一种自适应的网络代理集群负载均衡策略

现有动态负载均衡策略难以有效应用于任务资源需求特征动态多变、资源需求信息难以准确获取的网络代理集群中,为解决该问题,提出一种适用于网络代理集群的自适应负载均衡策略。该策略的负载模型可以随任务资源需求特征的变化自适应地调整,能够更准确地表征系统的负载状态和节点间的负载差异。实验结果表明,使用该自适应负载均衡策略的网络代理集群的平均响应时间比其他负载均衡策略低15%~20%。     

一种自适应的网络代理集群负载均衡策略

现有动态负载均衡策略难以有效应用于任务资源需求特征动态多变、资源需求信息难以准确获取的网络代理集群中,为解决该问题,提出一种适用于网络代理集群的自适应负载均衡策略。该策略的负载模型可以随任务资源需求特征的变化自适应地调整,能够更准确地表征系统的负载状态和节点问的负载差异。实验结果表明,使用该自适应负载均衡策略的网络代理集群的平均响应时间比其他负载均衡策略低15％～20％。     

基于云模式的负载均衡策略研究

为了解决高性能计算环境下的“资源饥饿”问题，介绍了若干负载均衡策略，配合原有的轮询算法并对其进行改进．提出了一种基于动态反馈的负载均衡策略。在云环境各个节点性能不一、提供的网络服务所访问的资源多样化的情况下，基于反馈机制的负载均衡能起到显著效果.     

高可用性校园网资源子系统的设计与实现

本文对常用的几种负载均衡技术进行了比较和分析，设计和实现了一种基于集群负载均衡技术的高可用性校园网资源子系统模型。     

一种网格环境下的动态负载均衡机制

负载均衡机制有利于提高广域分布式环境中资源共享和协同工作的效率。根据网格系统的特点,采用灰色预测方法,设计了一种动态资源负载均衡机制,给出了预测模型和实时预测策略以及基于该机制的负载均衡算法。该资源负载均衡机制具有以下特点：可在较小的开销下取得满意的负载均衡性能,具有网格环境下的可扩展性,能够适应网格资源动态变化的特性,解决资源发现过程中的负载均衡 问题。     

一种基于动态反馈的负载均衡算法研究

集群的负载均衡策略是提高集群整体性能的关键。文章提出了一种基于资源使用率和输入参数的负载均衡算法，有效地解决了集群内各服务器间的负载均衡问题。     

云数据中心基于负载权重的负载均衡调度算法

针对现有云数据中心的多维资源利用不均衡问题,提出基于资源负载权重的动态多资源负载均衡调度算法。算法结合服务器各维度资源动态负载情况,构造层次分析法（AHP）判断矩阵来处理多维资源对于负载均衡影响权重大小,在此基础上综合考虑任务资源需求,将任务放置到合适服务器来改善资源利用,实现资源间负载均衡。平台仿真显示新算法可有效提高利用率低的资源的利用效率,在提高整体资源利用率、降低资源间负载不均衡率方面有优势。     

视频网格动态负载加权平滑均衡算法

为实现网格中视频资源服务的动态负载均衡,对一种动态负载加权均衡算法进行改进。利用监测与发现系统收集每台视频服务器的CPU利用率等主要负载参数,运用上述参数加权得到综合负载,对相邻时刻的负载做平滑处理以避免调度抖动。通过比较平滑后的动态负载值与服务器综合负载阈值进行动态调度,改变相应节点的负载,避免视频服务器间的负载失衡。实验结果表明,该算法能有效降低系统平均服务延迟时间并提高吞吐量,从而提升视频资源网格服务的整体性能。     

负载均衡解决方案

服务器负载均衡(Server Load Balance)一般用于提高服务器的整体处理能力，它的最终目的是加快服务器的响应速度，从而全面的提高用户的体验度。从结构上可分为本地负载均衡(Local Server Load Balance)和地域负载均衡(Global Server Load Balance，全局负载均衡)。     

一种支持MMOG的MMOGGRID网格体系结构

基于C／S结构的MMOG不易扩展，服务器容易成为瓶颈．本文提出了一种应用网格技术实现MMOG的体系结构-MMOGGRID网格，并提出了适合此结构的负载均衡解决方案，该方案包括负载均衡算法和游戏地图动态划分算法．实验结果表明，该方案与其它同类的负载均衡机制相比减少了系统延迟，降低了计算资源使用率，更符合MMOG实时性和可扩展性的要求．     

一种支持服务网格的动态负载平衡系统

为了实现服务网格系统内负载的均衡分布，提高资源利用率和系统的吞吐率，设计并实现了一种基于服务网格环境的动态负载平衡系统。提出了层次式负载平衡调度模式，给出了本系统结构形式，设计并实现了一种综合考虑各局部代理作业数和各个局部代理性能以及当前的负载情况的动态双阈值作业分配算法。实验结果表明，此算法能有效地基于负载分派作业，达到了提高网格内分布资源的利用率和减少作业调度时间的目的。     

网络平滑算法在流量工程中的应用

流量工程是当前IP网络解决QoS问题的关键技术之一。然而目前实现流量工程的LSP分布算法一般只对网络资源的利用率进行优化，可能导致网络负载的不平衡。文中引入网络负载平滑度的概念，定义了链路代价函数。针对当前主要的LSP分布算法“带宽．跳数算法”在网络负载平滑度方面的不足，提出由代价函数控制的网络平滑算法，并对算法进行了分析，最后给出相应的实验结果和结论。     

