图形工作流驱动的空间信息服务链研究

空间信息服务链由于其空间数据操作对时间的要求以及其应用于工作流对接口要求的特殊性,使得传统的工作流很难调度空间信息服务链,主要的问题在于接口不匹配以及运行效率低下.在传统WPS服务链的基础上,针对其不足提出了基于图形工作流的空间信息服务链聚合模型.通过分析工作流调度空间信息服务链存在的主要问题,提出相应的接口改进方法,以及基于地址引用、压缩GML的数据调度策略.并结合Kepler工作流引擎,给出了空间信息服务链聚合的具体过程.经过实验证明：基于图形工作流环境,不但突破了以往空间信息服务应用于工作流的接口瓶颈,而且改进的数据流调度策略有助于空间信息服务链执行效率的提高.     

Performance of Scheduling Strategies for Client–Server Systems

Two approaches to the improvement of the performance of client–server systems, multithreading and scheduling of servers, are investigated. Both of these approaches are observed to have a significant impact on system performance. The use of multithreading improves throughput characteristics of systems, whereas the deployment of appropriate scheduling strategies at servers can produce a significant improvement in mean client response times. Based on a simulation model a number of basic questions that are important in the context of scheduling on nonmultithreaded, as well as multithreaded, systems are analyzed. Two important factors, monopolization of servers by large requests and software bottlenecks, are observed to be important in the context of scheduling on client–server systems. Both server scheduling, as well as multithreading, can be used to control these effects and lead to a higher system performance. Scheduling policies based on request characteristics are observed to perform well. A new request characteristic that is useful in the scheduling of client–server systems in the presence of software bottlenecks is proposed. Selection of both the server process, as well as the thread within the server, is required when multiple server, are co-located on the same CPU. A comparison between two scheduling approaches, single level and two level is presented in the paper. The results of this research are useful primarily in the design of operating systems for client–server systems and are also of interest to system designers and users.     

基于广义资源日历约束的项目资源调配优化问题

资源受限项目调度问题(RCPSP)是最具代表性的项目调度问题之一,针对实际情况中考虑资源投入的必要性,建立一种以资源投入为变量的基于广义资源日历约束的项目调度优化模型.首先,引入组合优先关系的概念对广义资源日历的概念和具体内容进行整合和完善,为了避免传统网络图在表示组合优先关系时出现的网络循环等弊端,使用节点表示活动开始和结束的瞬时状态改进节点网络图;其次,考虑活动优先关系、活动持续时间、不可更新资源总量和资源日历约束,以项目工期最短和项目成本最小为优化目标,运用CP优化器求解所建立的多目标优化模型;最后,通过设计仿真算例并进行数值实验验证模型的准确性和高效性.     

基于流量调度的SDN数据中心网络拥塞控制算法

针对采用软件定义网络(SDN)的数据中心网络拥塞的问题,提出一种基于流量调度的数据中心网络拥塞控制算法。当链路发生拥塞时,该算法首先判别拥塞链路中 链路上关键度最大的大流,然后对大流进行重路由计算,选择调度开销最小的流,并进行调度代价计算,最后对调度代价最小的流进行调度。实验结果表明,所提算法能够有效缓解网络拥塞,降低丢包率,提高链路利用率,使得网络性能更为稳定。     

基于GPGP协同机制的多Agent车间调度方法研究

车间调度作为车间制造系统的重要组成部分,影响着整个车间制造系统的敏捷性和智能性.但是,由于资源和工艺约束的并存,使得车间调度成为一类NP-hard问题.基于静态的智能算法与动态的多Agent思想,提出了一种结合通用部分全局规划(generalized partial global planning,GPGP)机制与多种智能算法的多Agent车间调度模型,设计了从"初始宏观调度"到"微观再调度"的大规模复杂问题的调度步骤,并构建了一个柔性强且Agent可自我动态调度的仿真系统.同时,从理论上总结了GPGP基本协同机制的策略,实现了二级多目标优化调度.最后使用DECAF仿真Agent软件模拟了车间调度的GPGP协同机制,并与CNP,NONE机制进行了比较.结果表明,所提出的模型不仅提高了调度的效率,而且降低了资源的损耗.     

