Java并行计算环境中的负载监测系统

动态负载平衡是使用机群进行网络并行计算的关键，而获取节点的负载信息是实现动态负载平衡的前提。该文介绍了Java并行计算环境中负载监测系统JSYS的体系结构和使用Java中RMI结合JNI机制的实现方法，并讨论了JSYS使用的负载信息获取技术和原始数据的处理方法，实验结果表明JSYS能够有效地为动态负载平衡提供服务。     

支持动态负载平衡的分层消息队列模型

中间件技术为解决异构分布式环境下的负载平衡问题提供了有力的工具,但传统的消息中间件负载平衡的实现较为复杂,其动态参数繁多且容易带来额外开销。提出了一种分层消息队列模型,该模型中利用队列组管理器对分布式队列进行组管理,并提供了丰富的任务分配策略。在该模型的基础上提出动态负载平衡实现方案:通过基于队列的阈值阈长模型实时监控成员队列的负载情况,采用集中式调度进行负载信息搜集和负载平衡决策,结合负载迁移和队列组管理进行过载处理。     

NOW环境中一种负载平衡系统ALBSIN的研究与实现

本文介绍了一种在NOW（Network of Workstation)环境中进行负载平衡的方法－ALBSIN（A Loand Banlancing System in NOW),它采用分布式负载信息存储和交互的方式在节点间进行任务分配和负载平衡,实验表明,此方法具有良好的自适应性,可行性和可扩放性,是NOW中任务分配和负载平衡的一种理想方法。     

一种改进的启发式网格任务调度算法

网格任务调度算法是影响网格成功与否的关键技术之一。网格计算中,一个好的任务调度算法不但要考虑所有任务的makespan,使其值尽量小,同样要考虑到整个系统机器间的负载平衡问题。文章对异构计算环境下的元任务调度算法进行了分析,针对Min-min算法可能引发的负载不平衡问题,结合网格计算环境的特点,提出了一种适用于网格计算环境中的任务调度算法。     

基于模糊逻辑技术的中间件负载平衡机制

针对当前基于中间件负载平衡机制中负载定位不够准确、监测粒度过大等问题,基于模糊逻辑技术提出一种更精确、更细粒度的负载平衡系统及其负载平衡机制。负载平衡系统的节点负载信息记录表记录系统中所有节点从模糊推理得出的负载状态信息,负载转发表记录服务节点与相应服务请求者之间的关系。实验结果表明,该负载平衡机制能有效地定位过载节点,较好地实现集群中的负载平衡服务。     

基于移动代理的负载平衡策略

在异构计算环境中负载平衡是一个重要问题。移动代理是一种新的分布计算模式,具有许多优势,比如移动代理能够从一台机器移动到另一台机器执行任务。该文提出了一个基于移动代理的并行计算框架,利用一个二段负载平衡策略使程序能够适应不断变化的异构计算环境。实验结果显示移动代理不仅能够用于并行计算,而且能够有效地改善负载平衡。     

网格计算中一种改进的启发式任务调度算法

网格计算技术是20世纪90年代出现的新兴研究领域.网格系统由异构的资源组成.网格计算中,一个好的任务调度算法不但要考虑所有任务的makespan,使其值尽量小,同样要考虑到整个系统机器间的负载平衡问题.对异构计算环境下的元任务调度算法进行了分析,针对Min-min算法可能引发的负载不平衡问题,结合网格计算环境的特点,提出了一种适用于网格计算环境中的任务调度算法.     

工作站网络中负载参数的一种收集方法

工作站网络 ( NOW)中影响负载平衡效果的关键之一是及时收集各工作站的负载信息 .本文提出在工作站网络中收集负载信息的一种新方法 .实验证明 ,将之应用于动态负载平衡算法 ,可取得良好的性能 .     

一种分布式数据流系统负载平衡算法

分布式数据流系统中,当输入数据流速发生较大波动时,会引起部分节点过载,从而影响整个系统的数据处理能力。针对这个问题,该文分析了分布式数据流系统的特点以及现有负载平衡算法的局限性,提出了一种利用多层重叠结构进行负载信息收集和负载分配的平衡算法。该算法利用虚拟树形结构进行负载信息的收集和负载分配,具有较好的扩展性能。以环形虚拟结构中保存的节点相对位置信息为依据进行负载移动,从而有效降低系统的响应时间。实验结果表明该算法具有良好的负载平衡能力和一定的应用价值。     

基于局部网络信息的P2P系统负载平衡算法

提出了一种基于P2P网络局部信息的负载平衡算法，该算法依赖于局部网络的负载信息，并在局部网络内部进行负载迁移使整个系统达到负载平衡状态。理论分析和实验数据均表明，该算法可在网络传输存在限制的条件下，尽快地使系统到达平衡状态。基于局部负载信息与基于全局负载信息的负载平衡效果几乎相同，而前者的时间复杂度远低于后者，特别是在节点较多的P2P网络中。同时由于在局部网络内进行负载迁移，故能够以较小的网络通信量得到良好的性能。     

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.     

