一种多核系统可靠性加强的任务调度方法

 多核系统已经被广泛应用于各行各业,其稳定性和可靠性也越来越受到人们的关注.在分析了现有芯片可靠性模型的基础上,增加温度和工作负载对多核芯片可靠性的影响,建立了对多核系统的可靠性评估模型.该模型以处理器为依托,从指令到任务,逐层构建可靠性评估指标,以便于定量分析影响多核系统可靠性的因素.同时,以该模型为指导,设计了一种面向多核系统可靠性的任务调度方法,该方法通过对评估指标值的计算,选择评估指标值尽可能高的调度策略对多核系统中的任务进行调度,以减少由于芯片本身可靠性而导致的错误.通过模拟实验可以看出,该任务调度算法能有效减少系统的出错率45%左右,为系统的稳定运行提供了可靠的保证.     

RTAI实时调度算法及其调度器的改进

在实时系统中,任务调度策略是内核设计的关键部分.如何进行实时的任务调度,使任务能在特定的周期内完成是实时操作系统领域研究的一个热点问题.文中将一种基于RM算法的改进算法CPSS算法引入到RTAI调度器中,针对RTAI调度器在系统过载情况下出现调度性能下降等缺点,对RTAI调度器进行优化和改进.对改进后的调度器在调度时延方面和调度算法仿真方面进行了测试,实验证明了改进后的调度器能够提高Linux系统的实时性.     

基于多核DSP架构的机械相控阵任务调度

相控阵雷达的任务调度是影响相控阵雷达整体效能的关键因素,应根据雷达搜索和跟踪模式的不同需求,需要对计算资源进行有效调度。对于机械旋转相控阵雷达而言,其额外增加的机械约束特征决定了调度过程本身也需要大量计算资源。基于多核DSP并行结构对旋转相控阵雷达任务规划进行了不同资源的优化。模拟运行数据表明,基于多核DSP调度的架构可在有效实现基于预估计的任务调度过程,提高系统层面雷达任务管理的调度效率。     

ARINC653多核多分区操作系统的任务调度

随着航空电子系统综合化、模块化的发展,单核处理器已难以满足综合模块化航空电子(Integrated Modular Avionics,IMA)高性能的计算要求,多核处理器逐步在航空电子系统中得到运用。ARINC653作为 IMA 架构的标准应用接口,如何在满足实时性前提下充分利用多核处理器资源成为 ARINC653 任务调度的关键。针对航空电子系统任务调度的实时性要求,采用实时任务中的周期性任务模型,提出了一种满足实时性要求的实时多核静态轮转调度算法。该算法通过优化RL(Round Length)轮转参数、任务的权值分配和多核处理器分配策略,减少任务拆分次数,提高多核处理器利用率。仿真结果显示,该算法能够对分区操作系统的任务集合进行高效调度,明显提高多核处理器利用率。     

基于预配置和配置重用的粗粒度动态可重构系统任务调度技术

配置时间过长是制约可重构系统整体性能提升的重要因素,而合理的任务调度技术可有效降低系统配置时间。该文针对粗粒度动态可重构系统(CGDRS)和具有数据依赖关系的流应用,提出了一种3维任务调度模型。首先基于该模型,设计了一种基于预配置策略的任务调度算法(CPSA);然后根据任务间的配置重用性,提出了间隔配置重用与连续配置重用策略,并据此对CPSA算法进行改进。实验结果证明,CPSA算法能够有效解决调度死锁问题、降低流应用执行时间并提高调度成功率。与其它调度算法相比,对流应用执行时间的平均优化比例达到6.13%～19.53%。     

一种短消息发送调度平台的设计及实现

任务调度本身涉及到多线程并发、运行时间规则制定和解析、场景保持与恢复、线程池维护等诸多方面的工作.如果直接使用自定义线程的原始办法,开发任务调度程序是一项颇具挑战性的工作.如何设计一个维护和操作都相对简单的通用的调度平台系统,对于软件开发商来说是至关重要的.     

