协同虚拟环境中的主动动态负载平衡算法

在基于多服务器体系结构的协同虚拟环境中，用户的随意运动引起某些服务器负载过大而导致系统性能的下降。主动动态负载平衡算法定义了3个主动触发条件，当服务器计算代价过大或任务划分不当引起服务器之间通信量过大或服务器负载接近饱和时，将兴趣隶属度大的用户转移给邻域服务器管理，保证了对虚拟环境中的用户代理进行合理划分。试验数据显示，该方法在均衡各个服务器负载的前提下，可以提高系统的实时性和服务质量。     

基于缓冲Cell的用户迁移方法研究

分布式虚拟环境中现有的用户迁移方法主要基于固定缓冲区,而近年研究热点的多服务器结构采用动态缓冲区,服务器区域调整中受影响的用户不能进行迁移,影响了交互性和效率.提出一种基于缓冲Cell的用户迁移方法,将缓冲区用一组Cell来表示,将用户替身运动和区域调整引起的迁移抽象为4项状态迁移条件.根据用户和所属Cell的状态变化,触发用户状态迁移,多个用户可并发高效地完成迁移.实验表明,这种用户迁移方法可有效支持用户的并发迁移,并具有较低的通信附加负载.     

MMOG中的一种动态负载共享算法*

针对MMOG中的服务器超载问题,提出一种动态负载共享算法,使得一个超载的服务器能够方便地将它的一部分负载迁移到没有超载的服务器上.同时在基于预订区域概念的基础上,通过扩大邻居服务器的范围,提出一种客户端迁移方案来减少该算法的开销,从而提高系统的响应速度和性能.     

基于DNS的异地服务器负载平衡

随着网络的发展,大型网站的访问量剧增,单台服务器已不能满足需要,负载平衡是解决这一问题的有效方式。分析现有的负载平衡方法,提出一种基于DNS的异地服务器负载平衡新方法。该方法通过控制域名解析,将负载分配到各地服务器,其优点在于既可分散各服务器的负载,增强系统的稳定性,又可利用服务器分放各地的网络环境优势,缩短用户和服务之间通信时间,提高服务质量,从而克服现有负载平衡方法的不足。     

基于行为的动态负载分配算法

将复杂行为引入共享虚拟环境,提出了一个基于行为的动态负载分配算法.该算法综合考虑了 结点的处理能力、网络通信能力以及行为计算的特性,能有效地调节系统负载,在提高用户实 时交互能力的前提下,尽量保持行为的真实感.     

针对Web服务的动态负载平衡模型

Web服务是一项非常重要的网络技术，如何提高其的性能是一个热点问题。为了增强Web服务系统的容错性和扩展性，将负载平衡技术应用到Web服务领域。报告了负载平衡算法的现状，分析了Web服务对负载平衡的要求，给出了一个针对Web服务的动态负载平衡模型，介绍了动态调整模型中的服务耗费值、负载阀值和服务器权重的方法，及过载保护的方法。     

考虑约束参数切换的小蜂窝LTE移动负载自适应均衡

针对因用户设备移动性和小区覆盖率造成的网络负载周期性不平衡问题,引入小蜂窝网络以支持高数据率服务和密集部署,提出一种自适应网络负载状态并考虑负载估计的小蜂窝网络移动性负载平衡算法。该算法根据过载单元和相邻单元调整进行参数切换,其资源利用取决于信号质量和所连接用户设备在长期演进网络(LTE)中的流量需求,并定义资源块利用率作为单元负载的度量,根据网络负载情况使用自适应阈值来确定过载单元。为避免性能振荡,考虑移动负载对网络的影响,进行负载均衡过程设计。最后通过系统级仿真评价了该算法在不同环境下的性能,仿真结果表明,该算法在网络间提供了更均衡的负载(即小区间的标准差较小),比以前的算法具有更高的网络吞吐量。     

一个有效的动态负载平衡方法

动态负载平衡问题是影响工作站网络并行计算性能的重要因素.首先分析出在负载平衡中产生额外开销的根本原因是负载的移动,进而定性地给出了每次移动负载的粒度公式.引入益处估计的方法,仅在有益的情况下进行负载平衡.另外还提出了一个动态负载平衡算法.最后,通过实验,将该算法的运行结果与其他人的负载平衡结果以及不作负载平衡的情况进行了对比.此负载平衡方法在工作站为空载以及不同的负载和应用问题的数据规模的情况下,都优于Siegell等人提出的方法.     

