基于OpenStack的高资源利用率Docker调度模型

在现有OpenStack云平台与Docker容器技术的集成方案中,基于容器初始资源请求的调度模型由于未充分考虑容器运行时的实际资源使用情况,导致资源利用率较低。为满足云计算领域的高资源利用率和低成本需求,构建基于OpenStack云平台的Docker调度模型（DSM）,将其与OpenStack的Keystone、Glance以及Neutron组件的API进行交互,获取创建容器所需的镜像、网络等资源,同时调用Docker Engine提供的API部署容器,对容器生命周期进行高效灵活管控。通过融合初始化模块、资源实时感知模块、容器调度模块、资源实时监测模块和容器迁移模块,并在容器调度模块中利用资源可用度评估与优先级决策调度机制为容器选择最优的计算节点,实现OpenStack云平台中资源的高效利用。实验结果表明,与经典Nova-Docker和Yun集成方案采用的调度模型相比,DSM调度模型在CPU和内存利用率上至少提升38.54、30.17个百分点和38.40、28.69个百分点。     

An integrated approach for requirement selection and scheduling in software release planning

It is essential for product software companies to decide which requirements should be included in the next release and to make an appropriate time plan of the development project. Compared to the extensive research done on requirement selection, very little research has been performed on time scheduling. In this paper, we introduce two integer linear programming models that integrate time scheduling into software release planning. Given the resource and precedence constraints, our first model provides a schedule for developing the requirements such that the project duration is minimized. Our second model combines requirement selection and scheduling, so that it not only maximizes revenues but also simultaneously calculates an on-time-delivery project schedule. Since requirement dependencies are essential for scheduling the development process, we present a more detailed analysis of these dependencies. Furthermore, we present two mechanisms that facilitate dynamic adaptation for over-estimation or under-estimation of revenues or processing time, one of which includes the Scrum methodology. Finally, several simulations based on real-life data are performed. The results of these simulations indicate that requirement dependency can significantly influence the requirement selection and the corresponding project plan. Moreover, the model for combined requirement selection and scheduling outperforms the sequential selection and scheduling approach in terms of efficiency and on-time delivery.     

基于SDN的数据中心动态优先级多路径调度算法

随着云计算技术和分布式业务的发展,数据中心内部“东西向”大象流量激增,这部分大象流在调度不当的情况下容易发生碰撞,造成链路拥塞。本文提出一种基于软件定义网络（SDN）的动态优先级多路径调度算法（DPMS）。该算法根据数据中心流量的特点制定大象流和老鼠流调度模型,充分利用各网络节点间的冗余链路,提高资源利用率;并结合组表优化SDN架构中控制器和交换机的通信模式,降低了数据包处理时延。实验结果表明,相比ECMP和Hedera这2种调度策略,DPMS提高了网络吞吐量和链路利用率,减少了平均流完成时间,网络的整体性能有所提高。     

GENRES: An extension of Brooks Algorithm for project scheduling with resource constraints

In project scheduling by network analysis, traditional critical path methods fail to include resource considerations. Other methods must be used to allow for resource considerations. This article explores one area of resource considerations: project scheduling under resource constraints. The specific case investigated is the single resource, single project schedule. A model, entitled the GENRES search model, is developed. The model utilizes Brooks Algorithm (BAG) to generate the project schedule. The criteria used are various weighted combinations of ACTIM and ACTRES (Bedworth, Industrial Systems). The best project schedule is that which gives the least project duration.The GENRES model was found effective in finding project durations equal to or less than that of ACTIM, ACTRES or TIMRES (the combination of ACTIM and ACTRES with each given equal weight). The research also found that when the project completion time found by the algorithm approaches the critical path duration, resource leveling may be preferred.     

物联网环境下具有顺序约束关系的静态任务表调度算法

针对物联网异构调度环境下并行计算的静态任务调度问题,提出了一种基于最早完成时间策略改变调度顺序的表调度算法HDPTS。该算法针对现有表调度算法在调度前不能准确地确定调度顺序的问题,在IHEFT算法的基础上添加了一个动态优先级调度策略,当节点的前驱任务都已经完成调度任务时,就改变该节点的调度优先级。任务优先级的计算在所有前驱任务到达这个任务的最晚完成时间与所有资源上最大可以使用时间之间取最大值的基础上,同时考虑到分配到各个资源上的任务对后继任务的影响和资源上的负载情况,以及上行权重的计算值和对出口任务的影响,使得优先级计算更加合理,能够根据任务分配动态合理改变任务调度顺序。通过随机生成一个算例进行测试,结果表明HDPTS比IHEFT、HEFT在调度长度方面减少14.29%;对大量随机产生的特定结构的有向无环图(DAG)进行测试,测试结果显示HDPTS算法比IHEFT、HEFT和LDCP算法更有效。     

异构环境下相关任务调度免疫遗传算法的研究

合理的调度可以在很大程度上提高异构系统资源的利用率,在研究现有任务调度算法的基础上,定义了异构环境下相关任务调度的数学模型,并基于分层和启发式算法的思想,提出最多度动态调度算法MDSA和最少入度动态调度算法LIDSA构造初始种群;借鉴生物免疫系统抗体多样性产生及保持机理,定义了基于抗体的矢量距离、亲合力及浓度相关的选择概率,并改进交叉算子,这样可以在进化过程中保留优秀个体,同时抑制抗体群早熟;提出了高度遍历解码算法HTDA,简化算法实现过程.仿真实验结果表明,本文算法与其它调度算法比较,更能有效地实现资源的分配,可以成功应用于异构环境下相关任务调度.     