异构多平台信号处理任务调度研究

简单的并行计算或单一异构平台已经无法满足计算量大、复杂度高的信号处理和任务调度需求，异构多平台系统已经成为信号处理和任务调度的发展趋势。针对提高平台的吞吐量、处理器的利用率以及任务的感知等问题，文中对异构多平台信号处理模型进行了研究，并利用有向无环图对调度任务和软硬件资源建模。基于已提出的调度算法，对任务调度进行了归纳总结、对比分析，发现基于任务感知的混合调度算法能够较好地满足平台调度需求。利用基于任务感知的混合调度算法解决信号处理中的任务调度将是未来研究发展的趋势。     

一种基于DAG的并行系统任务调度方法

设计了一种适用于同构总线式多核环境的任务调度算法,着重优化了传统静态列表算法中对于任务节点等级较为粗糙的计算方式,并改善了对通信的调度。通过统计性调度实验,表明该任务调度算法相比传统的调度方法具有一定的优化效果。     

航空电子核心处理分区优化设计及仿真研究

具有严格实时保障和安全保障的航空电子核心处理系统广泛采用分区管理技术,通过分层调度结构,满足任务时空隔离运行.通过将分区抢占行为看作是一个虚拟周期任务,针对下层调度器分别采用固定优先级和动态优先级两种调度策略,讨论了分区调度设计问题;利用分区任务调度的空闲时间构建分区调度保障条件,给出了满足任务可调度性的分区参数求解表...     

基于5G+MEC的电网边缘计算平台任务安全性调度方法

为保证电网边缘计算平台任务调度的安全性以及任务调度所需的数据质量,提出基于5G+MEC的电网边缘计算平台任务安全性调度方法。结合机密性服务和完整性服务,构建任务调度安全等级模型,约束调度任务队列调度传输过程中的风险,实现5G核心网的安全传输;确认优先级队列类型,选择最小化队列与最大队列,进行数据资源最大化支持、MEC 设备端的任务调度,构建分布式任务调度模型,并利用 Lyapunov候选函数提升任务调度的稳定性,通过交替方向乘子法求解模型,获取任务安全性调度最优解。测试结果表明,应用该方法后,风险概率结果均在0.15～0.35的范围波动,MEC设备提供的相关数据与核心服务器调度任务的拟合程度均高于0.92;任务调度数据的质量分值也高于0.94。     

Design of a hard real-time multi-core testbed for energy measurement

This paper presents a systematic methodology for designing a hard real-time multi-core testbed to validate and benchmark various rate monotonic scheduling (RMS)-based task allocation and scheduling schemes in energy consumption. The hard real-time multi-core testbed comprises Intel Core Duo T2500 processor with dynamic voltage scaling (DVS) capability and runs the Linux Fedora 8 operating system supporting soft real-time scheduling. POSIX threads API and Linux FIFO scheduling policy are utilized to facilitate the design and Dhrystone-based tasks are generated to verify the design. A LabView-based DAQ system is designed to measure the energy consumption of CPU and system board of the testbed. A case study of task allocation and scheduling algorithms is also presented that aim to optimize the schedule feasibility and energy consumed by the processor and memory module in the multi-core platform. The experience from the implementation is summarized to serve as potential guidelines for other researchers and practitioners.     

开放式悬挂物管理系统任务调度策略研究

针对开放式悬挂物管理系统(Stores Management System,SMS)硬实时、软实时和非实时任务并存,任务需动态加入和退出,且任务执行时间不确定导致的调度困难,提出一种改进的基于服务器的任务调度策略.系统调度器由任务接收控制、调度服务器和自适应控制3大模块组成,其中任务接收控制可在不进行全局可调度性分析的...     

Synthesis of Hard Real-Time Application Specific Systems

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.     

A non‐cooperative game model for reliability‐based task scheduling in cloud computing

Cloud computing is a newly emerging distributed system. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes, so that the tasks can get a balanced allocation or each task's execution cost decreases to the minimum, or the overall system performance is optimal. Unlike task scheduling based on time or cost before, aiming at the special reliability requirements in cloud computing, we propose a non‐cooperative game model for reliability‐based task scheduling approach. This model takes the steady‐state availability that computing nodes provide as the target, takes the task slicing strategy of the schedulers as the game strategy, then finds the Nash equilibrium solution. We also design a task scheduling algorithm based on this model. It can be seen from the experiments that our task scheduling algorithm is better than the so‐called balanced scheduling algorithm.     