A hybrid policy for fault tolerant load balancing in grid computing environments

Due to the emergence of grid computing over the Internet, there is a need for a hybrid load balancing algorithm which takes into account the various characteristics of the grid computing environment. Hence, this research proposes a fault tolerant hybrid load balancing strategy namely AlgHybrid_LB, which takes into account grid architecture, computer heterogeneity, communication delay, network bandwidth, resource availability, resource unpredictability and job characteristics. AlgHybrid_LB juxtaposes the strong points of neighbor-based and cluster based load balancing algorithms. Our main objective is to arrive at job assignments that could achieve minimum response time and optimal computing node utilization. Major achievements include low complexity of proposed approach and drastic reduction of number of additional communications induced due to load balancing. A simulation of the proposed approach using Grid Simulation Toolkit (GridSim) is conducted. Experimental results show that the proposed algorithm performs very well in a large grid environment.     

高性能集群系统中资源负载量化的研究

负载均衡一直是高性能集群系统中资源分配追求的一个主要目标，能否有效地进行负载识别则直接关系到负载均衡的最终实现。本文针对计算资源和通信资源这两类作业争用的主要资源，分别讨论了其负载的衡量以及负载的量化方法，并通过具体的实验来加以说明。     

Extremal Optimization applied to load balancing in execution of distributed programs

The paper describes methods for using Extremal Optimization (EO) for processor load balancing during execution of distributed applications. A load balancing algorithm for clusters of multicore processors is presented and discussed. In this algorithm the EO approach is used to periodically detect the best tasks as candidates for migration and for a guided selection of the best computing nodes to receive the migrating tasks. To decrease the complexity of selection for migration, the embedded EO algorithm assumes a two-step stochastic selection during the solution improvement based on two separate fitness functions. The functions are based on specific models which estimate relations between the programs and the executive hardware. The proposed load balancing algorithm is assessed by experiments with simulated load balancing of distributed program graphs. The algorithm is compared against a greedy fully deterministic approach, a genetic algorithm and an EO-based algorithm with random placement of migrated tasks.     

CORBA负载平衡的研究

一、引言作为分布式计算的一个重要规范—CORBA,其主要目标是解决面向对象的异构应用之间的互操作问题,并提供了分布式计算所需的多项服务。ORB是CORBA平台的核心,它用于屏蔽与底层平台有关的细节,使开发者可以集中精力去解决与应用相关的问     

LBS—基于PVM的动态任务负载平衡系统

负载平衡问题是影响工作站机群系统并行计算性能的一个重要因素。     

ATM网络拥塞预防的动态负载平衡策略

讨论了拥塞预防在ＡＴＭ网络中的必要性以及拥塞预防的关键性要素,并利用公式系统中并行计算的动态负载平衡的思想,提出了ＡＴＭ网络中负载的概念,给出了ＡＴＭ网络拥塞预防的算法。     

Artificial life techniques for load balancing in computational grids

Load balancing is a very important and complex problem in computational grids. A computational grid differs from traditional high performance computing systems in the heterogeneity of the computing nodes and communication links, as well as background workloads that may be present in the computing nodes. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of load balancing scenarios. Artificial life techniques have been used to solve a wide range of complex problems in recent times. The power of these techniques stems from their capability in searching large search spaces, which arise in many combinatorial optimization problems, very efficiently. This paper studies several well-known artificial life techniques to gauge their suitability for solving grid load balancing problems. Due to their popularity and robustness, a genetic algorithm (GA) and tabu search (TS) are used to solve the grid load balancing problem. The effectiveness of each algorithm is shown for a number of test problems, especially when prediction information is not fully accurate. Performance comparisons with Min-min, Max-min, and Sufferage are also discussed.     

分布式计算中基于资源分级的自适应Min-Min算法*

Min-Min任务调度算法的思路总是优先调度执行时间较短的小任务,无法得到理想的最优跨度及资源负载平衡．针对该问题,提出基于资源分级的自适应Min-Min算法．分配任务前,先参考现有资源的属性进行分级处理,再与任务在资源中的最小完成时间作乘积得到的最小任务资源组合进行调度;在任务调度过程中,引入自适应阈值,调节长任务的调度等级,从而达到优化效果．通过模拟仿真实验,表明该算法在时间跨度和负载平衡上均有较好性能．