应急物流车辆调度多目标鲁棒优化研究

针对应急物流车辆调度问题中对于经济性、时效性、可靠性和鲁棒性的多种要求，考虑了含有时间窗、不确定需求、不确定行驶时间，以及路段含有失效风险的多目标鲁棒车辆路径优化问题，通过定义新的成本函数、满意度函数、风险度函数和鲁棒度函数作为四个优化目标来构建模型，并基于鲁棒优化理论将不确定模型转化为确定性鲁棒对应模型求解，为解决不确定环境下优化问题提供了新的思路。算法方面，主要基于SPEA2算法框架求解该多目标模型，针对算法缺陷提出多种改进策略，并通过对比实验证明了改进策略的有效性。     

软件定义网络架构下基于流调度代价的数据中心网络拥塞控制路由算法

针对传统数据中心网络极易发生拥塞的问题,提出了在软件定义网络（SDN）的架构下设计基于流调度代价的拥塞控制路由算法加以解决。首先,进行拥塞链路上的大小流区分,并对所有大流的各条等价路径进行路径开销权重的计算,选择权重最小的路径作为可用调度路径;然后,使用调度后路径开销变化量和流占用带宽比例来共同定义流调度代价;最终选择调度代价最小的流进行调度。仿真结果表明,所提算法能在网络发生拥塞时降低了拥塞链路上的负荷,并且与仅进行流路径选择的拥塞控制算法相比,提高了链路利用率,减少了流传输时间,使得网络链路资源得到更好的利用。     

基于多目标数学规划的网络多源数据调度仿真

传统的目标网络多源数据调度方法通常以时间或费用为单一调度优化目标,无法实现任务完成时间以及任务执行成本之间的均衡,造成系统资源利用率较低。针对上述问题,提出一种基于多目标数学规划的网络多源数据调度方法。使用DAG构建网络多源数据流,确定多源数据调度任务模型的信任关系,以任务完成时间、任务完成成本、资源利用率为优化目标,建立多目标调度任务模型。对模型进行求解,在遗传算法变异操作中加入粒子群算法,对数据变异的方向与幅度进行调整,完成网络多源数据调度。仿真证明,所提方法相较于传统方法,在多源数据的调度上成本更低、资源利用率更高,并且调度任务目标完成时间更短。     

数据中心网络流量调度的研究进展与趋势

近年来,流量调度已经发展成为网络领域的热点研究问题.该问题主要决定何时以及以多大速率传输网络中的每条数据流,其对网络性能和应用性能都具有十分重要的影响.然而,在托管着许多大规模互联网应用的数据中心中,流量调度问题正面临着流量矩阵多变、流量种类混杂、以及流量突发等与流量模型相关的挑战.此外,随着数据中心规模的不断壮大,流量调度问题还面临着网络带宽动态化、网络拥塞随机化、以及网络目标多样化等与网络模型相关的挑战.为了进一步提升对数据中心流量调度的关注和理解,推动流调度技术在实际应用中的不断发展,本文分别从调度目标、调度方式和调度对象这三个维度对数据中心网络流调度的相关研究工作进行了分析和对比,并概括出如下结论:现有研究主要以分布式、集中式或混合式的调度方式对数据中心内、数据中心间或数据中心与用户间的流进行高效地调度,从而达到带宽保障、时限保障、最小化流完成时间、最小化Coflow完成时间、公平性保证、最小化流传输成本等目标.本文最后还指出了四个数据中心流调度的未来发展方向,并相应提出尚未解决的研究问题.     

A neural network model and algorithm for the hybrid flow shop scheduling problem in a dynamic environment

A hybrid flow shop (HFS) is a generalized flow shop with multiple machines in some stages. HFS is fairly common in flexible manufacturing and in process industry. Because manufacturing systems often operate in a stochastic and dynamic environment, dynamic hybrid flow shop scheduling is frequently encountered in practice. This paper proposes a neural network model and algorithm to solve the dynamic hybrid flow shop scheduling problem. In order to obtain training examples for the neural network, we first study, through simulation, the performance of some dispatching rules that have demonstrated effectiveness in the previous related research. The results are then transformed into training examples. The training process is optimized by the delta-bar-delta (DBD) method that can speed up training convergence. The most commonly used dispatching rules are used as benchmarks. Simulation results show that the performance of the neural network approach is much better than that of the traditional dispatching rules.This revised version was published in June 2005 with corrected page numbers.     

基于深度强化学习的微电网在线优化调度

提出一种基于深度强化学习的微电网在线优化调度策略.针对可再生能源的随机性及复杂的潮流约束对微电网经济安全运行带来的挑战,以成本最小为目标,考虑微电网运行状态及调度动作的约束,将微电网在线调度问题建模为一个约束马尔可夫决策过程.为避免求解复杂的非线性潮流优化、降低对高精度预测信息及系统模型的依赖,设计一个卷积神经网络结构学习最优的调度策略.所提出的神经网络结构可以从微电网原始观测数据中提取高质量的特征,并基于提取到的特征直接产生调度决策.为了确保该神经网络产生的调度决策能够满足复杂的网络潮流约束,结合拉格朗日乘子法与soft actor-critic,提出一种新的深度强化学习算法来训练该神经网络.最后,为验证所提出方法的有效性,利用真实的电力系统数据进行仿真.仿真结果表明,所提出的在线优化调度方法可以有效地从数据中学习到满足潮流约束且具有成本效益的调度策略,降低随机性对微电网运行的影响.     