云环境下任务调度算法对比研究

云计算是完全基于互联网的新兴技术。云计算环境中的任务调度问题一直都是该领域的研究热点。合理高效的任务调度算法在云环境中能有效的缩短任务完成时间,提高系统负载均衡,更好的满足用户与云提供商的需求。本文研究了云平台的任务调度机制,探究了任务调度过程中的关键性指标。通过云仿真平台CloudSim实现并分析了顺序调度算法、Min-Min算法和Max-Min算法,对比其在随机生成用户任务负载与虚拟机计算资源的情况下的任务完成时间,实验证明Min-Min算法与Max-Min算法均优于顺序调度算法。以此为未来研究提供实验支撑和方向。     

Parallelization Strategies and Performance Analysis of Media Mining Applications on Multi-Core Processors

This paper studies how to parallelize the emerging media mining workloads on existing small-scale multi-core processors and future large-scale platforms. Media mining is an emerging technology to extract meaningful knowledge from large amounts of multimedia data, aiming at helping end users search, browse, and manage multimedia data. Many of the media mining applications are very complicated and require a huge amount of computing power. The advent of multi-core architectures provides the acceleration opportunity for media mining. However, to efficiently utilize the multi-core processors, we must effectively execute many threads at the same time. In this paper, we present how to explore the multi-core processors to speed up the computation-intensive media mining applications. We first parallelize two media mining applications by extracting the coarse-grained parallelism and evaluate their parallel speedups on a small-scale multi-core system. Our experiment shows that the coarse-grained parallelization achieves good scaling performance, but not perfect. When examining the memory requirements, we find that these coarse-grained parallelized workloads expose high memory demand. Their working set sizes increase almost linearly with the degree of parallelism, and the instantaneous memory bandwidth usage prevents them from perfect scalability on the 8-core machine. To avoid the memory bandwidth bottleneck, we turn to exploit the fine-grained parallelism and evaluate the parallel performance on the 8-core machine and a simulated 64-core processor. Experimental data show that the fine-grained parallelization demonstrates much lower memory requirements than the coarse-grained one, but exhibits significant read-write data sharing behavior. Therefore, the expensive inter-thread communication limits the parallel speedup on the 8-core machine, while excellent speedup is observed on the large-scale processor as fast core-to-core communication is provided via a shared cache. Our study suggests that (1) extracting the coarse-grained parallelism scales well on small-scale platforms, but poorly on large-scale system; (2) exploiting the fine-grained parallelism is suitable to realize the power of large-scale platforms; (3) future many-core chips can provide shared cache and sufficient on-chip interconnect bandwidth to enable efficient inter-core communication for applications with significant amounts of shared data. In short, this work demonstrates proper parallelization techniques are critical to the performance of multi-core processors. We also demonstrate that one of the important factors in parallelization is the performance analysis. The parallelization principles, practice, and performance analysis methodology presented in this paper are also useful for everyone to exploit the thread-level parallelism in their applications.

云计算环境下任务调度算法的研究

在云计算环境中存在庞大的任务数,为了能更加高效地完成任务请求,如何进行有效地任务调度是云计算环境下实现按需分配资源的关键。针对调度问题提出了一种基于蚁群优化的任务调度算法,该算法能适应云计算环境下的动态特性,且集成了蚁群算法在处理NP-Hard问题时的优点。该算法旨在减少任务调度完成时间。通过在CloudSim平台进行仿真实验,实验结果表明,改进后的算法能减少任务平均完成时间、并能在云计算环境下有效提高调度效率。     

一种基于协作型任务的网格资源调度算法

网格是建立在互联网上的新一代基础设施,网格计算中大部分任务为子任务之间具有复杂关系的协作型任务。首先对协作型进行了详细的分析,采用AOV网表示子任务之间的关系,针对协作型任务提出了一种基于遗传模拟退火算法的调度策略,该调度策略满足了对协作型任务资源调度的需求。同时还阐述了该算法的原理和操作步骤。     

一种云计算环境下任务调度策略

文章提出的问题是在云计算环境下任务调度策略。该策略的目标是将任务分配到计算单元上达到任务完成总时间最少和资源充分利用。基于此目标文章提出利用遗传算法对任务完成时间进行优化,并为处于空闲状态计算单元动态调整任务分配以改善资源利用率。利用CloudSim仿真平台验证该方法的有效性。