一种基于代价抢占的混合可重构任务调度算法*

针对同时存在独立任务和相依性任务的混合可重构任务调度,提出了基于代价抢占的混合可重构任务实时调度算法。提出了相依性任务等价运行截止时刻的计算方法,使混合可重构任务按照配置截止时刻排队配置。针对相依性任务调度特点,分析得到了相依性任务集合调度失败的充分条件,提前判定和丢弃无法调度成功的相依性任务集合;通过有限预配置防止相依性任务无效占用可重构资源;通过基于代价抢占减少调度失败任务个数。仿真结果表明,该调度算法提高了任务调度成功率。     

混合关键级多任务调度中低关键级任务的积极处理

当前的混合关键级多任务调度,一旦高关键级任务的执行时间需求增加,通常的做法是立即抛弃相对低关键级任务,以确保高关键级任务获得足够处理器时间。考虑到数据访问的一致性和完整性以及相应的性能损失,这种处理低关键级任务的方法过于消极;同时,任务的实际执行时间并不总是能达到最差情况下执行时间,且多处理器平台上的调度存在可观的空闲时隙。因此,完全可以也应该对低关键级任务采取更为积极的处理方法。基于同构多处理器平台,构建了两类队列,一类队列容纳回收的空闲时隙,另一类队列为任务队列,包括就绪任务队列和被抛弃的低关键级任务队列,针对这两种任务队列的特性采取不同的调度方案：就绪任务队列采用混合关键级局部调度,被抛弃的低关键级任务则对空闲时隙进行分配。仿真实验表明,此调度方法在保证高关键级任务截止时限的同时,能够使混合关键级系统的可接受任务集数目获得明显提升。     

FF现场总线系统中实时任务的建模与调度

本文研究了单网段 FF现场总线系统中具有时间约束和次序约束的实时任务，即 功能块任务和通信任务的建模与调度．首先，将功能块任务和通信任务等视为相同的任务， 在只考虑任务间次序约束的情况下，提出了基于紧凑模式的任务模型，以保证每个作业被尽 可能早地完成．其次，考虑单网段通信任务共享一个传输介质而引起的通信超时，提出了基 于作业速率单调优先级算法的扩展紧凑模式的任务调度算法，以满足实时任务的时间约束 和次序约束．最后，通过一个应用实例来描述实时任务的调度过程．     

Multimodal Pre-training Method for Vision-language Understanding and Generation

Most existing vision-language pre-training methods focus on understanding tasks and use BERT-like loss functions (masked language modeling and image-text matching) during pre-training. Despite their good performance in the understanding of downstream tasks, such as visual question answering, image-text retrieval, and visual entailment, these methods cannot generate information. To tackle this problem, this study proposes Unified multimodal pre-training for Vision-Language understanding and generation (UniVL). The proposed UniVL is capable of handling both understanding tasks and generation tasks. It expands existing pre-training paradigms and uses random masks and causal masks simultaneously, where causal masks are triangular masks that mask future tokens, and such pre-trained models can have autoregressive generation abilities. Moreover, several vision-language understanding tasks are turned into text generation tasks according to specifications, and the prompt-based method is employed for fine-tuning of different downstream tasks. The experiments show that there is a trade-off between understanding tasks and generation tasks when the same model is used, and a feasible way to improve both tasks is to use more data. The proposed UniVL framework attains comparable performance to recent vision-language pre-training methods in both understanding tasks and generation tasks. Moreover, the prompt-based generation method is more effective and even outperforms discriminative methods in few-shot scenarios.     