云制造环境下面向多目标优化的虚拟资源调度研究

为解决云制造环境下虚拟资源调度存在的算法求解效率不高、模型建立缺乏考虑任务间关系约束和任务间及子任务间的物流时间及成本因素等不足,构建了兼顾交货期时间最小化、服务成本最低化、服务质量最优化为目标的多目标虚拟资源调度模型;采用一种基于项目阶段的双链编码方式进行编码,并提出自适应交叉与变异概率公式,以避免交叉、变异概率始终不变导致算法效率下降与过早收敛的问题;在此基础上利用基于项目阶段的多种交叉变异策略相结合的改进遗传算法进行求解,保证了算法的全局与局部搜索性能。实例结果表明,相比于传统的模型与算法,该模型适用性更强,改进的遗传算法在求解效率、准确度与稳定性方面均有较大提高。     

基于流量特征的流调度策略研究综述

软件定义网络将网络的数据层和控制层相分离,具有可编程性强和全局网络视图的优点,这一优点被越来越多的应用于数据中心网络流调度中。然而,数据中心网络中大象流和老鼠流共存的流量特征是流调度技术的一大挑战。对基于流量特征的流调度策略进行了综述,首先梳理了基于软件定义网络的数据中心网络的理论基础,然后对大象流、老鼠流、大象流与老鼠流三个方面的流调度策略进行了分析和总结,并探讨了相比于传统的流调度技术,在软件定义网络架构下基于流量特征的流调度策略的优势。最后针对目前基于流量特征的流调度技术面临的若干主要问题进行了讨论,指出了下一步的研究方向。     

基于区块链的云数据审计方案

云存储凭借高扩展性、高可靠性、低成本的数据管理优点得到用户青睐。然而,如何确保云数据完整性成为亟待解决的安全挑战。目前的云数据完整性审计方案,绝大部分是基于半可信第三方来提供公共审计服务,它们存在单点失效、性能瓶颈以及泄露用户隐私等问题。针对这些缺点提出了基于区块链的审计模型。该模型采用分布式网络、共识算法建立一个去中心化、易扩展的网络解决单点失效问题和计算力瓶颈,利用区块链技术和共识算法加密用户数据保证数据不可窜改和伪造,确保了用户数据的隐私。实验结果表明,与基于半可信第三方云数据审计方案相比,该模型能够保护用户隐私,显著提高了审计效率,减少通信开销。     

基于Petri网的网格调度模型研究

网格环境具有异构性、动态性和不可靠性,为了合理而经济地利用资源,本文提出一个基于QoS且具有容错性的任务调度算法,以时间和费用的预算以及时间和费用的权重比值作为QoS参数。使计算过程和通信过程重叠,以隐藏网络时延。本文用随机Petri网模型描述网格环境中的任务调度模型;定义了随机Petfi肉的可达图,用来分析任务调度模型的性能。通过分析和模拟,反映此算法能够在满足用户的时间和费用的限制,具有容错性,任务完成时间短,以及综合花费少等优点。     

智能电网系统中费控服务的优化调度研究

智能电网系统中,费控指令通过应用服务器集群被下发到前置服务器集群,再通过前置服务器集群被下发到终端,终端接收到指令后将指令下发到特定的智能电表.在这个过程中,应用服务器集群和前置服务器集群的可用资源不对等,导致负载不均衡,影响费控服务执行的效率和成功率;前置服务器集群中前置机与终端的固定匹配方式,容易导致前置服务器集群负载不均衡,延长了费控指令完成时间,降低了费控服务的执行效率.为此深入研究费控指令的下发过程,分析存在的问题和瓶颈,提出了一种基于负载预测和负载均衡的费控服务优化调度模型.该模型首先提出一种基于时间序列的负载预测方法,实现服务器负载的预测;基于预测负载,提出一种费控指令的均衡下发算法,为不同负载的服务器下发合适规模的费控指令,以提高指令下发的效率和成功率;针对前置服务器集群可能出现的负载不均衡,提出一种基于图聚类的终端均衡布局算法.实验结果表明,本文提出的费控服务优化调度模型,均衡了两个集群和终端之间的负载,有效提高了费控指令下发的效率和成功率.     

