一种基于负载反馈的分布式数字集群动态负载均衡算法

针对传统负载均衡算法不能满足公网数字集群系统高并发用户请求和快速呼叫建立等需求,提出一种基于负载反馈的分布式数字集群动态负载均衡算法,实现公网数字集群系统负载均衡,提高用户容量。首先建立参与MCPTT服务器的静态负载和动态负载监控机制和指标;然后利用加权轮询算法为用户分配参与MCPTT服务器,并通过用户请求的处理获得复合负载参数;根据负载指标的反馈更新参与MCPTT服务器权值以动态调整服务器负载。仿真结果表明,该算法的负载均衡效果优于传统算法和其他动态反馈算法,负载均衡度更小、用户请求响应延迟更低。     

Web服务器集群系统的实现方法及负载管理

影响WWW网站应用的一个主要因素是用户请求的响应时间太慢,这严重影响了基于Internet的各项应用的发展。本文主要阐述了如何通过多个分布式的Web服务器结点来构筑Web服务器集群系统。文中详细说明了Web服务器集群系统的实现原理及方法,并讨论提出了实出其中各个服务结点之间负载平衡的一些基本技术及机制。     

基于Nginx负载均衡的动态改进算法

随着过去几十年互联网服务的指数增长,各大网站的访问量急剧上升。海量的用户请求使得热门网站的网络请求率可能在几秒钟内大规模增加。一旦服务器承受不住这样的高并发请求,由此带来的网络拥塞和延迟会极大地影响用户体验。负载均衡是高可用网络基础架构的关键组件,通过在后端引入一个负载均衡器,将工作负载分布到多个服务器来缓解海量并发请求对服务器造成的巨大压力,提高后端服务器和数据库的性能以及可靠性。而Nginx作为一款高性能的HTTP和反向代理服务器,正越来越多地应用到实践中。文中将分析Nginx服务器负载均衡的体系架构,研究默认的加权轮询算法,并提出一种改进后的动态负载均衡算法,实时收集负载信息,重新计算并分配权值。通过实验测试,对比不同算法下的负载均衡性能,改进后的算法能有效提高服务器集群的性能。     

Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

Web service applications are increasing tremendously in support of high-level businesses. There must be a need of better server load balancing mechanism for improving the performance of web services in business. Though many load balancing methods exist, there is still a need for sophisticated load balancing mechanism for not letting the clients to get frustrated. In this work, the server with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests. The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low, medium and high load by the load balancing application. Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system. Many Load Balancing schemes are based on the graded thresholds, because the exact information about the network flux is difficult to obtain. Using two thresholds L and U, it is possible to indicate the load on particular server as low, medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L, between L and U or above U respectively. However, the existing works of load balancing in the server farm incorporate fixed time to measure real time response time, which in general are not optimal for all traffic conditions. Therefore, an algorithm based on Proportional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal performance. The emulation results has shown a significant gain in the performance by tuning the threshold time. In addition to that, tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune the fixed time slots.     

Load balancing for video streaming services in hierarchical wireless networks

Wireless networks have focused on voice call services or wireless Internet access services. These days, the application service domain of wireless networks is rapidly expanding, and a wide variety of new services is emerging. Video streaming service is one of the most promising examples, evidenced by 3GPP’s MBMS (Multimedia Broadcast Multicast Service) and IMS (IP Multimedia Subsystem). In this paper, we consider the provision of video streaming services in hierarchical wireless networks with multiple layers of cells. We particularly focus on optimal load balancing among the cells, aiming at the minimization of frame drop ratio for given video streaming sessions. From this objective function, we derive the optimal load balancing condition. Load balancing is essentially the issue of which users are assigned to which cell, i.e., the user assignment problem. In our user assignment algorithm, we compute thresholds to divide users into groups according to the user characteristics, and map the user groups to proper cells. The optimal load balancing condition can be reached by adaptively adjusting the threshold at run time. This process does not require prior knowledge about the system status, such as the system capacity or user traffic requests, which warrants the practicality of the proposed scheme. Via simulations, we demonstrate that the proposed scheme achieves optimal load balancing in various realistic environments.     

