机群代理的单一缓存映像技术

为了在Web机群代理中有效定位缓存对象在节点机中的位置，必须实现单一缓存映像，以隐藏机群的分布和异构特性，为客户提供一致的缓存资源。为解决该问题，文中首先提出一个缓存摘要管理器体系结构，该结构基于各个缓存节点机的摘要、结合负载平衡信息，可有效定位缓存节点机，然后给出了机群代理中缓存节点机与缓存摘要管理器的实现方法。     

曙光机群资源管理的设计与实现

机群资源管理负责机群系统内计算所需资源的管理，该文介绍机群资源管理的主要功能和重要的研究问题，着重介绍曙光3000机群系统的资源管理的设计与实现，包括分区管理，任务管理和通信管理功能及其对高可用性，可扩展性，动态特性的支持；并给出相应的性能分析结果，此外，还介绍了相关系统的资源管理和对未来该领域研究的方法。     

机群并行系统的设计--机群并行系统设计中需考虑的因素

机群并行计算机成为并行计算研究的热点。本文介绍了在设计机群并行系统时需要考虑的几点因素。     

单一系统映象在机群管理中的实现

单一系统映象是机群系统的关键技术。文章讨论了机群单一系统映象的含义,提出了一种用于并行计算机群管理的单一系统映象模型,实现了机群单一控制和单一作业管理,解决了机群系统难于管理、管理方式可视化不强等问题。     

基于SSI的远程集群管理系统

受集群系统结构的固有特性的影响,集群系统的管理问题日益突出。早期集群系统通过命令行方式进行管理,存在功能不完善、结构单一、可用性差、不支持远程管理等缺点。该文分析了集群管理软件的功能需求和相关技术,设计和实现了一套基于SSI的远程集群管理系统。该系统采用标准化模块设计方法,其功能可灵活组态,扩展性较好,并实现比较完整的单一系统映像,可提供简单、高效的管理功能。对系统进行了测试和评价,并提出该系统未来的研究方向。     

计算机机群系统分析

计算机系统具有可以同时进行运算或操作的特性,它包括同时性和并发性,计算机系统的机群系统由一组完整的分布在不同地理位置的计算机系统通过高性能的专业网络或局域网系统互连而成的系统,是并行计算机系统或计算机网络系统的一种类型,它作为一个单独的统一计算机资源来使用,既可以做科学运算,也可供商业运用。     

基于PBS的机群任务调度管理中间件的设计与实现

介绍了基于PBS的机群任务调度管理中间件，是基于现在比较流行的调度系统PBS进行开发和封装，对上层提供了统一的接口．同时在该中间件中还封装了用户管理模块、配置模块和任务调度模块，采用基于XML的通信协议．该中间件具有很强的扩展性，可以扩展应用于LSF等多种流行的机群任务调度系统．最后，在该中间件之上，使用Java开发了前台图形界面，实现了一整套完备的具有实时监控和远程管理能力的机群任务管理系统．     

路面施工机械机群智能控制系统

针对国内路面施工的现状和存在的问题，结合施工机械本身的特点，对施工机械智能机群系统的控制结构及通信方式进行了研究，给出了基于无线局域网的施工机械智能机群系统的整体结构框架，对单个机械重点介绍了如何使用CAN总线实现一个分布式实时控制系统，并对单机控制的软件结构及嵌入式实现方法进行了探讨。     

基于工作站机群的PVM系统的序列比对

序列比对是分子生物学研究领域的一个重要的工具。在DNA数据量急剧增加的今天，高效的序列比对算法在研究新发现的次序中显得非常重要。通过Smith和Waterman法用PVM系统在工作站机群上已完成了分布式序列比对法。也同样在Inter iPSC/860高效性能并行计算机上获得了成功。这个分布式Smith-Waterman算法在Internet GRAIL和GENQUEST上充当搜索工具。该文论述了此算法的实现和性能指标。     

基于多级副本分布的机群服务器动态扩展研究

升级和扩展是机群服务器系统需要经常面对的问题。该文提出了基于多级副本分布的机群服务器系统重构和动态扩展模型及结点动态添加算法,采用优化的局部平衡文件迁移策略,有效减少系统动态添加新结点时的文件迁移开销,支持机群系统的动态扩展。     

