改进GAs算濡大规模资源分配问题中的应用

采用改进ＧＡｓ算法建立了求解大规模规划的资源分配模型，针对大规模资源分配问题的具体特点，设计了合适的ＧＡｓ算子，并以实全我验证了处工法的合理性及有效性。     

TS与GAs混合算法在大规模资源分配问题中的应用

采用ＴＳ与ＧＡｓ混合算法建立资源分配模型来分解协调大规模规划问题。针对大规模资源分配问题的特点，选择合适的ＴＳ参数及ＧＡｓ算子，构造出一种新的混合算法。具体实例验证了算法的合理性及有效性。     

电脑文摘

９９０６８一种基于场编程门阵列的协处理系统用ＰＣＩ接口的设计与实现／／ＭｉｃｒｏｐｒｏｃｅｓｓｏｒｓａｎｄＭｉｃｒｏｓｙｓｔｅｍｓ．—１９９９,２２（７）．—３７３～３８８本文介绍了一种适用于通用并行神经计算机（其宿主机为个人计算机）的基于场编程门陈列（ＦＰＧＡ）的ＰＣＩ总线接口的设计方法和实现途径。给出了ＰＣＩ总线的发展背景,详细讨论了ＦＰＧＡ可行性设计方法。在采用ＦＰＧＡ的情况下,最重要的是状态机（ｓｔａｔｅｍａｃｈｉｎｅｓ）的运行速度必须足够快且资源分配必须有效和合理。鉴于此,文章特别强调…     

解决一类遗传算法早熟收敛的混合法及其推广

本文分析了变型标准遗传算法ＶＣＧＡ（ｖａｒｉａｎｔｓｏｆｃａｎｏｎｉｃａｌｇｅｎｅｔｉｃａｌｇｏｒｉｔｈｍｓ）有时会产生早熟收敛的机理，提出了混合法ＨＶＣＳＤＡ（ｈｙｂｒｉｄＶＣＧＡｃｏｍｂｉｎｅｄｗｉｔｈｓｔｅｅｐｅｓｔｄｅｓｃｅｎｔａｐｐｒｏａｃｈ），并进行了推广．该方法可使最优保存的超级个体时间序列离开早熟收敛状态而继续接近全局最优解．仿真实例表明了本文算法的有效性．在３０城市ＴＳＰ（ｔｒａｖｅｌｉｎｇｓａｌｅｓｍａｎｐｒｏｂｌｅｍ）的基准测试问题中，本文得到了路径为６．８２的结果，它好于用新的现代的启发式搜索方法——ＴＡＢＵ搜索法得到的６．９９的结果．     

关于并行FFT算法

关于并行ＦＦＴ算法魏公毅，蔡中善（中国科学院计算中心）ＯＮＰＡＲＡＬＬＥＬＦＦＴＡＬＧＯＲＩＴＨＭＳ￥ＷｅｉＧｏｎｇ－ｙｉ；ＣａｉＺｈｏｎｇ－ｓｈａｎ（ＣｏｍｐｕｔｉｎｇＣｅｎｔｅｒ．ＡｃａｄｅｍｉａＳｉｎｉｃａ）Ａｂｓｔｒａｃｔ：Ｔｈｅｐａｒａｌｌ...     

“Algorithms for Degree-Raising of Splines”中的问题及其解决办法

研究表明，Ｃｏｈｅｎ，Ｌｙｃｈｅ和Ｓｃｈｕｍａｋｅｒ于１９８５年在ＡＣＭＴｒａｎｓａｃｔｉｏｎｓｏｎＧｒａｐｈｉｃｓ杂志上所发表的论文“ＡｌｇｏｒｉｔｈｍｓｆｏｒＤｅｇｒｅｅ－ＲａｉｓｉｎｇｏｆＳｐｌｉｎｅｓ”中存在着严重问题．文中给出了解决Ｂ样条曲线升阶的经典理论中的这些问题的三个算法，同时也给出了计算实例．     

统计遗传算法

本文讨论了遗传算法中框架定理的不足之处，并对之进行了改进，然后分析了遗传算法与Ａ算法的相似性，以及遗传算法的概率性质．由此联想到它与ＳＡ算法的相似性，在此基础上，作者将原先发展的一套ＳＡ算法的理论移植到遗传算法中来，建立一个新的算法，称之为统计遗传算法（简记为ＳＧＡ算法）．为适合于优化计算，作者引入最大值统计量及其对应的ＳＡ算法（简称为ＳＭＡ算法），并将ＳＭＡ算法与ＧＡ算法相结合（记为ＳＧＡ（ＭＡＸ）算法）．新的算法不仅提高了算法的精度和降低了计算的复杂性，而且能克服ＧＡ算法中出现“早熟”的现象以及提供进行并行计算的可能性．更主要的是新的方法为ＧＡ算法的精度、可信度和计算复杂性的定量分析提供了理论和方法上的有力工具.     

