Web应用服务器自适应负载平衡服务

Web应用服务器是为事务性Web应用提供一系列运行时服务的分布式系统.基于中间件的自适应负载平衡服务是为Web应用服务器提供高可信赖性和高伸缩性的一种有效方法,但目前还存在许多不足,如缺乏服务端透明性、负载策略不可替换等,不能满足Web应用服务器特有的需求.分析了Web应用服务器负载平衡服务的关键需要,设计了一种自适应负载平衡服务,阐述了在J2EE应用服务器WebFrame2.0上实现该服务的若干关键技术及其解决办法,包括可热插拔、负载策略可替换、负载反馈与自适应控制、状态迁移以及容错技术等,最后是相关工作介绍及其比较.该负载平衡服务已在Web应用服务器WebFrame2.0中得以实现.     

基于LVS＋KEEPALIVED的高可用负载均衡研究与应用

本文针对网络服务商普遍面临的的高并发访问量导致响应速度迟缓的问题进行分析,研究了基于LVS（Linux虚拟服务器）和KEEPALIVED（交换机制软件）的高可用负载均衡的原理,实现了一个可伸缩的、高可用的并具有容错容灾能力的负载均衡系统,该系统采用VS/DR的模式搭建,系统基于IP层和基于内容请求分发的负载平衡调度解决算法,将这些算法在Linux内核中进行实现,并对系统的高可用和负载均衡进行实验,实验证明,该负载均衡系统能对客户的请求进行分流,能有效减轻单个服务器的负载压力.     

Load balancing and data placement for multi-tiered database systems

A materialized view or Materialized Query Table (MQT) is an auxiliary table with precomputed data that can be used to significantly improve the performance of a database query. A Materialized Query Table Advisor (MQTA) is often used to recommend and create MQTs. The state-of-the-art MQTA works in a standalone database server where MQTs are placed on the same server as that in which the base tables are located. The MQTA does not apply to a federated or scaleout scenario in which MQTs need to be placed on other servers close to applications (i.e. a frontend database server) for offloading the workload on the backend database server. In this paper, we propose a Data Placement Advisor (DPA) and load balancing strategies for multi-tiered database systems. Built on top of the MQTA, DPA recommends MQTs and advises placement strategies for minimizing the response time for a query workload. To demonstrate the benefit of the data placement advising, we implemented a prototype of DPA that works with the MQTA in the IBM^® DB2^® Universal Database™ (DB2 UDB) and the IBM WebSphere^® Information Integrator (WebSphere II). The evaluation results showed substantial improvements of workload response times when MQTs are intelligently recommended and placed on a frontend database server subject to space and load characteristics for TPC-H and OLAP type workloads.     

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.     

A trace-driven simulation study of dynamic load balancing

A trace-driven simulation study of dynamic load balancing in homogeneous distributed systems supporting broadcasting is presented. Information about job CPU and input/output (I/O) demands collected from production systems is used as input to a simulation model that includes a representative CPU scheduling policy and considers the message exchange and job transfer cost explicitly. Seven load-balancing algorithms are simulated and their performances compared. Load balancing is capable of significantly reducing the mean and standard deviation of job response times, especially under heavy load, and for jobs with high resource demands. Algorithms based on periodic or nonperiodic load information exchange provide similar performance, and, among the periodic policies, the algorithms that use a distinguished agent to collect and distribute load information cut down the overhead and scale better. With initial job placements only, source initiative algorithms were found to perform better than server initiative algorithms. The performances of all hosts, even those originally with light loads, are generally improved by load balancing     

A new fuzzy-decision based load balancing system for distributed object computing

Distributed object computing systems are widely envisioned to be the desired distributed software development paradigm due to the higher modularity and the capability of handling machine and operating system heterogeneity. Indeed, enabled by the tremendous advancements in processor and networking technologies, complex operations such as object serialization and data marshaling have become very efficient, and thus, distributed object systems are being built for many different applications. However, as the system scales up (e.g., with larger number of server and client objects, and more machines), a judicious load balancing system is required to efficiently distribute the workload (e.g., the queries, messages/objects passing) among the different servers in the system. Unfortunately, in existing distributed object middleware systems, such a load balancing facility does not exist. In this paper, we present the design and implementation of a new dynamic fuzzy-decision-based load balancing system incorporated in a distributed object computing environment. Our proposed approach works by using a fuzzy logic controller which informs a client object to use the most appropriate service such that load balancing among servers is achieved. We have chosen Jini to build our experimental middleware platform, on which our proposed approach as well as other related techniques are implemented and compared. Extensive experiments are conducted to investigate the effectiveness of our fuzzy-decision-based algorithm, which is found to be consistently better than other approaches.     