Distributed scheduling strategy for divisible loads on arbitrarily configured distributed networks using load balancing via virtual routing

In this paper, we consider a scheduling problem for divisible loads originating from single or multiple sites on arbitrary networks. We first propose a generalized mathematical model and formulate the scheduling problem as an optimization problem with an objective to minimize the processing time of the loads. We derive a number of theoretical results on the solution of the optimization problem. On the basis of these first set of results, we propose an efficient algorithm for scheduling divisible loads using the concept of load balancing via virtual routing for an arbitrary network configuration. The proposed algorithm has three major attractive features. Firstly, the algorithm is simple to realize and can be implemented in a distributed fashion. The second one is in its style of working by avoiding the need for generating a timing diagram explicitly for any complex networks having an arbitrary network topology. The last one is its capability of handling divisible loads originating from both single and multiple sites. When divisible loads originate from a single node, we compare the proposed algorithm with a recently proposed RAOLD algorithm which is based on minimum cost spanning tree [J. Yao, V. Bharadwaj, Design and performance analysis of divisible load scheduling strategies on arbitrary graphs, Cluster Computing 7(2) (2004) 191–207]. When divisible loads originate from multiple sites, we test the performance on sparse, medium and densely connected networks. This is the first time in the divisible load theory (DLT) literature that such a generic approach for handling divisible loads originating from multiple sites on arbitrary networks employing load balancing via virtual routing is attempted.     

Urban traffic congestion propagation and bottleneck identification

Bottlenecks in urban traffic network are sticking points in restricting network collectivity traffic efficiency. To identify network bottlenecks effectively is a foundational work for improving network traffic condition and preventing traffic congestion. In this paper, a congestion propagation model of urban network traffic is proposed based on the cell transmission model （CTM）. The proposed model includes a link model, which describes flow propagation on links, and a node model, which represents link-to-link flow propagation. A new method of estimating average journey velocity （AJV） of both link and network is developed to identify network congestion bottlenecks. A numerical example is studied in Sioux Falls urban traffic network. The proposed model is employed in simulating network traffic propagation and congestion bottleneck identification under different traffic demands. The simulation results show that continual increase of traffic demand is an immediate factor in network congestion bottleneck emergence and increase as well as reducing network collectivity capability. Whether a particular link will become a bottleneck is mainly determined by its position in network, its traffic flow （attributed to different OD pairs） component, and network traffic demand.     

Conformance checking and performance improvement in scheduled processes: A queueing-network perspective

Service processes, for example in transportation, telecommunications or the health sector, are the backbone of today׳s economies. Conceptual models of service processes enable operational analysis that supports, e.g., resource provisioning or delay prediction. In the presence of event logs containing recorded traces of process execution, such operational models can be mined automatically.In this work, we target the analysis of resource-driven, scheduled processes based on event logs. We focus on processes for which there exists a pre-defined assignment of activity instances to resources that execute activities. Specifically, we approach the questions of conformance checking (how to assess the conformance of the schedule and the actual process execution) and performance improvement (how to improve the operational process performance). The first question is addressed based on a queueing network for both the schedule and the actual process execution. Based on these models, we detect operational deviations and then apply statistical inference and similarity measures to validate the scheduling assumptions, thereby identifying root-causes for these deviations. These results are the starting point for our technique to improve the operational performance. It suggests adaptations of the scheduling policy of the service process to decrease the tardiness (non-punctuality) and lower the flow time. We demonstrate the value of our approach based on a real-world dataset comprising clinical pathways of an outpatient clinic that have been recorded by a real-time location system (RTLS). Our results indicate that the presented technique enables localization of operational bottlenecks along with their root-causes, while our improvement technique yields a decrease in median tardiness and flow time by more than 20%.     

考虑广义需求侧资源的深度置信网络短期负荷预测方法

随着智能电网信息化水平的不断提高以及可控负荷、分布式电源和储能等广义需求侧资源的大量接入,将产生海量负荷数据集并改变负荷特性.为了提高负荷预测精度,提出了一种考虑广义需求侧资源的深度置信网络(DBN)负荷预测方法.首先,借助负荷聚合商确定了广义需求侧资源参与电力市场的机制,构建了基于合同的广义需求侧资源调度模型,并利用该模型求解广义需求侧资源参与电力市场的最优调度计划.其次,引入DBN结构,并将广义需求侧资源的最优调度计划作为其输入量,建立了短期负荷预测模型.最后,以实际数据进行仿真测试,结果表明,本文所提方法具有更高的预测精度.