基于区分Web QoS的负载均衡集群模型

随着电子商务的应用逐步深入,用户访问量的激增且服务请求多样．如何实现对所有请求的快速响应是当前解决的问题．针对此问题,本文提出采用基于区分WebQoS的负载均衡技术．建立了基于区分WebQoS的负载均衡的集群模型．根据请求类型和用户权限划分服务等级,高服务的请求具有高优先调度权,在集群当中通过动态反馈技术均衡调度到某个节点,从而达到区分WebQoS服务的目的,同时也保证集群服务器的负载均衡．该模型在网络环境及硬件环境相同的条件下,与常用的负载均衡技术进行了比较,实验结果证明本文提出的方法效果显著．     

ASAS集群的模糊控制策略

传统的集群服务器依靠前端分发器基于负载均衡来分发客户请求,而ASAS集群采用了基于后端服务器负载分析后的主动调度策略。控制ASAS集群后端服务器的CPU利用客户请求的响应时间,或者连接容量,却很难建立一个精确的数学模型,因此传统的控制器很难控制。这里设计了一个模糊控制算法,控制算法的输入是CPU利用率和客户请求的响应时间,输出是连接数,试验表明这种模糊控制策略取得了很好的性能表现。     

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。     

负载均衡算法在智慧矿山软件平台中的应用

针对现有负载均衡算法在处理智慧矿山系统数据时存在处理速度慢、无法合理利用现有资源完成任务调度等问题,提出一种基于布谷鸟搜索的加权最小连接数(CS-WLC)算法,并将其应用于智慧矿山软件平台解决负载均衡问题。该算法综合考虑后端服务器处理速率、内存容量、磁盘IO速率、网络吞吐量、进程数指标,通过对指标赋予权值计算各后端服务器利用率;根据计算结果,采用布谷鸟搜索算法对后端服务器进行全局寻优,得到一组较优解;考虑连接数及使用频率对后端服务器赋予权值,采用加权最小连接数(WLC)算法在较优解中选取负载较轻的后端服务器处理实时数据存取和用户访问请求。采用分布式融合性监控系统软件平台进行负载均衡测试,结果表明在数据量不断增多的情况下,与WLC算法相比,CS-WLC算法应答时延小、响应连接数多,从而验证了CS-WLC算法具有更好的负载均衡效果。     

基于请求类型的数字图书馆负载平衡模型

对于具有海量信息的数字图书馆,文章针对不同请求服务时间变化大的特性,提出基于请求类型的负载平衡模型,该模型根据各种不同请求对服务器负载状况的影响并结合各服务器的性能进行动态负载调度。避免占用服务器资源较多的请求同时分配到同一台服务器上。仿真结果表明该模型具有良好的负载平衡效果。有效地缩短了用户请求的响应时间。     

Video server scheduling using random early request migration

Video request migration among servers to achieve effective video-on-demand (VoD) services is investigated in this work. Our study is focused on the design and analysis of a random early migration (REM) scheme for user requests. When a new request is dispatched to a video server, the REM-based scheduler decides whether request migration is needed with a certain probability, which is a function of the service load. To analyze the request migration process, we introduce a state matrix representation that stores the service load information of each video server and plays an important role in the determination of migration paths. Based on this representation, we develop two methods to calculate performance metrics: the service failure rate and the system delay in service migration. Simulation results show that the REM scheme outperforms both the DASD dancing algorithm [1] and the traditional migration scheme adopted in [2,3] with shorter service delay and lower failure rates. It is also confirmed that our theoretical results match well with experimental results. Revised: 24 October 2004, Published online: 8 April 2005     

A testbed for performance evaluation of load‐balancing strategies for Web server systems

To improve response time of a Web site, one replicates the site on multiple servers. The effectiveness of a replicated server system will depend on how the incoming requests are distributed among replicas. A large number of load‐balancing strategies for Web server systems have been proposed. In this paper we describe a testbed that can be used to evaluate the performance of different load‐balancing strategies. The testbed uses a general architecture which allows different load‐balancing approaches to be supported easily. It emulates a typical World Wide Web scenario and allows variable load generation and performance measurement. We have performed some preliminary experiments to measure the performance of a few policies for load balancing using this testbed. Copyright © 2003 John Wiley & Sons, Ltd.     

基于AHP的Web集群系统负载均衡算法

利用集群技术构建的服务器组在资源利用率上存在较大差异。为此,提出基于层次分析法(AHP)的集群系统负载均衡算法,建立判断矩阵,得到各项评估指标的单项和综合权重。调度器定时接收真实服务器上的4类参数：网络性能,服务器硬件,服务器软件和网络服务类型。根据调度器收到的每一个连接请求,采用动态反馈算法选择负载最小的服务器响应连接请求。实验结果表明,该算法能减少服务器平均响应时间,有效提高集群系统的响应率。     

基于异构分布式存储系统的动态反馈负载均衡技术

Samba分布式存储系统通过根服务器向用户提供全局名字空间,当用户提出访问请求时,根服务器只按照静态的方式返回逻辑名对应的物理目标位置.当存在多个物理目标时,大多数用户的访问请求都将被定位在一台服务器上.多目标只保证了服务可用性,而没有对系统的负载均衡作出贡献.针对这一问题,提出了一种基于服务器性能指标的动态反馈负载均衡策略,并给出了基于Samba分布式存储系统的实现方案,旨在提高整个系统的存储能力、网络吞吐率、服务平均响应时间等指标.实验表明,优化后系统的I/O性能得到了很大提升.     

