地震减灾科学计算网格系统中任务分配算法研究

优化计算时间是网格计算的主要目标，其中网格计算的任务分配算法是解决计算优化的有效方法，文章建立了一套适合地震震源破裂模型的计算任务分配算法，从而很好地解决了如何将原有的地震破裂过程计算模型改造成网格化的地震破裂过程计算模型。     

基于蚂蚁算法的网格任务分配算法研究

任务分配是网格计算环境中影响系统性能的重要因素，由于网格环境中各种资源分布于不同的地理位置，其性能也千差万别，因此需要一种有效的策略来进行任务的分配，使得整个系统完成任务的代价最小。文中将蚂蚁算法应用于解决网格环境中的任务分配问题，并进行了仿真实验，取得了良好的效果。     

基于蚂蚁算法的网格任务分配算法研究

任务分配是网格计算环境中影响系统性能的重要因素,由于网格环境中各种资源分布于不同的地理位置,其性能也千差万别,因此需要一种有效的策略来进行任务的分配,使得整个系统完成任务的代价最小。文中将蚂蚁算法应用于解决网格环境中的任务分配问题,并进行了仿真实验,取得了良好的效果。     

网格计算与对等计算的比较研究

对网格计算和对等计算从概念、目标、体系结构、组成团体、资源、安全需求、应用、基础架构和特征等方面进行了深入比较，总结了各自的特点，最后对二者的研究方向进行展望．     

多分辨率网格的数据压缩

在对三角形网格多分辨率分析中,为了避免重新网格化的过程,基于小波变换,扩展了Lounsbery的方法。该算法直接对不规则网格进行渐进压缩,得到了不同分辨率的网格。在此过程中还可以基于三角形网格的连接信息,对三角形网格进行优化,使之更加规则,从而使本文算法得到了改善。实验结果表明,算法速度快,效果良好,有一定的实用性。     

密码计算网格的计算节点控制系统设计与实现

密码计算需要强大的计算能力,而网格计算作为一种新型有效的计算技术,能广泛集合网络上的资源形成一台虚拟的超级计算机.因此,把网格技术应用到密码学计算中具有重大的意义.介绍了一种密码计算网格系统的框架设计,并对其中的计算节点控制管理模块进行了详细设计和实现.此系统在DES穷举攻击实验中取得了非常好的效果,其它一些密码计算任务类似地也可用此系统分布式地实现.     

引人注目的网格计算

所谓网格计算 ,是使分散在不同地方的多台电脑像一台电脑一样进行运算的一种新型的计算形式 ,该技术从 2 0 0 1年底开始引起人们广泛的关注。康柏网格支持中心通过利用高速网络 ,连接位于大阪市和筑波市的事业所和位于北美、法国以及爱尔兰的中心 ,实现了全球规模的网格计算环境。网格计算是一种通过连接无数台服务器创建巨大的虚拟超级电脑的技术 ,该技术目前正在科学技术计算以及需要进行复杂处理的领域获得利用。网格计算技术有望成为数据挖掘(DataMining)以及主干类业务处理等商业计算市场中的下一代主要技术。例如 ,如果连接到网络 ,…     

云计算与网格计算

网格计算与云计算是为了解决单一计算机的计算极限问题而产生的分布式计算技术。本文首先介绍了云计算和网格计算,然后比较了云计算和网格计算之间的异同。     

网格计算任务的资源管理算法

计算任务管理贯穿于整个网格计算的全生命周期,目标是运用既有规律又经济的方法对计算进行高效率的计划、组织、指导和控制,实现时间、费用和技术效果上的动态优化。该文对计算任务的资源管理进行研究,对资源管理指标进行形式化分析,对计算时耗、成本和资源平衡这3个目标进行综合决策,提出了一种网格计算任务的资源管理算法。仿真实验证明算法是可行有效的。     

传感器网络基于Voronoi 网格的数据压缩算法

提出了一种传感器网络中分布式多分辨率数据压缩算法.在分布式域剖分模型DDPM(distributed domain partition model)基础上,提出了一种多分辨率的数据压缩模型MDCM(multiscale data compress model),DDPM 把传感器网络按域划分来构建连通核,传感节点只需在连通核中寻径,因而能够明显减少寻径时间复杂度并且具有更好的分布性;MDCM 利用Voronoi 网格来对DDPM所形成的域中的节点进行划分,然后采用多分辨率方法构建数据压缩模型.理论分析和实验仿真结果表明,MDCM 具有很好的逼近性能,并且能够对传感器网络中的数据进行有效压缩,可以更大程度地降低传感器网络中的数据传输量.     

An adaptive multi-policy grid service for biological sequence comparison

In the last decade, we have observed an unprecedented development in molecular biology. An extremely high number of organisms have been sequenced in genome projects and included in genomic databases, for further analysis. These databases present an exponential growth rate and they are intensively accessed daily, all over the world. Once a sequence is obtained, its function and/or structure must be determined. Direct experimentation is considered to be the most reliable method to do that. However, the experiments that must be conducted are very complex and time consuming. For this reason, it is far more productive to use computational methods to infer biological information from a sequence. This is usually done by comparing the new sequence with sequences that already had their characteristics determined. BLAST is the most widely used heuristic tool for sequence comparison. Thousands of BLAST searches are made daily, all over the world. In order to further reduce the BLAST execution time, cluster and grid environments can be effectively used. This paper proposes and evaluates an adaptive task allocation framework to perform BLAST searches in a grid environment. The framework, called PackageBLAST, provides an infrastructure that executes distributed BLAST genomic database comparisons. In addition, it is flexible since the user can choose or incorporate new task allocation strategies. Furthermore, we propose a mechanism to compute grid nodes’ execution weight, adapting the chosen allocation policy to the observed computational power and local load of the nodes. Our results present very good speedups. For instance, in a 16-machine heterogeneous grid testbed, a speedup of 14.59 was achieved, reducing the BLAST execution time from 30.88 min to 2.11 min. Also, we show that the adaptive task allocation strategy was able to handle successfully the complexity of a grid environment.     

