云计算环境下虚拟机资源均衡调度方法研究

虚拟机资源是云计算环境下的一类主要云服务资源,有效的资源调度方法对于改善负载均衡和提高资源利用率具有重要意义.针对虚拟机资源的调度问题进行分析,利用任务到达触发任务分配,当任务到达时,首先判断云服务资源池的运行状态,分为饱和与不饱和两种;然后针对当前任务请求下的云服务资源池状态,分别给出资源调度策略,并通过算例分析验证所提方法的有效性.所提虚拟机资源调度方法将有助于云服务资源池中虚拟机资源的均衡利用.     

基于改进匈牙利算法的云仿真资源调度

针对传统仿真系统平台的资源分配存在资源闲置、任务挤压和负载均衡等优化问题,利用云计算技术的优势研究并提出了模块化的云仿真平台框架,通过对云仿真资源调度策略研究,提出了一种改进的匈牙利算法．该算法克服了传统匈牙利算法只适用于一对一资源调度的不足,实现了多对一的仿真任务与云仿真资源分配方案,能尽量避免资源调度负载失衡．通过扩展云计算仿真平台CloudSim实现了模拟算法仿真．结果表明．该调度策略能有效的减小云环境下计算机的负载,提高了资源的利用率．     

非侵入式Web请求加速平台研究

通过对非侵入式web请求加速平台的研究建设,要达到在不对原有系统做较大改动的前提下,有效加快web页面的加载速度,节约服务器、带宽等硬件资源的目的.从而提高系统性能,使得web应用能够更好地为企业提供信息化服务.因此,构建非侵入式web请求加速平台意义十分重大.     

基于云环境下的负载均衡算法研究

针对传统遗传算法易陷入局部解等缺点,提出了一种改进的元胞遗传算法,并将其运用到云环境下的负载均衡中.在云环境中,由于用户任务请求量大,会出现有些服务器过载,而有些服务器闲散的问题,利用该算法找到能提高服务器利用率的方法.在Clousim3.0仿真平台上进行试验,结果表明,该算法能提高服务器利用率,缩短任务完成时间.     

基于云计算的深度包检测技术研究

针对云计算平台的分布式、虚拟化等特点,从深度包检测技术的算法原理和实现框架两方面入手,研究如何将深度包检测技术引入云计算平台,提出深度包检测系统在云计算环境中的系统框架;其根据云后台硬件资源的异构性来智能地协调配置,并协作均衡处理和防御重复攻击,提高了整体效率;实验结果表明云计算框架比传统框架的深度包检测系统有效性和在时间、空间上的性能优势。     

网络负载均衡的控制理论及实践战略

网络负载均衡技术NLB（Network Load Balancing）采用完全分配算法来为集群中的服务器分配进入的访问流量,当集群中的某台服务器失效时,NLB会自动转发数据到其他可用的服务器上。负载均衡由多台服务器以对称的方式组成一个服务器集合,每台服务器都具有等价的地位,都可以单独对外提供服务而无须其他服务器的辅助。通过某种负载分担技术,将外部发送来的请求均匀分配到对称结构中的某一台服务器上,而接收到请求的服务器独立地回应客户的请求。均衡负载能够平均分配客户请求到服务器列阵,籍此提供快速获取重要数据,解决大量并发访问服务问题。     

基于云模型的负载均衡问题研究

云环境是一个复杂多变的环境,其不同层次提供不同的服务,在处理服务请求时其服务类型与服务器节点供给都是一个动态,随机的变化过程,如何高效的组织以及利用这些节点的处理能力,是该环境中负载技术应该考虑的关键问题.本文对云计算中私有云模型做了概括总结;并对负载均衡调度问题在不同平台中的研究现状做了相关描述,最后、指出了负载均衡调度问题在私有云中未来的研究方向.     

云呼叫中心系统中对虚拟化资源进行动态分配的方法

构建在云计算业务平台资源池上的云呼叫中心系统,不仅能够在业务层面上进行负载均衡控制,实现呼叫中心的智能化资源调度和分配,自动均衡处理来话负荷、座席签人负荷,支撑话务、服务资源的统一调度以及业务的统一运营管理;而且由于云呼叫中心业务系统部署在云数据中心资源池上,与其他应用可以共享基础设施,因此还需要实现资源动态伸缩分配.提出了一种云呼叫中心系统中对虚拟化资源进行动态分配的方法,这是一种根据资源池上层应用系统的运行情况决定资源池资源动态分配的方法,该方法包括云呼叫中心系统发起虚拟化资源动态分配请求的触发机制、云呼叫中心系统与资源管理平台之间进行资源动态分配的接口等.     

一种基于分布式服务器集群的可扩展负载均衡策略技术

提出了一种基于软件定义网络的分布式数据库负载均衡算法,将数据、控制、应用分离的同时计算服务器集群中单个服务器的实际负载.通过查询流量采样记录来决策最少连接的服务器路径,减少了访问请求的响应时间,提高了系统吞吐量和容错能力,实现了充分利用服务器资源的目的.内网中的分布式数据库实验对通用负载均衡技术和基于软件定义网络的负载均衡技术进行了比较,在不同服务器集群的负载状态下,后者的平均响应时间小于前者,并得到了更好的负载均衡效果.     

