基于3D碎裂度布局策略的可重构硬件任务调度算法

现有硬件任务调度算法任务描述不完善且忽视时间维上紧凑性。该文考虑任务下载时间、完善任务属性,以器件2维资源与时间建立3维资源模型,将任务布局问题抽象成特殊的3维空间放置问题,在此模型上分析出现有算法不能克服任务不可预知性和资源占用多变性,导致调度成功率和资源利用率低。针对此问题,该文提出了一种3维可重构任务调度算法3D_RTSA。设计并实现了基于任务紧迫度的调度策略和基于3D碎裂度的布局策略。与其他4种算法实验对比结果表明,在重负载、小任务C30情况下,3D_RTSA调度成功率比GC, Look-aheadest, SPSA, DTI算法分别高3%, 21%, 28%, 35%左右;在轻负载、大任务C50情况下,资源利用率比Look-aheadest, SPSA算法分别高5%, 18%左右,且该文算法时间复杂度并未增加。     

一种可重构阵列结构及其任务调度算法

提出了一种可以利用计算时间覆盖配置时间和数据传输时间的可重构阵列结构,并且针对该可重构阵列结构提出了一种表调度算法进行任务调度.在SOCDesigner平台上进行了软硬件协同仿真,对于IDCT,FFT,4×4矩阵乘法新可重构阵列相比原来的可重构阵列有平均约10%的速度提升.     

基于配置模式匹配和层次化映射结构的高效FPGA码流生成系统研究

码流生成在FPGA电子设计自动化(EDA)流程中,提供应用电路在芯片上物理实现所需的精准配置信息。现代FPGA的发展一方面呈现出器件规模及码流容量越来越大的趋势,另一方面越来越多可变阵列大小的嵌入式应用(例如eFPGA)又要求码流生成器具备更高的配置效率以及更精简的可重构数据库。针对码流生成时间增加的问题和阵列规模任意缩放的需求,该文提出一种模式匹配和层次映射的码流生成方法,即对编程单元按配置模式进行分类建模,在配置时按模型进行调用匹配,并采用了层次化的码流映射策略,使得数据库可随阵列排布调整动态生成。该方法可有效应对FPGA嵌入式应用中码流容量的增大以及阵列规模可变所带来的挑战,同时相比平面化的建模及映射方法,码流配置的时间复杂度由O(n)降低为O(lgn)。     

基于价值优化的相控阵雷达任务调度算法

针对相控阵雷达时间资源分配问题,该文提出一种基于价值优化的任务调度算法。首先建立任务调度属性参数,对跟踪任务队列进行可行性分析和筛选操作,确定跟踪任务调度属性。其次,根据任务最大价值及其变化斜率,建立关于实际执行时刻的动态任务价值函数,并基于此构建任务调度的价值优化模型,对跟踪任务执行时刻进行分配,以更好满足及时性原则。最后,利用执行跟踪任务间的空闲时间片对搜索任务进行调度。仿真结果表明,该文算法有效减小了时间偏移量,提升了实现价值率。     

基于列生成的毫米波多连接链路配置调度算法

多连接技术允许用户同时建立和保持与多个小区/接入点的连接,通过网络元素之间的协调在吞吐量和可靠性方面大幅提高网络性能。针对毫米波通信中超高频段的链路中断问题,研究了多连接基于链路配置的调度算法,以提高链路调度效率,降低复杂度。首先,在系统模型中采用链路配置作为优化变量;其次,设计了多连接比例公平的调度准则;最后,提出一种基于列生成算法的链路配置调度优化算法,利用Dantzig-Wolfe分解将原问题分解为限制主问题和定价问题,并结合分支定界方法获得最优解。仿真结果表明,所提算法能够在数值上逼近全局最优,并且比现有的毫米波蜂窝网络链路调度方案增益平均提高40%以上。     

区域医疗数据采集方法优化

本文研究了ETL任务调度优化问题,提出了一套基于Kettle改进的集群调度策略。该策略将ETL任务调度分为任务预处理、任务分配和任务执行三个阶段。任务预处理阶段根据业务紧迫度和任务负载模式,构建任务初始调度序列。任务分配阶段为了避免集群负载不均衡,依据任务待处理数据规模,引入贪心算法思想进行任务调度分配。任务执行阶段为保证任务执行机会均等,采用高响应比优先算法执行任务。实验结果表明本文所提策略对比Kettle原始策略,能够有效提升任务执行效率、缩短关键任务执行时间。     