Towards a hybrid load balancing policy in grid computing system

Grid computing has become conventional in distributed systems due to technological advancements and network popularity. Grid computing facilitates distributed applications by integrating available idle network computing resources into formidable computing power. As a result, by using efficient integration and sharing of resources, this enables abundant computing resources to solve complicated problems that a single machine cannot manage. However, grid computing mines resources from accessible idle nodes and node accessibility varies with time. A node that is currently idle, may become occupied within a second of time and then be unavailable to provide resources. Accordingly, node selection must provide effective and sufficient resources over a long period to allow load assignment. This study proposes a hybrid load balancing policy to integrate static and dynamic load balancing technologies. Essentially, a static load balancing policy is applied to select effective and suitable node sets. This will lower the unbalanced load probability caused by assigning tasks to ineffective nodes. When a node reveals the possible inability to continue providing resources, the dynamic load balancing policy will determine whether the node in question is ineffective to provide load assignment. The system will then obtain a new replacement node within a short time, to maintain system execution performance.     

Can Economics-based Resource Allocation Prove Effective in a Computation Marketplace?

Several companies offer computation on demand for a fee. More companies are expected to enter this business over the next decade, leading to a marketplace for computation resources. Resources will be allocated through economic mechanisms that establish the relative values of providers and customers. Society at large should benefit from discoveries obtained through the vast computing power that will become available. Given such a computation marketplace, can economics-based resource allocation provide benefits for providers, customers and society? To investigate this question, we simulate a Grid economy where individual providers and customers pursue their own ends and we measure resulting effects on system welfare. In our experiments, customers attempt to maximize their individual utilities, while providers pursue strategies chosen from three classes: information-free, utilization-based and economics-based. We find that, during periods of excess demand, economics-based strategies yield overall resource allocation that benefits system welfare. Further, economics-based strategies respond well to sudden overloads caused by temporary provider failures. During periods of moderate demand, we find that economics-based strategies provide ample system welfare, comparable with that of utilization-based strategies. We also identify and discuss key factors that arise when using economic mechanisms to allocate resources in a computation marketplace.     

网格计算技术及应用综述

Grid computing is a new kind of distributed computing technology and computing environment ,and also an attentive hot point of information technology in the world. It launches a space for new generation internal application,due to its supporting the complicated service efficiently and useable resource in the Internet. This paper sums up the results and applications instance in the grid computing in the world during recent years ,and then analyzes emphatically the system architecture ,components ,working principle in the grid computing and some typical grid systems ,and then discusses the problems of the large scale science computing and network service in grid computing in China,and alsopoints out the future trends.     

分布式容错系统的任务分配算法

文章提出了分布式容错系统的任务分配算法,该算法考虑了系统任务的周期性、冗余性特点,以处理机负载平衡为目标,通过三步静态分配实现了任务在处理机中的冗余分布,在系统执行过程中的处理机故障,通过冗余任务动态唤醒实现系统重构。     

网格计算环境中可视化技术的研究

Visualization research is one of the critical techniques in the Grid computing.In this paper a visual method is used to implement a distributed parallel computing program which makes the node point‘‘s status of the parallel system visible during the program‘‘s running time.At the end of the paper,the implementation of the visiual algorithm is given.     

网格并行计算模型研究

分析了传统分布式并行计算和网格基础上并行计算技术应用中存在的问题,将Log P并行计算模型拓展到网格上,给出双层Log P模型和设计策略,针对网格特点对CG并行算法进行了改进,取得了很好的效果.     

用于手写汉字识别的分布式平台研究与实现

手写汉字识别实验样本量巨大,使得实验无法在有限时间内在单机上运行完成。而模式识别实验需要利用已有的并非为分布式实验而设计的工具,使得其利用当前的分布式中间件模型比较复杂。本文设计并实现了一种轻量级的用于模式识别实验的分布式平台——HWDC。它将网络上的多台计算资源及存储资源整合为一体,提高实验速度。使用TML对任务进行描述,以使得已有的工具可不加修改的部署到该平台上。本文研究了该平台的架构、调度策略、错误处理等。     

基于Scilab的分布式并行计算方法的研究

文章利用Scilab构建分布并行计算的支撑环境,为实现分布并行计算提供了新方法。设计了Netbutterfly Grid Computing System（NGCS）,讨论了NGCS的体系结构,任务分布、系统通信和容错机制等关键问题。最后给出网格计算环境下基于Scilab的一个分布并行计算实例,实验的结果是令人满意的。     

An effective iterated greedy algorithm for reliability-oriented task allocation in distributed computing systems

This paper investigates the problem of allocating parallel application tasks to processors in heterogeneous distributed computing systems with the goal of maximizing the system reliability. The problem of finding an optimal task allocation for more than three processors is known to be NP-hard in the strong sense. To deal with this challenging problem, we propose a simple and effective iterative greedy algorithm to find the best possible solution within a reasonable amount of computation time. The algorithm first uses a constructive heuristic to obtain an initial assignment and iteratively improves it in a greedy way. We study the performance of the proposed algorithm over a wide range of parameters including problem size, the ratio of average communication time to average computation time, and task interaction density. The viability and effectiveness of our algorithm is demonstrated by comparing it with recently proposed task allocation algorithms for maximizing system reliability available in the literature.