The EDL Server for Scheduling Periodic and Soft Aperiodic Tasks with Resource Constraints

In this paper, we are concerned with the problem of serving soft aperiodic tasks on a uniprocessor system where periodic tasks are scheduled on a dynamic-priority, preemptive basis and exclusively access to critical sections. Scheduling of tasks is handled by the Dynamic Priority Ceiling Protocol working with an Earliest Deadline scheduler. Our analysis determines the maximum processing time which may be stolen from periodic tasks without jeopardizing both their timing constraints and resource consistency. It provides the basis for an on-line scheduling algorithm, the EDL Server, to deal with the minimization of response times for soft aperiodic tasks.     

基于模糊理论的软件项目调度算法

针对软件项目调度问题中信息的不确定性及资源分配的特殊性需要,提出了一种基于模糊理论的软件项目调度算法FSSA。该算法采用模糊数表示任务的工期并应用遗传算法产生任务的调度序列。实验结果表明,FSSA算法能在很短的时间内得到理想的结果,有一定的应用价值。     

编程型游戏的线程调度模型设计与实现

编程型游戏采用多线程技术实现对角色控制程序的执行和调度。阐述了编程型游戏的线程调度模型和基于.NET框架的实现技术,包括协作多线程调度模型、调度线程的算法、基于AutoResetEvent的线程同步技术、角色控制程序的基本结构和线程管理模块的构成。测试结果表明,采用所述线程调度模型和技术的线程管理模块能够对角色线程进行有序调度,性能良好。     

Multi-mode renewable resource-constrained allocation in PERT networks

This paper presents a hybrid metaheuristic algorithm (HMA) for Multi-Mode Resource-Constrained Project Scheduling Problem (MRCPSP) in PERT networks. A PERT-type project, where activities require resources of various types with random duration, is considered. Each activity can be accomplished in one of several execution modes and each execution mode represents an alternative combination of resource requirements of the activity and its duration. The problem is to minimize the regular criterion namely project's makespan by obtaining an optimal schedule and also the amount of different resources assigned to each activity. The resource project scheduling model is strongly NP-hard, therefore a metaheuristic algorithm is suggested namely HMA. In order to validate the performance of new hybrid metaheuristic algorithm, solutions are compared with optimal solutions for small networks. Also the efficiency of the proposed algorithm, for real world problems, in terms of solution quality and CPU time, is compared to one of the well-known metaheuristic algorithms, namely Genetic Algorithm of Hartmann (GAH). The computational results reveal that the proposed method provides appropriate results for small networks and real world problems.     

基于时延的动态优先级调度算法

队列管理是提高网络QoS的一种有效方法。在基于时延的调度算法（BDS）基础上将时间片与优先级相结合,提出了一种基于时延的动态优先级调度算法（DDPQS）。为了实现该算法,针对进入缓冲区的每个子队列设置一个计数器,以调整的计数器值为基准来动态的改变队列的优先级,从而达到队列调度的效果;又从研究该算法的过程中,发现其局限性,即计数器值对时间片过于敏感的问题,于是进一步采用设置阈值进行区分的方法来优化。优化前后的仿真结果表明,时延和吞吐率性能具有明显改善。     

AFDX交换机的队列整形调度研究

AFDX交换机的静态优先级非抢占式队列调度(SPUS)算法不能消除输出分组中的随机抖动。为此,采用将虚拟时钟作为分组调度发放合法时间的方法对SPUS算法进行改进。在发往级联端口的分组输出之前,先经过整形调度使数据流均匀地输出,以确保交换机的服务质量。仿真结果表明,改进后的算法可消除输出分组随机抖动,适用于交换机级联端口。     

网格环境下基于信任模型的动态级调度

网格用户、资源和服务的不确定性潜在地影响网格应用任务的正常执行,这样使得设计既能减小应用任务执行时间又能减小欺骗可能性的调度算法十分困难．参考社会学的人际关系信任模型,建立网格节点信任推荐机制,并利用D-S理论对推荐证据进行综合分析,从而定义出基于不确定性推理理论的信任度计算函数．将该函数并入DLS算法得到“可信”动态级调度算法(TDLS),从而在计算调度级别时考虑网格节点的可信程度．仿真结果证实,提出的TDLS算法以小的时间花费为代价,能有效提高任务在信任方面的服务质量需求．     

Reactive scheduling in the multi-mode RCPSP

The multi-mode resource-constrained project scheduling problem (MRCPSP) involves the determination of a baseline schedule of the project activities, which can be executed in multiple modes, satisfying the precedence relations and resource constraints while minimizing the project duration. During the execution of the project, the baseline schedule may become infeasible due to activity duration and resource disruptions. We propose and evaluate a number of dedicated exact reactive scheduling procedures as well as a tabu search heuristic for repairing a disrupted schedule, under the assumption that no activity can be started before its baseline starting time. We report on promising computational results obtained on a set of benchmark problems.     