基于模因优化和循环调度的多目标负载均衡技术

针对现有多目标调度方法所需时间较长以及处理突发情况时性能降低的问题,提出一种基于模因优化和循环调度的多目标负载均衡技术。使用突发检测器检测发送到云服务器的用户请求,确定负载状态。基于测器结果,应用不同的负载平衡算法来高效地调度用户任务。利用选定的负载平衡算法将用户请求任务调度到资源最佳的虚拟机上,保证在最低的时间消耗内达到负载均衡的状态。实验结果表明,与其他算法相比,该方法在多个性能指标上具有明显优势,可以提高调度效率的同时,最大限度地降低云中的能源使用。     

集群系统中自适应负载反馈平衡策略的研究

当前在集群系统中，负载平衡策略虽然很多，但是为了减少反馈开销，一般策略为采用在前端估计后端负载，所以不能很好地完成负载平衡的任务。针对这一问题，提出了一种自适应负载反馈平衡策略，各个服务器根据自身负载的变化来决定负载反馈的时机，前端根据负载信息和请求率计算出各个服务器的负载权值，最后根据负载权值来调度服务器处理请求，以实现负载平衡。由于采用了自适应的反馈策略，在获得各个服务器负载信息的同时减少了负载反馈的开销，实现了系统的负载均衡。测试结果表明该策略表现出了一定的优势。     

基于DHT的P2P系统负载均衡的有效算法

基于DHT的P2P系统中,各种因素例如结点异构性和不同的文件访问率等,都可能会影响DHT系统的效率。本文提出一个基于DHT的P2P系统中有效的负载均衡算法。该算法提出一个全分布机制来维护文件访问的历史信息,用来预测未来文件访问频率。设计了一个新的负载均衡算法,当新结点加入时,历史信息和结点异构性一起用来决定最佳负载分配。在系统运行期间如果有过载结点出现也可动态执行负载重分配。该算法不使用虚服务器,减少了维护路由元数据的处理开销。     

A load balancing scheme for massively multiplayer online games

In a distributed MMOG (massively multiplayer online game) server architecture, the server nodes may become easily overloaded by the high demand from the players for state updates. Many works propose algorithms to distribute the load on the server nodes, but this load is usually defined as the number of players on each server, what is not an ideal measure. Also, the possible heterogeneity of the system is frequently overlooked. We propose a balancing scheme with two main goals: allocate load on server nodes proportionally to each one’s power and reduce the inter-server communication overhead, considering the load as the occupied bandwidth of each server. Four algorithms were proposed, from which ProGReGA is the best for overhead reduction and ProGReGA-KF is the most suited for reducing player migrations between servers. We also make a review of related works and some comparisons were made, where our approach performed better.     

On delay adjustment for dynamic load balancing in distributed virtual environments

Distributed virtual environments (DVEs) are becoming very popular in recent years, due to the rapid growing of applications, such as massive multiplayer online games (MMOGs). As the number of concurrent users increases, scalability becomes one of the major challenges in designing an interactive DVE system. One solution to address this scalability problem is to adopt a multi-server architecture. While some methods focus on the quality of partitioning the load among the servers, others focus on the efficiency of the partitioning process itself. However, all these methods neglect the effect of network delay among the servers on the accuracy of the load balancing solutions. As we show in this paper, the change in the load of the servers due to network delay would affect the performance of the load balancing algorithm. In this work, we conduct a formal analysis of this problem and discuss two efficient delay adjustment schemes to address the problem. Our experimental results show that our proposed schemes can significantly improve the performance of the load balancing algorithm with neglectable computation overhead.     