Adaptive and scalable load balancing for metadata server cluster in cloud-scale file systems

Big data is an emerging term in the storage industry, and it is data analytics on big storage, i.e., Cloud-scale storage. In Cloud-scale (or EB-scale) file systems, load balancing in request workloads across a metadata server cluster is critical for avoiding performance bottlenecks and improving quality of services.Many good approaches have been proposed for load balancing in distributed file systems. Some of them pay attention to global namespace balancing, making metadata distribution across metadata servers as uniform as possible. However, they do not work well in skew request distributions, which impair load balancing but simultaneously increase the effectiveness of caching and replication. In this paper, we propose Cloud Cache (C²), an adaptive and scalable load balancing scheme for metadata server cluster in EB-scale file systems. It combines adaptive cache diffusion and replication scheme to cope with the request load balancing problem, and it can be integrated into existing distributed metadata management approaches to efficiently improve their load balancing performance. C² runs as follows: 1) to run adaptive cache diffusion first, if a node is overloaded, loadshedding will be used; otherwise, load-stealing will be used; and 2) to run adaptive replication scheme second, if there is a very popular metadata item (or at least two items) causing a node be overloaded, adaptive replication scheme will be used, in which the very popular item is not split into several nodes using adaptive cache diffusion because of its knapsack property. By conducting performance evaluation in trace-driven simulations, experimental results demonstrate the efficiency and scalability of C².     

Load Balancing in a Cluster-Based Web Server for Multimedia Applications

We consider a cluster-based multimedia Web server that dynamically generates video units to satisfy the bit rate and bandwidth requirements of a variety of clients. The media server partitions the job into several tasks and schedules them on the backend computing nodes for processing. For stream-based applications, the main design criteria of the scheduling are to minimize the total processing time and maintain the order of media units for each outgoing stream. In this paper, we first design, implement, and evaluate three scheduling algorithms, first fit (FF), stream-based mapping (SM), and adaptive load sharing (ALS), for multimedia transcoding in a cluster environment. We determined that it is necessary to predict the CPU load for each multimedia task and schedule them accordingly due to the variability of the individual jobs/tasks. We, therefore, propose an online prediction algorithm that can dynamically predict the processing time per individual task (media unit). We then propose two new load scheduling algorithms, namely, prediction-based least load first (P-LLF) and prediction-based adaptive partitioning (P-AP), which can use prediction to improve the performance. The performance of the system is evaluated in terms of system throughput, out-of-order rate of outgoing media streams, and load balancing overhead through real measurements using a cluster of computers. The performance of the new load balancing algorithms is compared with all other load balancing schemes to show that P-AP greatly reduces the delay jitter and achieves high throughput for a variety of workloads in a heterogeneous cluster. It strikes a good balance between the throughput and output order of the processed media units     

网络计算机集群负载均衡机制的研究

目前使用单台网络计算机应用服务器难以满足大量用户的并发访问需求,在网络计算机系统中引入集群/负载均衡技术是解决这一问题的理想途径.研究网络计算机集群的负载均衡机制.设计和实现了适合于网络计算机应用模式的负载评估方法及负载状态更新机制,并在此基础上提出和实现了一种基于分布式协商的动态负载均衡算法.     

Tight Bounds for Selfish and Greedy Load Balancing

We study the load balancing problem in the context of a set of clients each wishing to run a job on a server selected among a subset of permissible servers for the particular client. We consider two different scenarios. In selfish load balancing, each client is selfish in the sense that it chooses, among its permissible servers, to run its job on the server having the smallest latency given the assignments of the jobs of other clients to servers. In online load balancing, clients appear online and, when a client appears, it has to make an irrevocable decision and assign its job to one of its permissible servers. Here, we assume that the clients aim to optimize some global criterion but in an online fashion. A natural local optimization criterion that can be used by each client when making its decision is to assign its job to that server that gives the minimum increase of the global objective. This gives rise to greedy online solutions. The aim of this paper is to determine how much the quality of load balancing is affected by selfishness and greediness.     

HPC Cloud环境中基于网络Ⅰ/O负载的虚拟机放置算法

随着虚拟化技术和云计算技术的发展,越来越多的高性能计算应用运行在云计算资源上.在基于虚拟化技术的高性能计算云系统中,高性能计算应用运行在多个虚拟机之中,这些虚拟机可能放置在不同的物理节点上.若多个通信密集型作业的虚拟机放置在相同的物理节点上,虚拟机之间将竞争物理节点的网络Ⅰ/O资源,如果虚拟机对网络Ⅰ/O资源的需求超过物理节点的网络Ⅰ/O带宽上限,将严重影响通信密集型作业的计算性能.针对虚拟机对网络Ⅰ/O资源的竞争问题,提出一种基于网络Ⅰ/O负载均衡的虚拟机放置算法NLPA,该算法采用网络Ⅰ/O负载均衡策略来减少虚拟机对网络Ⅰ/O资源的竞争.实验表明,与贪心算法进行比较,对于同样的高性能计算作业测试集,NLPA算法在完成作业的计算时间、系统中的网络Ⅰ/O负载吞吐率、网络Ⅰ/O负载均衡3个方面均有更好的表现.     