Priority scheduling with consolidation based backfilling algorithm in cloud

In cloud computing, scheduling plays an eminent role while processing enormous jobs. The paralle jobs utmost need parallel processing capabilities which leads to CPU underutilization mainly due to synchronization and communication among parallel processes. Researchers introduced several algorithms for scheduleing parallel jobs namely, Conservative Migration Consolidation supported Backfilling (CMCBF) and Aggressive Migration Consolidation supported Backfilling (AMCBF). The greatest challenge of a existing scheduling algorithm is to improve the data center utilization without affecting job responsiveness. Hence, this work proposes an Effective Multiphase Scheduling Approach (EMSA) to process the jobs. In EMSA, the jobs are initially preprocessed and batched together to avoid starvation and to mitigate unwanted delay. Later, an Associate Priority Method has been proposed which prioritizes the batch jobs to minimize the number of migrations. Finally, the prioritized jobs are scheduled using Priority Scheduling with BackFilling algorithm to utilize the intermediate idle nodes. Moreover, the virtualization technology partitions the computing capacity of the Virtual Machine (VM) into two-tier VM as foreground VM (FVM) and Background VM (BVM) to improve node utilization. Hence, Priority Scheduling with Consolidation based BackFilling algorithm has been deployed in a two-tier VM that processes the jobs by utilizing the VMs effectively. Experimental results show that the performance of the proposed work performs better than other existing algorithms by increasing the resource utilization by 8%.     

一种帧间相关的窗口限制实时系统的分析与研究

在研究窗口限制实时系统的基础上提出了一种受窗口限制的帧间相关周期模型。首先对该模型的调度性进行了分析与研究,得出了一些结论,然后提出了相应的动态双窗口限制调度算法,该算法充分利用了帧间相关的特点来提高实时任务的可调度性,减少窗口限制违例。同时,还进行了大量的模拟研究,这些模拟以传统的动态窗口限制调度算法为基准,将其应用于帧间相关的周期模型时的窗口限制违例情况与新算法进行比较,模拟结果表明,新算法的表现优于传统的动态窗口限制调度算法。     

动态规划-粒子群算法在水库优化调度中的应用

水库优化调度是一个典型的具有多约束条件的、动态的、非线性的优化问题。针对这些问题,利用动态规划-粒子群(DP-PSO)算法加以求解。利用动态规划中的多阶段最优策略原理,将水库优化调度问题转化为多阶段决策子问题,各个子问题采用粒子群算法优化求解。数值实验表明,在计算时段较多时,DP-PSO算法计算的可靠性明显优于一般的动态规划(DP)算法,在计算时间上,DP-PSO算法用时较动态规划-遗传算法(DP-GA)少。     

Manufacturing lot-sizing under MRP II environment: An improved analytical model & heuristic procedure

Under single-level lot-sizing problem, well known Wagner-Whitin algorithm based on Dynamic Programming (DP) provides optimal order schedule. Order schedule does not work properly under Manufacturing Resource Planning (MRP II), where it is considered multi-level capacitated problem and requirements of subcomponents are depend on the parent product. In this paper, we formulate a multi-level capacitated optimization model and develop a relatively efficient heuristic working under MRP II environment which considers work center capacities and interrelationship between levels in lot-sizing computation.     

FC-AE网络数据发送调度算法的设计与实现

针对FC-AE(Fibre Channel Avionic Environment)网络中缺乏实时有效的调度算法,提出一种基于位图映射的动态优先级调度算法(Bitmap-based Mapping of Dynamic Priority Scheduling-BMDPS),根据发送任务等待时间动态改变优先级,同时将不同优先级的发送任务映射到位图表中,来实现高优先级任务的发送调度;通过与"先来先服务"和"时间片轮转"两种调度算法进行对比,证明了该算法具有较小的平均响应时间和平均周转时间,且在最差的条件下,能保证FC-AE网络数据传输的实时性。     

一种基于汽车CPS的服务调度算法

驾驶者通过路边基础设施感知外部环境并根据经验作出反应是汽车信息物理融合系统的一个最基本的特点,研究汽车与路边基础设施信息交互对建设汽车信息物理融合系统具有重要意义。基于汽车与路边基础设施通信的场景,提出一种新的服务消息调度模型,设计了基于优先级的调度算法,采用贪心思想,优先调度效用值大的消息,将效用值小的消息进行插空调度,最后通过实验证明了本文算法的有效性。