在线多任务异构云服务器负载均衡算法研究

针对由于云服务器之间软件环境存在异构性及数据分布不均匀等特点而导致云服务器集群在处理大量任务时往往出现节点负载不均衡的情况,提出了解决在线多任务异构云服务器集群负载均衡的方法与相关算法。首先统计集群提供的各类服务的平均资源消耗,结合任务在服务器上已运行时长和资源占用情况,预测评估某一时刻服务器上任务剩余负载总量;然后按周期获取节点实际任务负载情况,及时修正任务负载情况;最后综合考虑节点各项性能,计算在待分配任务提交时刻各节点的预测负载评估值,并将任务分配给预测负载最轻的节点。实验结果表明,该算法具有可行性且在多任务异构云服务器集群负载均衡方面具有一定优势。     

Web服务器负荷状态检测方法研究

本文对Web服务器的负荷状态检测方法进行了深入研究。在基于对系统负荷状态与内部资源关系的研究基础上，对Web服务器的负荷特性进行了分析，给出了负荷状态检测参数、检测模型及基于多参数数据融合的检测方法，并通过实验数据对其有效性进行了分析。     

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.     

Utopia: A load sharing facility for large,heterogeneous distributed computer systems

Load sharing in large, heterogeneous distributed systems allows users to access vast amounts of computing resources scattered around the system and may provide substantial performance improvements to applications. We discuss the design and implementation issues in Utopia, a load sharing facility specifically built for large and heterogeneous systems. The system has no restriction on the types of tasks that can be remotely executed, involves few application changes and no operating system change, supports a high degree of transparency for remote task execution, and incurs low overhead. The algorithms for managing resource load information and task placement take advantage of the clustering nature of large-scale distributed systems; centralized algorithms are used within host clusters, and directed graph algorithms are used among the clusters to make Utopia scalable to thousands of hosts. Task placements in Utopia exploit the heterogeneous hosts and consider varying resource demands of the tasks. A range of mechanisms for remote execution is available in Utopia that provides varying degrees of transparency and efficiency. A number of applications have been developed for Utopia, ranging from a load sharing command interpreter, to parallel and distributed applications, to a distributed batch facility. For example, an enhanced Unix command interpreter allows arbitrary commands and user jobs to be executed remotely, and a parallel make facility achieves speed-ups of 15 or more by processing a collection of tasks in parallel on a number of hosts.     

网络负载均衡优化算法的建模与仿真

研究网络资源管理中的负载均衡与优化问题,网络资源有限且负载具有突发性,造成资源浪费。采用传统单一蚁群算法或遗传算法均存在各自不足,难以适应用网络负载变化特点,使网络资源利用率低,网络拥塞严重。为了提高网络资源利用率,使网络负载更加均衡,提出一种蚁群-遗传算法的网络负载均衡方法。首先利用遗传算法对网络负载均衡问题进行全局搜索,使网络负载均衡的解迅速处于全局最优区域解附近,然后将遗传算法的解作为蚁群算法初始信息素,进行进一步搜索,最后找到网络负载均衡的最优解。仿真结果表明,用蚁群-遗传算法提高了网络资源利用率,降低网络丢包率,提高网络整体性能。     

Resource scheduling algorithm with load balancing for cloud service provisioning

Cloud computing uses scheduling and load balancing for virtualized file sharing in cloud infrastructure. These two have to be performed in an optimized manner in cloud computing environment to achieve optimal file sharing. Recently, Scalable traffic management has been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, latency reducing during multidimensional resource allocation still remains a challenge. Hence, there necessitates efficient resource scheduling for ensuring load optimization in cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The method constructs a Fuzzy-based Multidimensional Resource Scheduling model to obtain resource scheduling efficiency in cloud infrastructure. Increasing utilization of Virtual Machines through effective and fair load balancing is then achieved by dynamically selecting a request from a class using Multidimensional Queuing Load Optimization algorithm. A load balancing algorithm is then implemented to avoid underutilization and overutilization of resources, improving latency time for each class of request. Simulations were conducted to evaluate the effectiveness using Cloudsim simulator in cloud data centers and results shows that the proposed method achieves better performance in terms of average success rate, resource scheduling efficiency and response time. Simulation analysis shows that the method improves the resource scheduling efficiency by 7% and also reduces the response time by 35.5 % when compared to the state-of-the-art works.     

Developing a distributed scalable Java component server

We present here approaches for a distributed scalable Java component server. The first one uses a resource broker model, whereby the system is composed of one or several entry point servers, a resource broker and a set of participating servers. The resource broker gives the system its dynamic scalability and load balancing capability by notifying participants and providing information to the entry point servers. An experimental version of the server has been developed. Two other approaches based on Jini and JavaSpace are proposed. An experimental version of the latter one is also compared with the resource broker model.     

基于议价机制的网格资源分配研究

资源管理和分配是网格计算的核心问题，用市场机制可有效地管理和分配网格资源。提出了一个基于议价机制的网格资源分配模型，设计了资源分配策略。模拟实验表明该模型能够较好地适应网格环境的动态变化，并实现各个网格计算资源的负载平衡。