基于粗糙集的负载均衡算法研究

在负载均衡服务器集群中,负载均衡算法是一个关键部分。针对目前服务器集群使用的负载均衡算法所存在的缺陷:服务器可能出现较大倾斜,本文提出了一种基于粗糙集理论的动静结合的负载均衡算法。该算法避免了服务器间负载的不均衡,能有效地防止服务器倾斜的发生,提高了集群系统的整体性能。     

基于Kademlia的负载平衡云存储算法

针对采用主从式结构的主流云存储系统可能出现的性能瓶颈和可扩展问题,基于分布式哈希表(DHT)技术的完全分布式云存储系统成为一种新的选择。解决好节点的负载平衡问题,是此类技术获得推广的关键。研究了Kademlia算法应用于云存储系统的负载平衡性能。考虑到算法在异构环境下负载平衡性能有明显下降,改进算法在Kademlia找出的候选存储节点中根据节点的存储能力来分配负载。仿真结果表明,改进后算法的负载平衡性能有非常明显的提高,在系统模拟运行时间足够长(如1500 h以上)时,过载节点平均下降7.0%(轻载)和33.7%(重载);文件保存成功率平均提高27.2%(轻载)和35.1%(重载),而增加的通信开销可接受。     

Load sharing in distributed multimedia-on-demand systems

Service providers have begun to offer multimedia-on-demand services to residential estates by installing isolated, small-scale multimedia servers at individual estates. Such an arrangement allows the service providers to operate without relying on a highspeed, large-capacity metropolitan area network, which is still not available in many countries. Unfortunately, installing isolated servers can incur very high server costs, as each server requires spare bandwidth to cope with fluctuations in user demand. The authors explore the feasibility of linking up several small multimedia servers to a (limited-capacity) network, and allowing servers with idle retrieval bandwidth to help out servers that are temporarily overloaded; the goal is to minimize the waiting time for service to begin. We identify four characteristics of load sharing in a distributed multimedia system that differentiate it from load balancing in a conventional distributed system. We then introduce a GWQ load sharing algorithm that fits and exploits these characteristics; it puts all servers' pending requests in a global queue, from which a server with idle capacity obtains additional jobs. The performance of the algorithm is captured by an analytical model, which we validate through simulations. Both the analytical and simulation models show that the algorithm vastly reduces wait times at the servers. The analytical model also provides guidelines for capacity planning. Finally, we propose an enhanced GWQ+L algorithm that allows a server to reclaim active local requests that are being serviced remotely. Simulation experiments indicate that the scheduling decisions of GWQ+L are optimal, i.e., it enables the distributed servers to approximate the performance of a large centralized server     

分布式数据流处理系统的动态负载平衡技术

设计了一种新的大规模分布式数据流处理系统的体系结构。系统由一组异构的服务器集群组成，负载在每个服务器集群内部多台同构的服务器之间获得平衡，从而达到整个系统的负载平衡。集群设计的主要目标之一是以资源换性能，服务器集群中服务器的最大数目足够保证系统不再发生过载现象，不再需要会降低性能的卸载技术。而且投入运行的服务器的数目根据实际的系统负载来决定，负载较轻时，一部分服务器可以进入休眠状态来减少能源的消耗。根据系统动态增减服务器的特点，设计了全新的初始化算法、动态负载平衡算法。与以前的分布式数据流处理系统相比，由于单个集群的服务器的数目大大减少，算法复杂性降低、速度加快、优化的空间增大。     

Join-Idle-Queue: A novel load balancing algorithm for dynamically scalable web services

The prevalence of dynamic-content web services, exemplified by search and online social networking, has motivated an increasingly wide web-facing front end. Horizontal scaling in the Cloud is favored for its elasticity, and distributed design of load balancers is highly desirable. Existing algorithms with a centralized design, such as Join-the-Shortest-Queue (JSQ), incur high communication overhead for distributed dispatchers.We propose a novel class of algorithms called Join-Idle-Queue (JIQ) for distributed load balancing in large systems. Unlike algorithms such as Power-of-Two, the JIQ algorithm incurs no communication overhead between the dispatchers and processors at job arrivals. We analyze the JIQ algorithm in the large system limit and find that it effectively results in a reduced system load, which produces 30-fold reduction in queueing overhead compared to Power-of-Two at medium to high load. An extension of the basic JIQ algorithm deals with very high loads using only local information of server load.