函数图形发生器FGG的设计与实现

介绍一个在Ｗｉｎｄｏｗｓ环境下，用ＢｏｒｌａｎｄＣ＾＋＋３．１开发的通用函数图形发生器的结构和功能，讨论了实现中的表达式求值算法和图形生成算法。ＦＧＧ的主要特点是，对输入的任意一元、二元函数表达式可直接生成对应的二维和三维图形。ＦＧＧ作为ＣＡＩ开发工具的支撑软件，可用于ＣＡＩ课件的开发。     

光电子多芯片组件中PE布局的优化算法

提出了一种新型的适用于布局问题的二维基因算法２ＤＧＡ（２Ｄ Ｇｅｎｅｔｉｃ Ａｌｇｏｒｉｔｈｍｓ），并用该算法实现对ｔｗｉｎ－ｂｕｔｔｅｒｆｌｙ并行多级互连网络中心处理单元的布局，实验结果表明，该算法优于现有的其它方法。     

STGA的变种及其互模拟验证

为刻画和验证无穷值域上的传值进程，Ｈｅｎｎｅｓｓｙ和Ｌｉｎ先后提出符号迁移图（ＳＴＧ）和带赋值符号迁移图（ＳＴＧＡ）作为传值进程的语义表示模型，并给出了相应的强互模拟算法．为将该方法推广至实际应用中更常用的弱互模拟等价和观察同余的验证问题，该文首先引入了ＳＴＧＡ的一个变种，它与原模型的不同之处在于将符号迁移上赋值和符号动作的执行次序颠倒，因而可定义些种ＳＴＧＡ结果间的符号双迁移关系．文中提出了从正     

面向大规模云资源调度的可扩展分布式调度方法

云数据中心异构物理服务器的能耗优化资源分配问题是NP难的组合优化问题,当资源分配问题规模较大时,求解的空间比较大,很难在合理时间内求得最优解。基于分而治之的思想,从调度模式方面提出可扩展分布式调度方法,即当云数据中心待调度的物理服务器的数量比较大时,将待调度的服务器划分为若干个服务器集群,然后在每个服务器集群建立能耗优化的资源分配模型,并利用约束编程框架Choco求解模型,获得能耗最优的资源分配方式。将提出的基于可扩展分布式调度方法的能耗优化云资源调度算法与非扩展调度算法进行实验比较,实验结果表明,提出的基于可扩展分布式调度方法的能耗优化云资源调度算法在大规模云资源分配上有明显的性能优势。     

Self-Adaptive Resource Management for Large-Scale Shared Clusters

In a shared cluster, each application runs on a subset of nodes and these subsets can overlap with one another. Resource management in such a cluster should adaptively change the application placement and workload assignment to satisfy the dynamic applications workloads and optimize the resource usage. This becomes a challenging problem with the cluster scale and application amount growing large. This paper proposes a novel self-adaptive resource management approach which is inspired from human market: the nodes trade their shares of applications' requests with others via auction and bidding to decide its own resource allocation and a global high-quality resource allocation is achieved as an emergent collective behavior of the market. Experimental results show that the proposed approach can ensure quick responsiveness, high scalability, and application prioritization in addition to managing the resources effectively.     

Cooperative game-based distributed resource allocation in horizontal dynamic cloud federation platform

Distributed resource allocation is a very important and complex problem in emerging horizontal dynamic cloud federation (HDCF) platforms, where different cloud providers (CPs) collaborate dynamically to gain economies of scale and enlargements of their virtual machine (VM) infrastructure capabilities in order to meet consumer requirements. HDCF platforms differ from the existing vertical supply chain federation (VSCF) models in terms of establishing federation and dynamic pricing. There is a need to develop algorithms that can capture this complexity and easily solve distributed VM resource allocation problem in a HDCF platform. In this paper, we propose a cooperative game-theoretic solution that is mutually beneficial to the CPs. It is shown that in non-cooperative environment, the optimal aggregated benefit received by the CPs is not guaranteed. We study two utility maximizing cooperative resource allocation games in a HDCF environment. We use price-based resource allocation strategy and present both centralized and distributed algorithms to find optimal solutions to these games. Various simulations were carried out to verify the proposed algorithms. The simulation results demonstrate that the algorithms are effective, showing robust performance for resource allocation and requiring minimal computation time.     

一种启发式网络虚拟化资源分配算法

网络虚拟化是克服当前Internet僵化问题的一种重要方法,而资源分配是网络虚拟化技术的核心.为了平衡负载,本文提出了一种启发式资源分配算法HVNE.该算法充分利用虚拟节点和虚拟链路间的关联因素(虚拟网络拓扑),将节点映射和链路映射两个过程合并为一个统一的过程,改善了传统映射算法在拓扑稀疏时,算法性能不理想的问题.此外,HVNE允许同一个虚拟请求中的多个虚拟节点映射到同一个物理节点,节约了物理链路资源.HVNE将无向图的"k-区域划分优化"理论与传统的拓扑分割理论相结合,定义了虚拟拓扑间节点的关联因子,改进了传统的星形分割方法,使之能适用于大规模网络.仿真实验表明,HVNE在保证网络负载的情况下,获得了较好的虚拟请求接受率,较高的资源利用率和网络收益.     