基于粒子群算法的云计算联盟负载均衡调度策略

云供应商与云用户的激增使得现在单一云供应商服务的商业模式不再具有市场竞争力。为了获取更大经济效益,各云计算服务商(Cloud Computing Service Provider,CCSP)合作形成了云计算联盟(Cloud Computing Federation)。文中提到的云计算联盟具有分布式、分级管理、快速收敛,易于延展和容灾的特性。CCSP以整体联盟的形式面向市场与客户,客户提交请求交给CCF,CCF再转交给具备服务能力的CCSP。为使用户请求得到快速响应,也为了保障联盟内CCSP的利益,除了CCSP内部的负载均衡外,文章采用粒子群算法对CCF的负载均衡调度策略进行研究,实验表明粒子群算法能够快速帮助CCF负载达到一个相对平衡状态。     

Estimation‐Based Load‐Balancing with Admission Control for Cluster Web Servers

The growth of the World Wide Web and web‐based applications is creating demand for high performance web servers to offer better throughput and shorter user‐perceived latency. This demand leads to widely used cluster‐based web servers in the Internet infrastructure. Load balancing algorithms play an important role in boosting the performance of cluster web servers. Previous load balancing algorithms suffer a significant performance drop under dynamic and database‐driven workloads. We propose an estimation‐based load balancing algorithm with admission control for cluster‐based web servers. Because it is difficult to accurately determine the load of web servers, we propose an approximate policy. The algorithm classifies requests based on their service times and tracks the number of outstanding requests from each class in each web server node to dynamically estimate each web server load state. The available capacity of each web server is then computed and used for the load balancing and admission control decisions. The implementation results confirm that the proposed scheme improves both the mean response time and the throughput of clusters compared to rival load balancing algorithms and prevents clusters being overloaded even when request rates are beyond the cluster capacity.     

An autonomic risk‐ and penalty‐aware resource allocation with probabilistic resource scaling mechanism for multilayer cloud resource provisioning

Cloud computing environment allows presenting different services on the Internet in exchange for cost payment. Cloud providers can minimize their operational costs by auto‐scaling of the computational resources based on demand received from users. However, the time and cost required to increase and decrease the number of active computational resources are among the biggest limitations of scalability. Thus, auto‐scaling is considered as one of the most important challenges in the field of cloud computing. The present study aimed to present a new solution to automatic scalability of resources for multilayered cloud applications under the Monitor‐Analysis‐Plan‐Execute‐Knowledge loop. In addition, the Google penalty payment model was used to model the penalty costs in the problem and to accurately evaluate the earned profit. A hybrid resource load prediction algorithm was proposed to evaluate the future of resources in each cloud layer. Further, we used statistical solution to determine the statuses of VMs in addition to presenting a risk‐aware algorithm to allocate the user requests to active resources. The experimental results by Cloudsim indicated the improvement of the proposed approach in terms of operational costs, the number of used resources, and the amount of profit.     

An adaptive load balancing scheme for web servers

This paper describes an overload control scheme for web servers which integrates admission control and load balancing. The admission control mechanism adaptively determines the client request acceptance rate to meet the web servers' performance requirements while the load balancing or client request distribution mechanism determines the fraction of requests to be assigned to each web server. The scheme requires no prior knowledge of the relative speeds of the web servers, nor the work required to process each incoming request. Copyright © 2002 John Wiley & Sons, Ltd.     

An efficient clustering and load balancing of distributed cloud data centers using graph theory

The prime focus of the Cloud Service Providers is enhancing the service delivery performance of the distributed cloud data centers. The clustering and load balancing of distributed cloud data centers have significant impact on its service delivery performance. Hence, this paper models distributed cloud data center environment as a network graph and proposes a two‐phase cluster‐based load balancing (CLB) algorithm based on a graph model. The first phase proposes a Cloud Data Center Clustering algorithm to cluster the distributed cloud data centers based on their proximity. The second phase proposes a Client‐Cluster Assignment algorithm to perform uniform distribution of the client requests across the clusters to enable load balancing. To assess the performance, the proposed algorithms are compared with other K‐constrained graph‐based clustering algorithms namely, graph‐based K‐means and K‐spanning tree algorithms on a simulated distributed cloud data center environment. The experimental results reveal that the proposed CLB algorithm outperforms the compared algorithms in terms of the average clustering time, load distribution, and fairness index and hence improves the service delivery performance of the distributed cloud data centers.     

基于mean-variance的服务集群负载均衡方法

大量并发请求任务进行分配时,负载调度机制是通过最小化响应时间及最大化节点利用率实现网络中节点的负载均衡,在基于遗传算法的负载均衡算法中,适应度函数设计对服务集群负载均衡效率产生重要的影响.对此提出了一种基于mean-variance的服务集群负载均衡方法对适应度函数进行优化,采用投资组合选择模型mean-variance进行最小化响应时间,以得到每个服务器资源利用率的权重,从而获得最优的分配组合,进而提高适应度函数的准确性和有效性.在不同服务环境下与其他模型进行比较,仿真结果表明,本文的负载均衡算法在节点利用率和响应时间方面使服务集群得到了更好的均衡.     