面向视觉语言理解与生成的多模态预训练方法

大多数现有的视觉语言预训练方法侧重于理解任务,并在训练时使用类似于BERT的损失函数(掩码语言建模和图像文本匹配).尽管它们在许多理解类型的下游任务中表现良好,例如视觉问答、图像文本检索和视觉蕴涵,但它们不具备生成信息的能力.为了解决这个问题,提出了视觉语言理解和生成的统一多模态预训练(unified multimodal pre-training for vision-language understanding and generation, UniVL). UniVL能够处理理解任务和生成任务,并扩展了现有的预训练范式,同时使用随机掩码和因果掩码,因果掩码即掩盖未来标记的三角形掩码,这样预训练的模型可以具有自回归生成的能力.将几种视觉语言理解任务规范为文本生成任务,并使用基于模版提示的方法对不同的下游任务进行微调.实验表明,在使用同一个模型时,理解任务和生成任务之间存在权衡,而提升这两个任务的可行方法是使用更多的数据. UniVL框架在理解任务和生成任务方面的性能与最近的视觉语言预训练方法相当.此外,实验还证明了基于模版提示的生成方法更有效,甚至在少数场景中它优于判别方法.     

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.     

基于设计迭代的耦合任务动态分配策略研究

为研究耦合迭代情况下的设计任务分配问题,引入任务转移矩阵,分析影响任务分配的动态性因素,并对设计任务量,设计成本和时间进行定量化描述。提出基于设计迭代的任务分配策略,并根据任务分配矩阵和关键路径任务分配矩阵建立设计任务动态分配数学模型。利用遗传算法进行求解。以平行分度凸轮设计为对象进行验证,证明了基于任务转移矩阵的设计任务动态分配策略的有效性。     

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.     

实时多处理器系统的双优先级调度算法

针对同构多处理器系统提出一种基于双优先级的实时任务调度算法.对偶发任务进行接受测试,进一步提高了系统对偶发任务调度的成功率.模拟结果表明,当多核处理器系统利用率达到极限时,该算法依然能够在完成强实时周期任务的成功调度前提下,保证软实时周期任务和偶发任务具有较高的调度成功率.     

VxWorks下声纳基阵稳定控制系统的软件实现

介绍了VxWorks操作系统中任务间的通信,声纳基阵稳定控制系统的结构,主要功能,软件任务的划分,在VxWorks中这些任务的实现及任务间的通信。同时介绍了VxWorks任务与Zinc任务的通信及实现,并对VxWorks任务间通信机制的性能作了比较,分析和总结。     

基于多核处理器并行系统的任务调度算法

针对多核处理器并行系统的特点,提出了相应的任务调度算法,该算法在任务调度之前加入了任务分配技术,通过合理的任务分配,可有效减少多个处理器间的通信开销,使任务调度效率更佳.仿真实现了该算法,并通过实验数据证明了该算法的优越性.     

Hybrid Two-Phase Task Allocation for Mobile Crowd Sensing

As a result of the popularity of mobile devices,Mobile Crowd Sensing (MCS) has attracted a lot of attention. Task allocation is a significant problem in MCS. Most previous studies mainly focused on stationary spatial tasks while neglecting the changes of tasks and workers. In this paper,the proposed hybrid two-phase task allocation algorithm considers heterogeneous tasks and diverse workers.For heterogeneous tasks,there are different start times and deadlines. In each round,the tasks are divided into urgent and non-urgent tasks. The diverse workers are classified into opportunistic and participatory workers.The former complete tasks on their way,so they only receive a fixed payment as employment compensation,while the latter commute a certain distance that a distance fee is paid to complete the tasks in each round as needed apart from basic employment compensation. The task allocation stage is divided into multiple rounds consisting of the opportunistic worker phase and the participatory worker phase. At the start of each round,the hiring of opportunistic workers is considered because they cost less to complete each task. The Poisson distribution is used to predict the location that the workers are going to visit,and greedily choose the ones with high utility. For participatory workers,the urgent tasks are clustered by employing hierarchical clustering after selecting the tasks from the uncompleted task set.After completing the above steps,the tasks are assigned to participatory workers by extending the Kuhn-Munkres (KM) algorithm.The rest of the uncompleted tasks are non-urgent tasks which are added to the task set for the next round.Experiments are conducted based on a real dataset,Brightkite,and three typical baseline methods are selected for comparison. Experimental results show that the proposed algorithm has better performance in terms of total cost as well as efficiency under the constraint that all tasks are completed.     

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.     

