高铁旅客认知频谱共享算法研究

针对高铁环境中包含多个主用户的主服务和多个次用户的频谱分配问题,提出一种认知博弈频谱共享算法。引入次用户优先级和用户传输业务等级,满足不同优先级用户的不同业务频谱使用需求,并根据主基站和次用户的距离变化更新主服务向次用户提出的价格函数,获得静态博弈下的纳什均衡解,同时分析不完全信息和完全信息的动态博弈,确定不完全信息动态博弈的稳定区间。分别对静态博弈和动态博弈进行仿真,结果表明,在该算法下,当次用户优先级相同时,不同业务等级租借不同的带宽,业务等级越高,租借的带宽也越多,随着距离的变化,较高的业务等级频谱变化较为迅速,而在同一业务等级下,用户优先级越高,则会租借到更多的频谱。     

云计算中一种多DAG工作流可抢占式调度策略

为解决多用户工作流调度过程中的公平性问题,提高资源利用率,满足不同用户DAG工作流的不同QoS需求,提出了抢占式多DAG工作流动态调度模型。该算法将DAG工作流按照QoS需求进行优先级划分,采用高优先级作业优先占有资源的原则调度作业。相同优先级DAG工作流的任务依据带有启发性信息的slowdown进行资源抢占,进一步提高了作业调度的公平性;对于不同优先级的作业调度,提出了基于阈值的回填算法,该算法在保证作业调度公平的同时提高了资源利用率。     

基于联邦学习的车联网多维资源动态分配算法

考虑到车联网系统中多维资源消耗会随时间波动的特性和用户对高效计算服务以及数据隐私安全的需求,提出了一种基于联邦学习的车联网多维资源分配方法。一方面,综合考虑计算、缓存和带宽资源分配,保证计算任务的完成率,避免多维资源的冗余分配,基于该目标设计了一种深度学习算法,通过边缘服务器收集的数据预测各项资源的消耗量,以此为依据分配多维资源;另一方面,考虑到用户的数据隐私安全需求造成的数据孤岛问题,采用联邦学习架构以获得泛化性较好的神经网络模型。该算法能随时间调整多维资源的分配量,满足随时间变动的资源需求,保证车联网系统中计算任务的高效完成。实验结果表明该算法具有收敛速度快、模型泛化性好等特点,能以较少的通信轮数完成联邦学习的聚合。     

一种面向任务需求的群智感知任务分配模型

针对群智感知平台中的任务分配问题,提出了一种任务需求特征提取算法和用户标签分类方法相结合的T REA U LCM任务分配模型.首先,通过任务需求特征提取算法提取感知任务的类别关键词;然后,通过多线性神经网络和多核学习对数据集进行训练得到分类器,通过分类器对用户的类型标签进行预测;最后,根据任务的类别关键词结合空间位置信息和用户参与度筛选有该任务类别标签且最大化满足任务需求的用户分发任务.仿真结果表明,T REA U LCM任务分配模型在任务匹配度和任务分配效率方面有较好的可行性.     

计算网格环境下一个统一的资源映射策略

由于资源具有广域分布、异构、动态等特性,计算网格环境下资源的管理和调度是一个非常复杂且具有挑战性的问题.提出了计算网格环境下一组相互独立的计算任务(meta-task)的资源映射策略.该策略采用重复映射方法,以更好地适应网格计算环境下的动态性和自治性.算法考虑到任务的输入数据位置对映射效果的影响;通过定义效益函数,该策略在追求较小的任务完成时间的同时兼顾任务的服务质量(QoS)需求.模拟实验结果显示,该映射策略更符合计算网格的复杂环境,能够更好地满足不同用户的实际需要.     

