基于负载权值的负载均衡算法

为解决服务器集群负载分配不均的问题,综合考虑节点负载和节点性能信息,提出了基于负载权值的动态反馈负载均衡算法。利用负载权值选择分配负载的节点集合,保证性能高的节点分配到较多的负载;引入负载差值计算节点分配负载的概率,使得负载的分布更加均匀;通过负载增量及负载修正保持系统的稳定性。使用OPNET仿真软件进行测试,结果表明该算法能有效提高负载均衡效率,有较好的负载均衡效果。     

异构分布式系统中基于负载均衡的容错调度算法

提出了基于主/从版本的具有容错功能的进程调度算法HDALF和HDLDF,且分别给出两种算法的时间复杂度并对算法的负载均衡性和节点资源利用率作了讨论.与以往容错调度算法不同的是,此算法是在被动进程复制模式下、适合于异构分布式系统的容错调度算法.而以往的研究都是建立在主从版本进程有相等的负载或执行时间相同的模型基础上,或者仅适合于同构分布式系统.实验结果表明,HDALF算法和HDLDF算法的性能比基于同构分布式模型下的两阶段算法更加优越.并且得出了这样的结果：当系统发生故障前后的负载均衡性权值相等时,在负载均衡和处理机资源利用率方面,HDLDF算法都要优于HDALF算法.     

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。     

基于公平调度算法的分布式系统负载均衡研究

负载均衡是提高分布式系统性能的重要技术,同时也是系统高可用性、可扩展性、冗余性的必然要求.针对分布式系统任务调度不均衡问题,在分析和建立系统仿真和任务调度模型的基础上,提出了一种基于公平指标的任务调度负载均衡算法,推导出在多节点条件下的任务分配方法,并在此模型下改进了基于公平指标的负载均衡算法.最后,在Linux平台下,进行了仿真实验和性能比较.实验结果表明,该算法是有效的,它可以有效地提高分布式系统的性能和效率.     

集群技术及负载均衡调度算法研究

集群(cluster)技术是一种较新的技术,通过集群技术,可以在付出较低成本的情况下获得在性能、可靠性、灵活性等方面相对较高的收益.任务调度是集群系统中的核心技术.文章对集群的定义、分类、优点及各种常见的负载均衡调度算法进行了详细归纳.     

基于DNS的负载均衡算法研究

随着网站访问量的增加,一些网站往往有多个地理上分布的提供相同服务内容的服务器群组,因此需要进行负载均衡处理。文章概括分析了现有的几种负载均衡算法,并提出了两种基于DNS的负载均衡新方法。该方法通过域名解析,根据每个服务器的可用性、客户与服务器之间的距离,以及地理范围匹配值,按照从优到劣的顺序返回网站的多个服务器的IP地址。     

基于流映射的负载均衡调度算法研究

网络管理者需要能够提供可扩展性、吞吐率保证及报文顺序的高性能路由器体系结构.目前基于Crossbar的集中式路由器体系结构难以实现性能和规模的可扩展,基于两级Mesh网络的负载均衡交换结构成为扩展Internet路由器容量的有效的途径.负载均衡路由器存在严重的报文乱序现象,输出端报文重定序复杂度为O(N2).文中提出一种区域均等的负载均衡交换结构,每k个连续的中间级输入端口划分为一个区域,输入端采用基于流映射的负载分配算法UFFS-k(Uniform Fine-grain Frame Spreading,k为聚合粒度,简称UFFS-k),在k个连续的外部时间槽,以细粒度的方式将同一条流的k个信元分派到固定的映射区域,通过理论证明,该调度策略可获得100%吞吐率并能够保证报文的顺序.为避免流量区域集中现象,采用双循环(dual-rotation)方式构建不同输入端口的流到区域的映射关系;为实现负载在中间级输入端口的均衡分布,每个输入端口维护全局统一视图的流量分布矩阵,UFFS-k调度算法根据流量分布矩阵调度单位帧,可以证明,对任意输出端口j,同一区域OQj队列长度相同且不同区域OQj队列长度至多差1,从而实现了100%负载均衡度.UFFS-k调度算法分布于每个输入端口独立执行,根据流到区域的映射关系及负载分布状态分派信元,模拟结果显示,当聚合粒度k=2时,UFFS-k算法在同类维序算法中表现出最优延迟性能.     

基于Red5服务器集群负载均衡调度算法研究

基于动态负反馈的机制,参考现有的加权最小连接数调度和轮转调度算法,设计出了改进后的负载均衡调度算法。通过负反馈机制计算每台服务器的综合负载权重值,而综合负载权重值直接体现着服务器的当前处理能力。调度服务器根据综合负载权重值分配工作负载,实现负载的均衡分布。     

SOA中一种基于负载均衡的服务查找请求路由算法

服务查找是面向服务架构(SOA)中一个非常重要的环节,但目前的服务查找算法一般并未考虑到服务查找节点间的负载均衡,在请求频繁条件下不能满足查找效率的要求。提出一种分布式的、综合考虑节点处理能力和网络延时、适用于SOA中分布式服务注册中心的服务查找请求路由算法HaFA。该算法利用负载度实现对服务节点计算能力的度量,解决了负载度均衡后任务仍可能分配到弱计算能力节点上的问题,提高了服务注册中心计算资源的利用率;利用节点负载波动率估量下一个离散时间点的负载度,解决了网络延时期间负载波动对实现均衡造成影响的问题。实验结果表明,HaFA在分布式服务查找中能有效提高系统吞吐率,缩短结果响应的平均等待时间。     

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.     