NAS集群中单一系统映像的关键技术

NAS(Network Attached Storage)和SAN(Storage Area Network)是目前数据存储领域的主流技术，但都存在缺陷。介绍了构建NAS集群的设想和动向，并就其单一系统映像的关键技术进行了探讨。     

单一系统映象集群管理系统的研究与实现

单一系统映象集群管理系统以传统的GUI图形界面完成与用户的人机交互,有效地屏蔽了集群的系统结构,方便了用户的使用和管理,为实现集群的高性能、高可靠性、高可用性提供了良好的系统软件支持。文章所设计的集群管理系统还提供了丰富的辅助工具,从而极大地简化了系统管理,使系统管理和操作对用户更加透明直观。目前此集群管理系统已应用于某实时测控集群系统。     

On cluster resource allocation for multiple parallel task graphs

Many scientific applications can be structured as parallel task graphs (PTGs), that is, graphs of data-parallel tasks. Adding data parallelism to a task-parallel application provides opportunities for higher performance and scalability, but poses additional scheduling challenges. In this paper, we study the off-line scheduling of multiple PTGs on a single, homogeneous cluster. The objective is to optimize performance without compromising fairness among the PTGs. We consider the range of previously proposed scheduling algorithms applicable to this problem, from both the applied and the theoretical literature, and we propose minor improvements when possible. Our main contribution is an extensive evaluation of these algorithms in simulation, using both synthetic and real-world application configurations, using two different metrics for performance and one metric for fairness. We identify a handful of algorithms that provide good trade-offs when considering all these metrics. The best algorithm overall is one that structures the schedule as a sequence of phases of increasing duration based on a makespan guarantee produced by an approximation algorithm.     

Predictive resource management for meta-applications

This paper defines meta-applications as large, related collections of computational tasks, designed to achieve a specific overall result, running on a (possibly geographically) distributed, non-dedicated meta-computing platform. To carry out such applications in an industrial context, one requires resource management and job scheduling facilities (including capacity planning), to ensure that the application is feasible using the available resources, that each component job will be sent to an appropriate resource, and that everything will finish before the computing resources are needed for other purposes.

This requirement has been addressed by the PAC in three major European collaborative projects: PROMENVIR, TOOLSHED and HPC-VAO, leading to the creation of job scheduling software, in which scheduling is brought together with performance modelling of applications and systems, to provide meta-applications management facilities. This software is described, focusing on the performance modelling approach which was needed to support it.

Early results from this approach are discussed, raising some new issues in performance modelling and software deployment for meta-applications. An indication is given about ongoing work at the PAC designed to overcome current limitations and address these outstanding issues.     

Modeling a resource contention in the management of virtual organizations

A virtual organization provides a cost-efficient method allowing different autonomous entities, such as organizations, departments and individuals, to extend service offerings in a virtual marketplace. To support cost-efficient service provisioning, a suitable procedure must be applied to determine the amount of resources necessary for the operation of virtual organizations.We propose a new mathematical model for a quantitative performance evaluation of resource management in virtual organizations. We present an efficient algorithm to determine the steady state probabilities and the performance measures of the system. A comparison with a detailed simulation model and other numerical approaches shows that the proposed algorithm is fast and accurate. This algorithm can therefore be used for resource dimensioning to support the cost-efficient operation of virtual organizations.     

Virtualized Web server cluster self-configuration to optimize resource and power use

This work proposes a reusable architecture that enables the self-configuration of a supporting infrastructure for Web server clusters using virtual machines. The goal of the architecture is to ensure service quality, evaluating how broadly it complies with the application's operating restrictions and proportionally acting on the configuration of physical servers (hosts) or virtual machines. In addition, through the rational use of resources, the proposal aims at saving energy. A prototype of the architecture was developed and a performance evaluation carried out with two different resource management approaches. This evaluation shows how fully functional and advantageous the proposal is in terms of using resources, avoiding waste, yet maintaining the application's quality of service within acceptable levels. The architecture also shows to be flexible enough to accept, with a reasonable amount of effort, different resource self-configuration policies.     