基于双重自适应策略的旋转相控阵雷达任务调度方法研究北大核心CSCD

针对旋转相控阵雷达任务调度过程中资源利用不充分问题和调度算法优化需求,提出基于双重自适应策略的任务调度算法(TSM-DAS)。首先,通过探究旋转相控阵雷达扫描特性,将雷达任务分为两类;然后,针对不同类型任务的调度时间差异,提出任务调度的双重自适应策略;最后,结合任务时间窗、优先级和贪心算法,给出基于双重自适应策略的任务调度算法,并通过仿真实验对TSM-DAS进行验证。实验结果表明,相比其他同类算法,TSM-DAS能够有效提升雷达资源利用率,降低任务截止期错失率,从整体上提升旋转相控阵雷达的任务调度效能。     

面向多功能雷达的模型参数化脉冲级调度方法

多功能相控阵雷达的任务复杂性和实时性需求不断提高,传统的基于一定驻留周期的任务调度方法不能满足实时切换要求。针对此问题,提出了一种基于脉冲周期的任务切换和实时调度的方法,两级调度器与执行器一体化设计减少任务调度时间开销,综合考虑优先级调度算法和抢占式调度策略确保了任务有效执行,面向任务模型的参数化设计使得任务调度能灵活适应各类作战任务需求。仿真结果表明,此方法能较好地控制高优先级任务丢失率, 获得更高的任务执行有效性。     

一种基于风险感知策略的多节点任务调度方法

为解决无人机(UAV)集群任务调度时面临各节点动态、不稳定的情况,该文提出一种面向多计算节点的可尽量避免任务中断且具有容错性的任务调度方法。该方法首先为基于多计算节点构建了一个以最小化任务平均完成时间为优化目标的任务分配策略;然后基于任务的完成时间和边缘计算节点的存留时间两者的概率分布,将任务计算节点上的执行风险量化成额外开销时间;最后以任务的完成时间与额外开销时间之和替换原本的完成时间,设计了风险感知的任务分配策略。在仿真环境下将该文提出的任务调度方法与3种基准调度方法进行了对比实验,实验结果表明该方法能够有效地降低任务平均响应时间、任务平均执行次数以及任务截止时间错失率。证明该文提出的方法降低了任务重调度和重新执行带来的额外开销,可实现分布式协同计算任务的调度工作,为复杂场景下的无人机集群网络提供新的技术支持。     

基于人工蜂群算法的中继卫星任务调度研究

研究中继卫星任务调度问题可以为跟踪与数据中继卫星系统(TDRSS)的任务计划编排提供科学合理的决策方法,任务调度模型的建立与调度算法的设计是中继卫星任务调度的两个关键问题。该文针对中继卫星任务调度问题特点,综合考虑中继卫星与用户航天器之间具有可见时间窗、用户提交的任务属性、中继卫星前向资源受限等约束条件,建立了中继卫星任务调度约束规划模型并提出基于人工蜂群(ABC)算法的中继卫星任务调度算法。最后,通过仿真数据分析,表明基于人工蜂群算法的中继卫星任务调度算法是一种有效的、合理的调度方法。     

基于风险估计模型的网格任务动态级调度算法

网格中的各种资源的失效是不可避免的,为尽可能减少由于资源失效导致任务执行失败带来的影响,网格的任务调度算法的目标不仅要最小化任务执行的时间,还要兼顾考虑任务在资源上执行失败的风险。提出了Risk—DLS（Dynamic Level Scheduling）算法是将资源的风险估计模型与DLS算法相结合,通过仿真试验与DLS算法相比较,新算法在选择适当的参数的情况下,不仅能够最小化DAG型应用的完成时间,还能提高任务执行的成功率,有效的减少网格环境的不确定性对任务执行的影响。     

Resource scheduling in mobile edge computing using improved ant colony algorithm for space information network