An enhanced load balancing mechanism based on deadline control on GridSim

A Grid is a network of computational resources that may potentially span many continents. Load balancing in a Grid is a hot research issue which affects every aspect of the Grid, including service selection and task execution. Thus, it is necessary and significant to solve the load balancing problem in a Grid. In this paper, we propose a dynamic, distributed load balancing scheme for a Grid which provides deadline control for tasks. In our scenario, first, resources check their state and make a request to the Grid Broker according to the change of load state. Then, the Grid Broker assigns Gridlets between resources and scheduling for load balancing under the deadline request. We apply our load balancing strategy into a popular Grid simulation platform GridSim. Experimental results prove that our proposed load balancing mechanism can (1) reduce the makespan, (2) improve the finished rate of the Gridlet, and (3) reduce the resubmitted time.     

Online vector scheduling and generalized load balancing

We give a polynomial time reduction from the vector scheduling problem (VS) to the generalized load balancing problem (GLB). This reduction gives the first non-trivial online algorithm for VS where vectors come in an online fashion. The online algorithm is very simple in that each vector only needs to minimize the _Lln(md)$L_{ln\left(md\right)}$ norm of the resulting load when it comes, where m$m$ is the number of partitions and d$d$ is the dimension of vectors. It has an approximation bound of elog(md)$elog\left(md\right)$, which is in O(ln(md))$O(ln\left(md\right))$, so it also improves the O(ln²d)$O\left({ln}^{2}d\right)$ bound of the existing polynomial time algorithm for VS. Additionally, the reduction shows that GLB does not have constant approximation algorithms that run in polynomial time unless P=NP$P=NP$.     

基于目标约束的分层动态负载均衡算法*

针对网格环境下的负载不均问题,提出了一种分层动态负载均衡机制,该机制采用随机服务模型描述网格任务流特性及其资源上的动态负载状态,将站点内负载平衡问题归结为目标约束规划问题。理论分析了分层负载均衡机制的有效性证明并设计了优化方案的求解算法,仿真实验结果显示,该分层负载均衡算法在平均响应时间、系统吞吐量方面优于以往的RBA算法和DBA算法。     

Resource scheduling and load balancing in distributed robotic control systems

This paper describes the latest advances made to a software architecture designed to control multiple miniature robots. As the robots themselves have very limited computational capabilities, a distributed control system is needed to coordinate tasks among a large number of robots. Two of the major challenges facing such a system are the scheduling of access to system resources and the distribution of work across multiple workstations. This paper discusses solutions to these problems in the context of a distributed surveillance task.     

一种基于统计思想的网格负载平衡调度算法

针对层次化网格模型结构,运用统计思想提出了一种新的资源分配与任务调度算法,不仅能够提高资源的利用率和系统的吞吐率,而且能够实现网格系统内部的负载平衡。算法主要包含三个功能模块,即负载跟踪模块、作业分配模块和负载监视模块。在解释了方案中各功能部件的作用及其相互之间关系的基础上,给出了相应的算法伪码。仿真实验表明,该算法是有效的。     

一种异构云环境下的负载均衡算法

针对动态负载均衡算法在异构云环境中的任务迁移次数过多的问题,提出了一种最小化任务迁移次数的动态负载均衡(MMLB)算法。MMLB算法通过自适应阈值对虚拟机进行分组、任务选择算法最小化任务迁移的次数、任务调度算法优化任务分配实现了任务的再分配。将MMLB与WRR、HBBLB、LBF算法进行实验对比分析,MMLB算法在Makespan、平均任务响应时间、负载不均衡度等评价指标上表现更优,并且有效降低了任务迁移的次数。实验结果验证了MMLB算法的可行性和有效性。     

Real-time load balancing scheduling algorithm for periodic simulation models

A scheduling algorithm is crucial for real-time simulations because it guarantees that each model meets its deadline. Traditional online real-time scheduling algorithms such as Earliest Deadline First (EDF) introduce a high overhead when scheduling a large number of models. In this paper, a new algorithm called time-stepped load balancing (TLS) is proposed to address the real-time execution of a model set in a time-stepped simulation. A load balancing schedule table is generated before a simulation and rebalanced at runtime to dynamically schedule the changed model set. This table is organized by the execution periods of the models and balanced according to the load of each time step. Moreover, the slack time is distributed evenly among the steps to improve the real-time reliability. An extension to the algorithm for a multi-core environment is further studied to address those models with long execution times. Experimental results show that our scheduling algorithm outperforms the classical EDF approach. The highest performance improvement of TLS over EDF reaches 3–4% in terms of saving processor resources, and the jitter is about 4 times less when 90 entities are employed in a typical tank combat simulation scenario.     

基于服务器实时性能状态的动态负载均衡算法

为实现Web服务器集群合理的作业任务分配,文章提出了一种新的负载均衡算法,综合考虑了负载均衡调度器后端的业务主机的实时性能,实现了负载均衡调度的动态调整.