Failure-aware resource management for high-availability computing clusters with distributed virtual machines

In large-scale networked computing systems, component failures become norms instead of exceptions. Failure-aware resource management is crucial for enhancing system availability and achieving high performance. In this paper, we study how to efficiently utilize system resources for high-availability computing with the support of virtual machine (VM) technology. We design a reconfigurable distributed virtual machine (RDVM) infrastructure for networked computing systems. We propose failure-aware node selection strategies for the construction and reconfiguration of RDVMs. We leverage the proactive failure management techniques in calculating nodes’ reliability states. We consider both the performance and reliability status of compute nodes in making selection decisions. We define a capacity–reliability metric to combine the effects of both factors in node selection, and propose Best-fit algorithms with optimistic and pessimistic selection strategies to find the best qualified nodes on which to instantiate VMs to run user jobs. We have conducted experiments using failure traces from production systems and the NAS Parallel Benchmark programs on a real-world cluster system. The results show the enhancement of system productivity by using the proposed strategies with practically achievable accuracy of failure prediction. With the Best-fit strategies, the job completion rate is increased by 17.6% compared with that achieved in the current LANL HPC cluster. The task completion rate reaches 91.7% with 83.6% utilization of relatively unreliable nodes.     

Maximizing stochastic robustness of static resource allocations in a periodic sensor driven cluster

This research investigates the problem of robust static resource allocation for distributed computing systems operating under imposed Quality of Service (QoS) constraints. Often, such systems are expected to function in an environment where uncertainty in system parameters is common. In such an environment, the amount of processing required to complete a task may fluctuate substantially. Determining a resource allocation that accounts for this uncertainty—in a way that can provide a probability that a given level of QoS is achieved—is an important area of research. We have designed novel techniques for maximizing the probability that a given level of QoS is achieved. These techniques feature a unique application of both path relinking and local search within a Genetic Algorithm. In addition, we define a new methodology for finding resource allocations that are guaranteed to have a non-zero probability of addressing the timing constraints of the system. We demonstrate the use of this methodology within two unique steady-state genetic algorithms designed to maximize the robustness of resource allocations. The performance results for our techniques are presented for a simulated environment that models a heterogeneous cluster-based radar data processing center.     

Grid resource management based on economic mechanisms

Resources in the grid context belong to different control organizations with different interest, therefore the economic interest of each grid participant should be considered. The economic mechanism can guarantee the interest of participants in the grid with fairness and efficiency. In this paper, an economic-based resource management framework is put forward for grid computing, and then how to determine the price of resources with the economic mechanism is studied. A general equilibrium method is presented for general resources and a double auction method is proposed for special resources in the grid environment, respectively. Simulations are performed and experimental results indicate that the two methods are effective for corresponding application scenarios. Expanded version of a paper presented at CCGrid 2005.     

Developing security-aware resource management strategies for workflows

This paper investigates the resource allocation problem for a type of workflow in pervasive computing. These workflows are abstracted from the enterprise-level applications in the business or commerce area. The activities in these workflows require not only computing resources, but also human resources. Human involvement introduces additional security concerns. When we plan/allocate resource capacities, we often assume that when a task is allocated to a resource, the resource will accept the task and start the execution once the processor becomes available. However, the security policies impose further constraints on task executions, and therefore may affect both application- and system-oriented performance. Authorization is an important aspect in security. This paper investigates the issue of allocating resources for running workflows under the role-based authorization control, which is one of the most popular authorization mechanisms. By taking into account the authorization constraints, the resource allocation strategies are developed in this paper for both human resources and computing resources. In the allocation strategy for human resources, the optimization equation is constructed subject to the constraint of the budget available to hire human resources. Then the optimization equation is solved to obtain the number of human resources allocated to each authorization role. The allocation strategy for computing resources calculates not only the number of computing resources, but also the proportion of processing capacity in each resource allocated to serve the tasks assuming each role. The simulation experiments have been conducted to verify the effectiveness of the developed allocation strategies. The experimental results show that the allocation strategy developed in this paper outperforms the traditional allocation strategies, which do not consider authorization constraints, in terms of both average response time and resource utilization.