基于软件抗衰的分布式负载均衡策略

随着网络的迅速发展,服务器集群技术得到了广泛的应用,对负载均衡策略的研究也变得越来越必要,但当前的分布式负栽均衡策略始终存在性能和开销不能兼顾的问题。本文将软件抗衰思想引入负载均衡策略设计,根据系统内的均衡程度来确定均衡过程的起止时机,在一定程度上解决了这一矛盾。文内给出了相应的实现算法。     

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。     

一种LVS服务器权值的动态调节方案

以LVS为基础，实现了一个负载动态均衡方案。该方案对整个系统的负载均衡情况作出评估，选出要被调整的服务器集合，然后根据服务器的负载情况采用负反馈的方法对这些服务器的权值作出适当调整，从而实现了Web服务器集群系统的动态负载均衡。     

基于预测机制的分级负载均衡算法

为解决服务器集群负载分配不均的问题,根据用户访问的请求类型,综合考虑用户历史请求引起的负载增量和服务器节点性能,提出了基于预测机制的分级负载均衡算法。负载均衡节点根据用户访问的请求类型建立一次指数平滑预测模型,对相应请求类型引起的负载进行预测,并将预测负载划分为低负载、正常负载、重负载等三个负载等级,根据负载等级对用户请求进行调度,从而实现负载均衡。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。     

服务器集群负载均衡的建模与仿真研究

研究服务器集群负载优化调试问题,各服务器负载能力差异较大,要求尽可能使每一个服务器的负载均衡,传统方法没有考虑负载动态变化特点,导致服务器集群负载极不均衡,系统性能差。为提高集群系统的整体性能,提出一种基于遗传算法的服务器集群负载均衡算法。首先根据负载均衡目标建立数学模型,然后采用遗传算法模型进行求解。仿真结果表明,遗传算法提高了服务器集群系统吞吐量,使系统负载更加均衡,使整个集群系统的资源得到充分利用。     

An adaptive load balancing management technique for RFID middleware systems

In recent years, Radio Frequency Identification (RFID) industries have taken a great interest in utilizing the benefits of RFID for supply chain management, inventory control and various other applications. This paper proposed an adaptive load balancing technique for RFID middleware systems to meet the demands of scalability and heterogeneity. First, we explored five basic load balancing policies, namely, information policy, job selection policy, transfer policy, initiation policy and location policy. Eighteen load balancing schemes were then proposed for RFID middleware systems that were combinations of various types of the five basic load balancing policies. Our empirical study suggested that these load balancing strategies performed differently under different workload statuses. Finally, an adaptive load balancing strategy was proposed. The load balancing schemes and the proposed adaptive load balancing strategy have been implemented in the RFID Middleware Load Management System (RM‐LMS). Copyright © 2010 John Wiley & Sons, Ltd.     

集群服务器IPv4/IPv6接入机制的设计与实现

设计了一种将集群服务器NVS(network virtual server)接入到IPv4/IPv6环境中的机制,通过将协议转换内置到NVS接口机的内核中以提高协议处理速度,实现应用对底层具体协议的透明使用,原有高可用性、高可扩展性和负载均衡等多接口机软件不加修改即可使用。实验结果表明,该机制协议处理延迟小于通过NAT-PT网关实现的延迟,同时具有更高的吞吐量。     

A message passing benchmark for unbalanced applications

We present a distributed memory parallel implementation of the unbalanced tree search (UTS) benchmark using MPI and investigate MPI’s ability to efficiently support irregular and nested parallelism through continuous dynamic load balancing. Two load balancing methods are explored: work sharing using a centralized work server and distributed work stealing using explicit polling to service steal requests. Experiments indicate that in addition to a parameter defining the granularity of load balancing, message-passing paradigms require additional techniques to manage the volume of communication and mitigate runtime overhead. Using additional parameters, we observed an improvement of up to 3–4X in parallel performance. We report results for three distributed memory parallel computer systems and use UTS to characterize the performance and scalability on these systems. Overall, we find that the simpler work sharing approach with a single work server achieves good performance on hundreds of processors and that our distributed work stealing implementation scales to thousands of processors and delivers more robust performance that is less sensitive to the particular workload and load balancing parameters.     

Prediction of job characteristics for intelligent resource allocation in HPC systems: a survey and future directions

Nowadays, high-performance computing (HPC) clusters are increasingly popular. Large volumes of job logs recording many years of operation traces have been accumulated. In the same time, the HPC cloud makes it possible to access HPC services remotely. For executing applications, both HPC end-users and cloud users need to request specific resources for different workloads by themselves. As users are usually not familiar with the hardware details and software layers, as well as the performance behavior of the underlying HPC systems. It is hard for them to select optimal resource configurations in terms of performance, cost, and energy efficiency. Hence, how to provide on-demand services with intelligent resource allocation is a critical issue in the HPC community. Prediction of job characteristics plays a key role for intelligent resource allocation. This paper presents a survey of the existing work and future directions for prediction of job characteristics for intelligent resource allocation in HPC systems. We first review the existing techniques in obtaining performance and energy consumption data of jobs. Then we survey the techniques for single-objective oriented predictions on runtime, queue time, power and energy consumption, cost and optimal resource configuration for input jobs, as well as multi-objective oriented predictions. We conclude after discussing future trends, research challenges and possible solutions towards intelligent resource allocation in HPC systems.