基于时间需求迭代和排队模型的开放式实时系统可调度性分析算法研究

基于RM调度策略和可延期服务器调度的开放式实时系统,以往的可调度性分析算法造成较低资源利用率.结合时间需求分析和服务台休假M/M/1/K排队模型,考虑带宽保留服务器,提出一种高资源利用率的可调度性分析算法,对系统中所有周期任务进行可调度性分析测试.给出其在临界点的响应时间;根据非周期事件到来率和接收缓冲定量分析非周期事件的平均响应时间和事件丢失率.实验表明,提出的可调度性分析方法通过估计任务的响应时间范围,能够在较高资源利用率下,验证多任务系统的可调度性.     

Problem partitioning in parallel processor simulation systems

Problem partitioning to solve ordinary differential equations on a parallel processor system using classical numerical integration methods involves defining and ordering computation tasks and scheduling the tasks for execution, In defining tasks there is a tradeoff between decomposing a computation into a large number of primitive tasks to expose all potential parallelism and decomposing it into a smaller number of tasks to simplify scheduling. Scheduling is an intractable problem; heuristic scheduling algorithms reduce the effort required to schedule tasks but cannot guarantee that the parallel solution will execute in minimum time. An example illustrates difficulties encountered in scheduling tasks for parallel computation and use of a dependency graph as a tool in problem partitioning. The need for an efficient mechanism for asynchronous data exchanges among processors is demonstrated.     

Isodecrement curves for task performance in hot environments

This paper proposes a set of isodecrement curves for performance of perceptual motor tasks as a function of temperature and exposure time. Tasks studied include reaction time, mental tasks, vigilance, tracking, complex tasks, and combination of these tasks.     

基于多样真实任务生成的鲁棒小样本分类方法

随着大数据、计算机与互联网等技术的不断进步,以机器学习和深度学习为代表的人工智能技术取得了巨大成功,尤其是最近不断涌现的各种大模型,极大地加速了人工智能技术在各个领域的应用.但这些技术的成功离不开海量训练数据和充足的计算资源,大大限制了这些方法在一些数据或计算资源匮乏领域的应用.因此,如何利用少量样本进行学习,也就是小样本学习成为以人工智能技术引领新一轮产业变革中一个十分重要的研究问题.小样本学习中最常用的方法是基于元学习的方法,这类方法通过在一系列相似的训练任务上学习解决这类任务的元知识,在新的测试任务上利用元知识可以进行快速学习.虽然这类方法在小样本分类任务上取得了不错的效果,但是这类方法的一个潜在假设是训练任务和测试任务来自同一分布.这意味着训练任务需要足够多才能使模型学到的元知识泛化到不断变化的测试任务中.但是在一些真正数据匮乏的应用场景,训练任务的数量也是难以保证的.为此,提出一种基于多样真实任务生成的鲁棒小样本分类方法(DATG).该方法通过对已有少量任务进行Mixup,可以生成更多的训练任务帮助模型进行学习.通过约束生成任务的多样性和真实性,该方法可以有效提高小样本分类方...     

基于排队论和时间需求分析法的实时系统时间行为分析

针对目前广泛使用的固态优先级RMS调度策略,利用时间需求分析算法对系统中所有周期任务进行可调度性分析测试,保证其在临界点仍可以满足时限。利用排队论中的M/M/1/K排队系统,根据非周期事件接收缓冲和可延期服务器定量分析非周期事件的平均响应时间和系统异步事件丢失率,使之符合系统要求。     

控制系统中实时任务的动态优化调度算法

提出一种新的调度算法——带有非周期服务器的EDF调度算法.分析了所有任务的可调度性,给出了可调度条件,并给出一种新的周期性任务模型以及主优先级和辅助优先级的概念.它们在保证任务可调度的前提下,对周期性任务的采样频率和控制延时进行优化.仿真结果表明,该算法可以提高周期性任务的采样频率,并降低控制延时,即能优化系统的性能.     

基于任务-角色的访问控制模型

本文介绍了一种称为基于任务一角色的访问控制机制。它从工作流中的任务角度建模,可以依据任务和任务状态的不同,对权限进行动态管理;阐述了任务和任务实例之间的关系,以任务为中介将角色和权限关联起来,并对其模型进行了形式化描述和分析。