基于不完全信息博弈的网格资源分配方法研究

针对网格环境下用户难以获得资源竞价所需的信息而导致的决策风险,将不完全信息资源竞价转化成完全信息下的重复博弈问题。分析了该博弈均衡解的存在性及求解过程,给出了相应的竞价算法,讨论了对用户低价联盟的抑制方法。仿真实验表明用户通过各阶段资源预配置的信息调整竞价策略,资源配置可逐步逼近均衡解,实现网格资源的优化配置。     

基于智能化集成设备的医院大数据信息化云测试系统设计

为满足医院信息化管理需求,解决医院信息软件测试的经验依赖性、不可量化性及兼容困难性等问题,实现高效率、高质量和低能耗软件运行目标,该研究基于已开发的MNSS（Medical Network System Simulator,医疗虚拟仿真）平台,设计了一种基于智能化集成设备的医院大数据信息化云测试系统,该系统内置任务分配器,根据不同用户需求,提供个性化智能终端服务;利用智能化集成设备实现快速边缘计算目标;利用机器学习算法解决软件自动化测评问题,并采用迁移学习方法和模糊神经网络完成资源调度任务;为医院信息化建设提供了一种高效、精准的测试工具。     

网格计算中工作流调度的非合作博弈优化*

在异构网络计算问题中,网格计算方法通过引入资源共享机制,可解决复杂的计算任务。然而在网格环境中,需要对网络可获得的资源进行合理调度和协调,才可以获得良好的网络工作流,以及合适的网络性能和网络响应时间。为了提高网格计算方法的任务调度和资源分配的能力和性能,提出了一种基于非合作博弈方式的博弈模型。该模型通过设定使用户的资源分配所需时间和代价降低的解来增加代理的利润,激励资源代理使用一种优化调度算法,使资源调度的时间和代价都最小。仿真结果表明了该模型的可行性和适用性,并且基于该模型的遗传算法是最好的资源调度算法。     

透明计算系统中基于BitMap的共享镜像存储管理

透明计算是一种将操作系统、应用程序和用户数据都作为资源存储在服务器端,资源以流块的方式调度到客户端执行的计算模式。针对透明计算系统中透明服务器端多用户的操作系统及应用程序资源冗余问题,设计并实现了一种基于BitMap的共享镜像存储管理方法BM-SISMS,该方法采用链式存储方法将系统数据和用户数据分离开来,多用户通过链式结构共享系统镜像,根据BitMap存储方法查找定位用户请求的各种资源。测试结果表明BM-SISMS方法能够完成多个客户端请求的实例操作系统加载和使用,在数据读写方面,BM-SISMS方法读数据速度约11.05 MB/s,写数据速度为4.01 MB/s,具有很高的性能,能够满足透明计算系统中镜像存储管理的需求。     

一种基于消息槽的K资源互斥算法

在分布式操作系统等一些有多个进程同时活跃的应用中,必须妥善解决不同进程对资源的需求,即同步与互斥问题.文章提出了一种基于消息槽的K资源互斥算法,介绍了该算法的原理,详细描述了该算法的运作过程,并进行了深入的分析.分析结果表明,该算法能够有效地满足K资源分布式环境下同步与互斥的要求.     

A task‐based and resource‐aware approach to dynamically generate optimal software architecture for intelligent service robots

Intelligent service robots provide various services to users by understanding the context and goals of a user task. In order to provide more reliable services, intelligent service robots need to consider various factors, such as their surrounding environments, users' changing needs, and constrained resources. To handle these factors, most of the intelligent service robots are controlled by a task‐based control system, which generates a task plan that represents a sequence of actions, and executes those actions by invoking the corresponding functions. However, the traditional task‐based control systems lack the consideration of resource factors even though intelligent service robots have limited resources (limited computational power, memory space, and network bandwidth). Moreover, system‐specific concerns such as the relationships among functional modules are not considered during the task generation phase. Without considering both the resource conditions and interdependencies among software modules as a whole, it will be difficult to efficiently manage the functionalities that are essential to provide core services to users. In this paper, we propose a mechanism for intelligent service robots to efficiently use their resources on‐demand by separating system‐specific information from task generation. We have defined a sub‐architecture that corresponds to each action of a task plan, and provides a way of using the limited resources by minimizing redundant software components and maintaining essential components for the current action. To support the optimization of resource consumption, we have developed a two‐phase optimization process, which is composed of the topological and temporal optimization steps. We have conducted an experiment with these mechanisms for an infotainment robot, and simulated the optimization process. Results show that our approach contributed to increase the utilization rate by 20% of the robot resources. Copyright © 2011 John Wiley & Sons, Ltd.     