移动边缘计算环境下的动态资源分配策略

在通讯设备爆炸式增长的时代,移动边缘计算作为5G通讯技术的核心技术之一,对其进行合理的资源分配显得尤为重要。移动边缘计算的思想是把云计算中心下沉到基站部署(边缘云),使云计算中心更加靠近用户,以快速解决计算资源分配问题。但是,相对于大型的云计算中心,边缘云的计算资源有限,传统的虚拟机分配方式不足以灵活应对边缘云的计算资源分配问题。为解决此问题,提出一种根据用户综合需求变化的动态计算资源和频谱分配算法(DRFAA),采用"分治"策略,并将资源模拟成"流体"资源进行分配,以寻求较大的吞吐量和较低的传输时延。实验仿真结果显示,动态计算资源和频谱分配算法可以有效地降低用户与边缘云之间的传输时延,也可以提高边缘云的吞吐量。     

异步共享存储器互斥算法的研究与实现

异步共享存储器是分布式计算系统的一个应用,它的出现使大规模的科学计算和存储成为可能.异步共享存储器算法要解决的前提问题是异步进程的互斥,资源的分配,一致性和原子对象.进程间的互斥保证各个进程访问单个非共享资源时的正确性和完整性.一致性则要分析故障可能性这个复杂的问题.原子对象指资源被几个不同的进程同时访问而不发生错误.对上述问题进行了分析,在此基础上重点讨论了几种互斥算法,并且在SunOS 5.9 Unix系统下模拟实现了互斥算法.     

Towards an adaptive human-centric computing resource management framework based on resource prediction and multi-objective genetic algorithm

The complexity, scale and dynamic of data source in the human-centric computing bring great challenges to maintainers. It is problem to be solved that how to reduce manual intervention in large scale human-centric computing, such as cloud computing resource management so that system can automatically manage according to configuration strategies. To address the problem, a resource management framework based on resource prediction and multi-objective optimization genetic algorithm resource allocation (RPMGA-RMF) was proposed. It searches for optimal load cluster as training sample based on load similarity. The neural network (NN) algorithm was used to predict resource load. Meanwhile, the model also built virtual machine migration request in accordance with obtained predicted load value. The multi-objective genetic algorithm (GA) based on hybrid group encoding algorithm was introduced for virtual machine (VM) resource management, so as to provide optimal VM migration strategy, thus achieving adaptive optimization configuration management of resource. Experimental resource based on CloudSim platform shows that the RPMGA-RMF can decrease VM migration times while reduce physical node simultaneously. The system energy consumption can be reduced and load balancing can be achieved either.     

A family of incentive-compatible and non-subsidizing optimal resource allocation problems

This paper provides a structure unifying several recent results on an incentive optimal resource allocation problem. They include an incentive-compatible modification of the Heal Algorithm, a generalization of it allowing the formation of secret coalitions and a class of center-free resource allocation algorithms as special cases. Our framework encompasses these earlier results and allows us to characterize a large class of incentive-compatible and non-subsidizing optimal resource allocation algorithms.     

Resource Allocation in the Grid with Learning Agents

One of the main challenges in Grid computing is efficient allocation of resources (CPU – hours, network bandwidth, etc.) to the tasks submitted by users. Due to the lack of centralized control and the dynamic/stochastic nature of resource availability, any successful allocation mechanism should be highly distributed and robust to the changes in the Grid environment. Moreover, it is desirable to have an allocation mechanism that does not rely on the availability of coherent global information. In this paper we examine a simple algorithm for distributed resource allocation in a simplified Grid-like environment that meets the above requirements. Our system consists of a large number of heterogenous reinforcement learning agents that share common resources for their computational needs. There is no explicit communication or interaction between the agents: the only information that agents receive is the expected response time of a job it submitted to a particular resource, which serves as a reinforcement signal for the agent. The results of our experiments suggest that even simple reinforcement learning can indeed be used to achieve load balanced resource allocation in large scale heterogenous system.     

A Pricing Mechanism for Resource Management in Grid Computing

We consider the problem of efficient resource allocation in a grid computing environment. Grid computing is an emerging paradigm that allows the sharing of a large number of a heterogeneous set of resources. We propose an auction mechanism for decentralized resource allocation. The problem is modeled as a multistage stochastic programming problem. Convergence of the auction allocations to the social optimum is established. Numerical experiments illustrate the efficacy of the method.