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。     

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。     

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.     

基于多目标优先级粒子群算法的资源调度策略

移动边缘云计算是5G技术的核心之一,也是当下非常热门的通信技术。但当前移动用户数量迅猛增长,传统资源分配方式已不能满足用户需求,因此根据用户的规模及其任务优先级的实时变化,如何合理制定资源分配策略来满足用户对计算单元、存储空间、软件等资源的需求是当下十分热门的研究方向。该文提出了一种基于多目标优先级粒子群算法的边缘云资源调度算法(MPPSO),合理布局多个边缘基站,形成边缘云。在多用户多任务并发时,综合用户数据传输速率、任务能耗、任务优先级和边缘基站性能等多方面因素,设计了两个适应度函数和一种粒子编解码方法,同时引入了帕累托控制机制,协助策略搜索多目标优先级最优解,为边缘云提供最优的资源调度策略,便于实时满足不同用户不同任务的资源需要,不仅使边缘云资源得到了充分利用,也大大提高了用户的使用体验。最后通过实验验证了该算法的有效性。     

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。     

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.     

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。     

信任驱动的云联盟博弈算法

为了解决云联盟中云资源提供者间的信任问题,提出一种基于信任机制的云联盟算法。算法将多个云资源提供者以合作形式完成用户任务的问题形式化为联盟博弈模型,在建立联盟时兼顾考虑成员间的信任关系及成员收益,使得具有更高信任度的资源提供者能够建立联盟以降低任务执行代价,并确保参与联盟的个体成员收益最大化。同时,证明了该算法求解的联盟结构是稳定的,并且满足Pareto最优性质。实验结果验证了算法的有效性和可行性。     

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.     

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.     

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.     

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.     

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.     

基于QMix的车辆云计算资源动态分配方法

城市交通智能化和通信技术的进步会产生大量基于车辆的应用,但目前车辆有限的计算资源无法满足车辆应用的计算需求与延迟性约束。车辆云（VC）可以高效地调度资源,从而显著降低任务请求的延迟与传输成本。针对VC环境下任务卸载与计算资源分配问题,提出一个考虑异质车辆和异质任务的计计资源分配算法。对到达的任务构建M/M/1队列模型与计算模型,并定义一个效用函数以最大化系统整体效用。针对环境中车辆地理分布的高度动态系统变化,提出基于双时间尺度的二次资源分配机制（SRA）,使用两个不同时间尺度的资源分配决策动作,对其分别构建部分可观测马尔可夫决策过程。两个决策动作通过执行各自的策略获得的奖励进行连接,将问题建模为两层计算资源分配问题。在此基础上提出基于二次资源分配机制的多智能体算法SRA-QMix求解最优策略。仿真结果表明,与深度确定性策略梯度算法对比,该算法的整体效用值和任务完成率分别提高了70%、6%,对于QMix和MADDPG算法分别应用SRA后的任务完成率分别提高了13%与15%,可适用于动态的计算资源分配环境。     

An algorithm for cooperative task allocation in scalable,constrained multiple robot systems

The current trends in the robotics field have led to the development of large-scale multiple robot systems, and they are deployed for complex missions. The robots in the system can communicate and interact with each other for resource sharing and task processing. Many of such systems fail despite the availability of necessary resources. The major reason for this is their poor coordination mechanism. Task planning, which involves task decomposition and task allocation, is paramount in the design of coordination and cooperation strategies of multiple robot systems. Task allocation mechanism allocates the task in a mission to the robots by maximizing the overall expected performance, and thereby reducing the total allocation cost for the team. In this paper, we formulate a heuristic search-based task allocation algorithm for the task processing in heterogeneous multiple robot system, by maximizing the efficiency in terms of both communication and processing cost. We assume a set of decomposed tasks of a mission, which needs to be allocated to the robots. The near-optimal allocation schemes are found using the proposed peer structure algorithm for the given problem, where the number of the tasks is more than the robots present in the system. The cost function is the summation of static overhead cost of robots, assignment cost, and the communication cost between the dependent tasks, if they are assigned to different robots. Experiments are performed to verify the effectiveness of the algorithm by comparing it with the existing methods in terms of computational time and quality of solution. The experimental results show that the proposed algorithm performs the best under different problem scales. This proves that the algorithm can be scaled for larger system and it can work for dynamic multiple robot system.     

A fault-tolerant dynamic scheduling algorithm for multiprocessorreal-time systems and its analysis

Many time-critical applications require dynamic scheduling with predictable performance. Tasks corresponding to these applications have deadlines to be met despite the presence of faults. In this paper, we propose an algorithm to dynamically schedule arriving real-time tasks with resource and fault-tolerant requirements on to multiprocessor systems. The tasks are assumed to be nonpreemptable and each task has two copies (versions) which are mutually excluded in space, as well as in time in the schedule, to handle permanent processor failures and to obtain better performance, respectively. Our algorithm can tolerate more than one fault at a time, and employs performance improving techniques such as 1) distance concept which decides the relative position of the two copies of a task in the task queue, 2) flexible backup overloading, which introduces a trade-off between degree of fault tolerance and performance, and 3) resource reclaiming, which reclaims resources both from deallocated backups and early completing tasks. We quantify, through simulation studies, the effectiveness of each of these techniques in improving the guarantee ratio, which is defined as the percentage of total tasks, arrived in the system, whose deadlines are met. Also, we compare through simulation studies the performance our algorithm with a best known algorithm for the problem, and show analytically the importance of distance parameter in fault-tolerant dynamic scheduling in multiprocessor real-time systems