CBC: Caching for cloud-based VOD systems

Cloud-based video on demand (VOD) service is a promising next-generation media streaming service paradigm. Being a resource-intensive application, how to maximize resource utilization is a key issue of designing such an application. Due to the special cloud-based VOD system architecture consisting of cloud storage cluster and media server cluster, existing techniques such as traditional caching strategies are inappropriate to be adopted by a cloud-based VOD system directly in practice. Therefore, in this study, we have proposed a systemic caching scheme, which seamlessly integrates a caching algorithm and a cache deployment algorithm together to maximize the resources utilization of cloud-based VOD system. Firstly, we have proposed a cloud-based caching algorithm. The algorithm models the cloud-based VOD system as a multi-constraint optimization problem, so as to balance the resource utilization between cloud storage cluster and media server cluster. Secondly, we have proposed a cache deployment algorithm. The algorithm further manages the bandwidth and cache space resource utilization inside the media server cluster in a more fine-grained manner, and achieves load balancing performance. Our evaluation results show that the proposed scheme enhances the resource utilization of the cloud-based VOD system under resource-constrained situation, and cuts down the reject ratio of user requests.     

On the Design of Distributed Object Placement and Load Balancing Strategies in Large-Scale Networked Multimedia Storage Systems

In a large-scale multimedia storage system (LMSS) where client requests for different multimedia objects may have different demands, the placement and replication of the objects is an important factor, as it may result in an imbalance in server loading across the system. Since replication management and load balancing are all the more crucial issues in multimedia systems, in the literature, these problems are handled by centralized servers. Each object storage server (OSS) responds to the requests that come from the centralized servers independently and has no communication with other OSSs in the system. In this paper, we design a novel distributed load balancing strategy for LMSS, in which OSSs can cooperate to achieve higher performance. Such OSS modeled as an M/G/m system can replicate the objects to and balance the requests among other servers to achieve a near-optimal average waiting time (AWT) of the requests in the system. We validate the performance of the system via rigorous simulations with respect to several influencing factors and prove that our proposed strategy is scalable, flexible, and efficient for real-life applications.     

Efficient, proximity-aware load balancing for DHT-based P2P systems

Many solutions have been proposed to tackle the load balancing issue in DHT-based P2P systems. However, all these solutions either ignore the heterogeneity nature of the system, or reassign loads among nodes without considering proximity relationships, or both. In this paper, we present an efficient, proximity-aware load balancing scheme by using the concept of virtual servers. To the best of our knowledge, this is the first work to use proximity information in load balancing. In particular, our main contributions are: 1) relying on a self-organized, fully distributed k-ary tree structure constructed on top of a DHT, load balance is achieved by aligning those two skews in load distribution and node capacity inherent in P2P systems - that is, have higher capacity nodes carry more loads; 2) proximity information is used to guide virtual server reassignments such that virtual servers are reassigned and transferred between physically close heavily loaded nodes and lightly loaded nodes, thereby minimizing the load movement cost and allowing load balancing to perform efficiently; and 3) our simulations show that our proximity-aware load balancing scheme reduces the load movement cost by 11-65 percent for all the combinations of two representative network topologies, two node capacity profiles, and two load distributions of virtual servers. Moreover, we achieve virtual server reassignments in O(log N) time.     

一个企业资源综合管理系统的研究设计与实现

设计了一个企业资源管理系统,并实现了动态的资源调度算法,能够综合利用计算能力、存储能力都达到中小型服务器水平的普通计算机,作为后备资源,缓冲大规模并发的用户请求带给服务器的压力,从而提高整个系统的性能、吞吐量和资源利用率,并优化物理资源的负载均衡。     

WEB服务器的负载研究与处理

WEB服务器过载是导致“网络拥塞”的根源,严重制约了动态Web技术的应用和发展。文章通过改变IIS服务器程序处理过程,,利用分布式缓冲池页面处理方式,结合调度池算法和重定向技术,提出了一种ISAPI的自适应负载均衡处理系统集成方案,减轻Web服务器的负载,加速并均衡了对客户的响应,大大缓解了“网络拥塞”现象。     

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