基于任务特征的云计算资源分配策略

云计算中存在大量的服务资源,高效利用这些资源是资源分配的主要任务。通过对任务流的资源占用和执行过程建模分析,掌握任务流特性,提出伙伴关系任务流资源分配方案。多任务流执行过程中出现资源需求增减的时间同步,这种现象持续不断将产生波动共振,本文采用资源申请-仲裁机制来解决这一问题,把共振的任务流转变为伙伴关系任务流来协作使用资源,保持资源的高效利用。实验结果表明,本文算法消耗的资源比静态方式要少,溢出数也要少,实现了资源的充分利用,验证了算法的正确性。     

Multi-QoS constrained and Profit-aware scheduling approach for concurrent workflows on heterogeneous systems

The execution of a workflow application can result in an imbalanced workload among allocated processors, ultimately resulting in a waste of resources and a higher cost to the user. Here, we consider a dynamic resource management system in which processors are reserved not for a job but only to run a task, thus allowing a higher resource usage rate. This paper presents a scheduling algorithm that manages concurrent workflows in a dynamic environment in which jobs are submitted by users at any moment in time, on shared heterogeneous resources, and constrained to a specified budget and deadline for each job. Recent research attempted to propose dynamic strategies for concurrent workflows but only addressed fairness in resource sharing among applications while minimizing the execution time. The Multi-QoS Profit-Aware scheduling algorithm (MQ-PAS) proposed here is able to increase the profit achieved by the provider by considering the budget available for each job to define tasks priorities. We study the scalability of the algorithm with different types of workflows and infrastructures. The experimental results show that our strategy improves provider revenue significantly and obtains comparable successful rates of completed jobs.     

面向单任务质量保障的移动群智感知任务分配

在移动群智感知中,现有的任务分配方法大多关注平台的整体感知质量,未充分考虑任务对工人、预算等资源的竞争,无法有效保障大规模任务分配场景下每个任务的感知质量,从而导致平台资源利用率降低。针对该问题,提出一种面向单任务质量保障的任务分配方法。为高效利用平台预算,考虑任务的难度和位置以及工人的设备能耗和理性因素,设计平台的激励成本。为保障每个任务的感知质量,考虑任务间的资源竞争情况并设计2种衡量指标,分别是从任务的角度根据差异化感知质量需求设计任务覆盖效率,以及从工人的角度基于最大熵原理设计工人利用效率,将这2种衡量指标相结合作为平台的系统效用,在平台资源有限的情况下以平台系统效用最大化为优化目标,提出一种融合交叉和变异操作的天牛群（BSO）算法。实验结果表明,与PSO、GA等基线方法相比,BSO算法的系统效用最大值平均提升13.51%,寻优速度平均提高40.61%,利用该算法获取的具有最大系统效用的任务分配方案可以有效保障每个任务的感知质量。     

Reliability-aware low energy scheduling in real time systems with shared resources

Dynamic voltage scaling (DVS) is a technique which is widely used to save energy in a real time system. Recent research shows that it has a negative impact on the system reliability. In this paper, we consider the problem of the system reliability and focus on a periodic task set that the task instance shares resources. Firstly, we present a static low power scheduling algorithm for periodic tasks with shared resources called SLPSR which ignores the system reliability. Secondly, we prove that the problem of the reliability-aware low power scheduling for periodic tasks with shared resources is NP-hard and present two heuristic algorithms called SPF and LPF respectively. Finally, we present a dynamic low power scheduling algorithm for periodic tasks with shared resources called DLPSR to reclaim the dynamic slack time to save energy while preserving the system reliability. Experimental results show that the presented algorithm can reduce the energy consumption while improving the system reliability.     