Integration of firefly optimization and Pearson service correlation for efficient cloud resource utilization

Cloud Computing (CC) environment presents a simplified, centralized platform or resources to usage while necessitated at minimum cost. In CC, the main processes in is the allocation of resources of web applications. However, with the increasing demands of Cloud User (CU), an efficient resource allocation technique for web applications is required. According to the request made by the user and response obtained, the cost of resources has also to be optimized. To overcome such limitations, Pearson service correlation‐based firefly resource cost optimization (PSC‐FRCO) technique is designed. Pearson service correlation‐based firefly resource cost optimization technique not only improves the performance of cost aware resource allocation but also achieves higher efficiency while rendering services in cloud computing environment for web applications. Pearson service correlation‐based firefly resource cost optimization technique initially uses Pearson service correlation in which the user‐required service is identified by correlating the available services provided by cloud owner. This helps in improving the Response Time (RT) of cloud service provisioning. Next, firefly resource cost optimization algorithm is applied to identify and allocate the cost‐optimized cloud resources to users to afford required service from the cloud server. Thus, PSC‐FRCO technique improves the Resource Utilization Efficiency (RUE) of web applications with minimal computational cost. This technique conducts experimental works on parameters such as RT, Bandwidth Utilization Rate (BUR) computational cost, Energy Consumption (EC), and RUE. Experimental analysis reveals that PSC‐FRCO technique enhances enhances RUE and lessens RT as compared to state‐of‐the‐art works.     

SD‐WLB: An SDN‐aided mechanism for web load balancing based on server statistics

Software‐defined networking (SDN) is a modern approach for current computer and data networks. The increase in the number of business websites has resulted in an exponential growth in web traffic. To cope with the increased demands, multiple web servers with a front‐end load balancer are widely used by organizations and businesses as a viable solution to improve the performance. In this paper, we propose a load‐balancing mechanism for SDN. Our approach allocates web requests to each server according to its response time and the traffic volume of the corresponding switch port. The centralized SDN controller periodically collects this information to maintain an up‐to‐date view of the load distribution among the servers, and incoming user requests are redirected to the most appropriate server. The simulation results confirm the superiority of our approach compared to several other techniques. Compared to LBBSRT, round robin, and random selection methods, our mechanism improves the average response time by 19.58%, 33.94%, and 57.41%, respectively. Furthermore, the average improvement of throughput in comparison with these algorithms is 16.52%, 29.72%, and 58.27%, respectively.     

基于排队论综合指标评估的动态负载均衡算法

互联网通信、计算机集群和云环境均具有一定的复杂性和动态性,极易发生负载失衡,从而降低服务效率、增加能耗。因此,负载均衡技术成为重点研究课题。现有的负载均衡策略均是以 CPU、内存、进程等参数的占用率来评估服务器当前的负载情况,但服务器负载情况的复杂性往往使其难以得到准确评估。针对该问题,提出了一种基于排队论综合指标评估的动态负载均衡算法,首先引入排队论模型评估各服务器的实时负载情况,然后根据各服务器的负载综合指标,将输入队列中的任务逐一分配给各服务器。实验结果表明,该方法可有效平衡各服务器的负载且减少任务请求的平均等待时间。     

云环境下一种基于任务类型匹配的负载均衡策略研究

针对云计算环境下大量并行计算节点容易产生计算节点之间的负载不均问题,本文提出了一种基于任务类型匹配的负载均衡方案。该方案针对任务集中的多种不同长度的子任务类型情况进行判定,并对当前主流的Max-Min和Min-Min两种启发式负载均衡算法进行分析,综合其优缺点,并针对任务集的类型采用不同的算法进行任务调度。实验结果表明在该负载均衡的策略下,提出的方案具有比单一应用Max-Min或者Min-Min算法具有更好的负载均衡特性和更短的完成时间。     

An Enhanced Load Balancing Approach for Dynamic Resource Allocation in Cloud Environments

Cloud computing is one of the distributed resource-sharing technology that offers resources on a pay-as-you-use basis. Platform as a service, Infrastructure as a service, and Software as a Service are services provided by the Cloud. Each end user's Quality of service must be ensured by the cloud service provider. In recent days, cloud utilization is rapidly increasing. To avoid congestion and to preserve the Service Level Agreement, the large workload must be balanced across the network. In this research work, a new load balancing approach is proposed for the dynamic resource allocation process to improve stability and to increase profit. PBMM algorithm is devised for an effective load balancing process through which, resource scheduling is performed. Task size and the bidding value coded by each customer are taken into account. To optimize the waiting time, resource tables and task tables are employed. The average waiting time and response time of the special users are minimized. The simulation results show that the proposed load balancing technique ensures the maximum profit and it enhances load balancing stability by increasing the number of special users.