With the development of space information network (SIN), new network applications are emerging. Satellites are not only used for storage and transmission but also gradually used for calculation and analysis, so the demand for resources is increasing. But satellite resources are still limited. Mobile edge computing (MEC) is considered an effective technique to reduce the pressure on satellite resources. To solve the problem of task execution delay caused by limited satellite resources, we designed Space Mobile Edge Computing Network (SMECN) architecture. According to this architecture, we propose a resource scheduling method. First, we decompose the user tasks in SMECN, so that the tasks can be assigned to different servers. An improved ant colony resource scheduling algorithm for SMECN is proposed. The heuristic factors and pheromones of the ant colony algorithm are improved through time and resource constraints, and the roulette algorithm is applied to route selection to avoid falling into the local optimum. We propose a dynamic scheduling algorithm to improve the contract network protocol to cope with the dynamic changes of the SIN and dynamically adjust the task execution to improve the service capability of the SIN. The simulation results show that when the number of tasks reaches 200, the algorithm proposed in this paper takes 17.52% less execution time than the Min-Min algorithm, uses 9.58% less resources than the PSO algorithm, and achieves a resource allocation rate of 91.65%. Finally, introducing dynamic scheduling algorithms can effectively reduce task execution time and improve task availability.     

基于改进蚁群算法的自适应云资源调度模型研究

对于传统蚁群算法用于云计算资源分配和调度问题过程中存在的不足,提出了一种可以提高负载均衡度、缩短任务执行时间、降低任务执行成本的改进自适应蚁群算法,改进算法以能够基于用户提交的任务求解出执行时间较短、费用较低,负载率均衡的分配方案为目标,通过CloudSim平台对传统蚁群算法、最新的AC-SFL算法、改进自适应蚁群算法进行仿真实验对比。实验数据表明,改进后的自适应蚁群算法能够快速找出最优的云计算资源调度问题的解决方案,缩短了任务完成时间,降低了执行费用,保持了整个云系统中心的负载均衡。     

Network real-time scheduling algorithm based on multi-feature dynamic priority

Real-time task scheduling system structure and task model were proposed aiming at the network real-time scheduling problem.The task degree of urgency was defined by considering the deadline of task,execution time and interval time between works.The task degree of tightness was proposed based on service-level assurance,according to functional importance of different tasks in the real-time task scheduling system.The thrashing limit for avoiding task switching frequently was acquired through dynamic regulation to task priorities by degree of urgency and degree of tightness,which guaranteed the success rate of tasks execution and utilization ratio of client execution.Test simulation results suggest that the multi-feature dynamic priority scheduling strategy improves the success rate of task scheduling and shorten the average response time,which suggests it has obvious superiority compared with BE and EDF scheduling algorithm.     

EA-DFPSO: An intelligent energy-efficient scheduling algorithm for mobile edge networks

Cloud data centers have become overwhelmed with data-intensive applications due to the limited computational capabilities of mobile terminals. Mobile edge computing is emerging as a potential paradigm to host application execution at the edge of networks to reduce transmission delays. Compute nodes are usually distributed in edge environments, enabling crucially efficient task scheduling among those nodes to achieve reduced processing time. Moreover, it is imperative to conserve edge server energy, enhancing their lifetimes. To this end, this paper proposes a novel task scheduling algorithm named Energy-aware Double-fitness Particle Swarm Optimization (EA-DFPSO) that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution time. The proposed EA-DFPSO algorithm applies a dual fitness function to search for an optimal tasks-scheduling scheme for saving edge server energy while maintaining service quality for tasks. Extensive experimentation demonstrates that our proposed EA-DFPSO algorithm outperforms the existing traditional scheduling algorithms to achieve reduced task completion time and conserve energy in an edge computing environment.     

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.     

云计算资源调度算法的研究与实现

在研究蚁群算法、任务分配和资源调度的基础上,提出了一种改进的蚁群资源调度算法.首先通过引入节点可信度机制在一定程度上增强了云计算资源的搜索能力和节点完成任务的成功率.然后在改进的算法中使用了信息素的局部更新机制和全局更新机制,可以有效地平衡负载.最后通过选取合适的参数利用CloudSim仿真工具对改进的资源调度算法进行实验测试,实验结果表明此算法缩短了任务的执行时间,改善了云计算资源调度的性能.