铁路应急资源动态多阶段调度决策模型及算法研究

近年来,铁路突发事件时有发生,严重影响铁路的正常运营,合理地进行应急资源的调度是提高铁路整体应急救援能力,减少突发事件所造成损失的有效途径。以博弈论为理论基础,将各应急点看作博弈局中人,考虑救援点到应急点的运力限制以及不同资源在不同应急点的重要度等因素,构建了资源动态需求函数,并用应急点对资源缺少量的时间累积来刻画系统损失。将多应急点的资源调度描述为一个多阶段非合作博弈过程,以系统总损失最小为目标,建立多应急点-多救援点-多种资源的动态多阶段资源调度模型,并设计了求解该模型Nash均衡的改进布谷鸟算法,从而得到最优的铁路应急资源调度方案。通过具体算例验证了模型的可行性与算法的优越性。结果表明该模型较为切近实际、适用性较强且改进后的算法更具高效性,可为铁路应急资源调度决策提供依据和支持。     

Video registration using dynamic textures

We consider the problem of spatially and temporally registering multiple video sequences of dynamical scenes which contain, but are not limited to, nonrigid objects such as fireworks, flags fluttering in the wind, etc., taken from different vantage points. This problem is extremely challenging due to the presence of complex variations in the appearance of such dynamic scenes. In this paper, we propose a simple algorithm for matching such complex scenes. Our algorithm does not require the cameras to be synchronized, and is not based on frame-by-frame or volume-by-volume registration. Instead, we model each video as the output of a linear dynamical system and transform the task of registering the video sequences to that of registering the parameters of the corresponding dynamical models. As these parameters are not uniquely defined, one cannot directly compare them to perform registration. We resolve these ambiguities by jointly identifying the parameters from multiple video sequences, and converting the identified parameters to a canonical form. This reduces the video registration problem to a multiple image registration problem, which can be efficiently solved using existing image matching techniques. We test our algorithm on a wide variety of challenging video sequences and show that it matches the performance of significantly more computationally expensive existing methods.     

Simultaneous resource portfolio planning under demand and technology uncertainty in the semiconductor testing industry

One of the most challenging issues for the semiconductor testing industry is how to deal with capacity planning and resource allocation simultaneously under demand and technology uncertainty. In addition, capacity planners require a tradeoff among the costs of resources with different processing technologies, while simultaneously considering resources to manufacture products. The need for exploring better solutions further increases the complexity of the problem. This study focuses on the decisions pertaining to (i) the simultaneous resource portfolio/investment and allocation plan accounting for the hedging tradeoff between the expected profit and risk, (ii) the most profitable orders from pending ones in each time bucket under demand and technology uncertainty, (iii) the algorithm to efficiently solve the stochastic and mixed integer programming problem. Due to the high computational complexity of the problem, this study develops a constraint-satisfaction based genetic algorithm, in conjunction with a chromosome-repair mechanism and sampling procedure, to resolve the above issues simultaneously. The experimental results indicate that the proposed mathematical model can accurately represent the resource portfolio planning problem of the semiconductor testing industry, and the solution algorithm can solve the problem efficiently.     

Simple and integrated heuristic algorithms for scheduling tasks with time and resource constraints

We consider the problem of scheduling a set of n tasks in a system having r resources. Each task has an arbitrary, but known, processing time and a deadline, and may request use of a number of resources. A resource can be used either in shared mode or exclusive mode. In this article, we study algorithms used for determining whether or not a set of tasks is schedulable in such a system, and if so, determining a schedule for it. This scheduling problem is known to be NP-complete and hence we methodically study a set of heuristics that can be used by such an algorithm. Due to the complexity of the problem, simple heuristics do not perform satisfactorily. However, an algorithm that uses combinations of these simple heuristics works very well compared to an optimal algorithm that takes exponential time complexity. For the combination that performs the best, we also determine the scheduling costs as a function of the size of the task set scheduled.     

基于网格的两级动态负载平衡算法

网格系统具有异构性、动态性和分布性的特点,且资源数量巨大,这使得网格中的任务调度十分复杂.针对网格的特点,在两级树型网格结构的基础上,设计了一种基于该结构的两级负载平衡算法,针对传统的负载平衡算法考虑资源因素单一,难以满足复杂的网格环境的要求,该算法使用了多种负载参数来衡量网格节点的负载状况.