service guarantees in soft real-time systems, and Linux-based real-time operating systems and applications. Maryline Chetto received the degree of Docteur de 3ème cycle in control engineering and the degree of Habilitée à Diriger des Recherches in Computer Science from the University of Nantes, France, in 1984 and 1993, respectively. From 1984 to 1985, she held the position of Assistant professor of Computer Science at the University of Rennes, while her research was with the Institut de Recherche en Informatique et Systèmes Aléatoires, Rennes. In 1986, she returned to Nantes and is currently a professor with the Institute of Technology of the University of Nantes. She is conducting her research at IRCCyN. Her main research interests include scheduling and fault-tolerance technologies for real-time applications. She has published more than 60 journal articles and conference papers in the area of real-time operating systems. She is the leader of a French national R&D project, namely Cleopatre, supported by the French government, which aims to provide free open source real-time solutions.     

A compact task graph representation for real-time scheduling

A new task graph representation, namely the compact task graph (CTG), is developed to aid in the scheduling of a set of communicating periodic real-time tasks. This representation explicitly expresses the potential for parallelism across tasks as well as the idle times that may be encountered within application tasks. A CTG based scheduler can generate schedules that are able to meet deadlines by interleaving the execution of tasks on a single processor and/or overlapping the execution of tasks on multiple processors. The construction of a CTG is based upon the busy-idle execution profiles for the tasks generated by the compiler. The profiles are computed assuming that sufficient resources are available for parallel execution of all tasks. Thus, they expose all opportunities for overlapped and interleaved execution. The compiler analyzes the profiles to identify useful opportunities for interleaving and expresses them in the CTG without unnecessary partitioning of the tasks. The CTG is powerful because it expresses schedules that are not expressed by existing approaches for constructing task graphs. Schedules can be generated efficiently since a CTG's construction minimizes the splitting of tasks. We briefly demonstrate the usefulness of CTGs in scheduling real-time tasks and scheduling monitoring tasks without affecting the timing of application tasks.Supported in part by the National Science Foundation through a Presidential Young Investigator Award CCR-9157371 and Grant CCR-9212020.     

A New Approach for Scheduling of Parallelizable Tasks in Real-Time Multiprocessor Systems

In a parallelizable task model, a task can be parallelized and the component tasks can be executed concurrently on multiple processors. We use this parallelism in tasks to meet their deadlines and also obtain better processor utilisation compared to non-parallelized tasks. Non-preemptive parallelizable task scheduling combines the advantages of higher schedulability and lower scheduling overhead offered by the preemptive and non-preemptive task scheduling models, respectively. We propose a new approach to maximize the benefits from task parallelization. It involves checking the schedulability of periodic tasks (if necessary, by parallelizing them) off-line and run-time scheduling of the schedulable periodic tasks together with dynamically arriving aperiodic tasks. To avoid the run-time anomaly that may occur when the actual computation time of a task is less than its worst case computation time, we propose efficient run-time mechanisms.We have carried out extensive simulation to study the effectiveness of the proposed approach by comparing the schedulability offered by it with that of dynamic scheduling using Earliest Deadline First (EDF), and by comparing its storage efficiency with that of the static table-driven approach. We found that the schedulability offered by parallelizable task scheduling is always higher than that of the EDF algorithm for a wide variety of task parameters and the storage overhead incurred by it is less than 3.6% of the static table-driven approach even under heavy task loads.     

多核系统中基于Global EDF 的在线节能实时调度算法

随着多核系统能耗问题日益突出,在满足时间约束条件下降低系统能耗成为多核实时节能调度研究中亟待解决的问题之一.现有研究成果基于事先已知实时任务属性的假设,而实际应用中,只有当任务到达之后才能够获得其属性.为此,针对一般任务模型,不基于任何先验知识提出一种多核系统中基于Global EDF在线节能硬实时任务调度算法,通过引入速度调节因子,利用松弛时间,结合动态功耗管理和动态电压/频率调节技术,降低多核系统中任务的执行速度,达到实时约束与能耗节余之间的合理折衷.所提出的算法仅在上下文切换和任务完成时进行动态电压/频率调节,计算复杂度小,易于在实时操作系统中实现.实验结果表明,该算法适用于不同类型的片上动态电压/频率调节技术,节能效果始终优于Global EDF算法,最多可节能15％～20％,最少可节能5％～10％.     

Algorithms and optimality of scheduling soft aperiodic requests in fixed-priority preemptive systems

In this paper, we investigate the problem of scheduling soft aperiodic requests in systems where periodic tasks are scheduled on a fixed-priority, preemptive basis. First, we show that given any queueing discipline for the aperiodic requests, no scheduling algorithm can minimize the response time of every aperiodic request and guarantee that the deadlines of the periodic tasks are met when the periodic tasks are scheduled on a fixed-priority, preemptive basis. We then develop two algorithms: Algorithm is locally optimal in that it minimizes the response time of the aperiodic request at the head of the aperiodic service queue. Algorithm is globally optimal in that it completes the current backlog of work in the aperiodic service queue as early as possible.     

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.     

A fast predictive algorithm with idle reduction for heterogeneous system scheduling

A heterogeneous task scheduling algorithm called Predict and Arrange Task Scheduling (PATS) algorithm was proposed to achieve a lower bound time complexity with minimum schedule length. Two major steps were introduced, i.e. earliest finish time with level-based task scheduling and idle slot reduction. In the first step, tasks are scheduled according to their predicted earliest finish time from the candidate task list and their dependencies. Scheduling is performed one level at a time starting from top level and transcend downward. In the second step, the idle time slots in each processing unit are minimized. Two sets of experiments were designed to evaluate the merits of proposed algorithm. The first experiment involved the task graphs used by other methods. These graphs are all synthesized. The second experiment concerned the task graphs derived from real world applications such as montage work flow, molecular dynamic code. The experimental results showed that the PATS algorithm yielded better average schedule length ratio, running time, and efficiency than the compared algorithms.     

计算网格中基于时间均衡的并行粗粒度任务调度算法

考虑网格资源异构、自治、动态等特性,讨论本地用户具有强占优先权情况下的任务调度问题,提出了TBBS(Time-Balancing Based Scheduling Algorithm)算法.建立调度优化模型,以期望完成时间最小为目标选择执行任务的最佳资源组合.以时间均衡策略将任务分解并调度到资源上执行,减少了子任务同步时因等待而产生的延时,获得较好的并行计算性能.采用重复调度策略,适应计算网格中资源的特性.