并行I/O中一种基于服务时间最小变化的文件分配策略

论文中对于文件访问的服务时间进行了较深入的研究,提出一种并行I/O文件分配算法——启发式文件分类分配策略,它在负载基本均衡前提下,按照相似的访问服务时间对每个待分配的数据文件进行磁盘分配。通过对启发式文件分类分配策略与已有的贪婪文件分配法进行实验比较,结果表明:系统处理重负载时,访问响应时间提高了30%左右,而且数据访问速率越高,由启发式文件分类分配策略所提高的性能就越明显。     

多媒体文件服务器的磁盘带宽分配策略

讨论多媒体文件服务器磁盘I/O带宽的分配策略问题,提出一种根据不同应用请求的负载变化情况对服务器磁盘带宽进行自适应分配的算法。该算法由负载检测模块、带宽管理模块组成,提高了多媒体文件服务器的易管理性,在短暂的过载情况下保持了服务器的稳定性,为更多的软实时请求提供服务。     

一种流媒体系统中文件存储分配算法

随着集群搭建的高性能计算中心快速发展,海量数据处理中CPU和内存的速度已经有了质的飞跃,但是I／O部分的速度仍是制约整个系统性能的瓶颈。本论文目的在于通过深入研究流媒体系统的特性,来提取出一种高效、均衡的文件存储分配策略,增大服务并行度并且减小服务延时。首先对媒体文件进行特征评估,然后通过存储调度器进行磁盘间的均衡性平摊,进而应用更细粒度的分划。做到每个磁盘内部的存储分布优化。仿真结果验证了算法的有效性,这样的分配策略可使盘间、盘内在数据组织上更为合理,有效的提高了并行服务能力,提供了更加迅捷的用户体验。。     

一个基于分布式数据库系统的动态负载分配算法

负载分配算法能够通过在其结点间明智地再分配工作负载而提高分布式系统的性能．在本文中,我们提出了一个新的基于分布式数据库系统的动态负载分配算法．它能够根据系统负载状况、数据的分布和结点间的通信开销自适应地改变其参数和策略。对一个分布式数据库系统的模拟表明,该算法能比稳定的发送者启动自适应算法提供更好的稳定性和性能。     

弹性Web Cache集群的自适应负载均衡策略

在Web cache集群中,Web突发请求的频繁出现引发资源供给不足,造成系统性能显著下降.为有效处理Web突发请求,构建了同时使用本地资源和云资源的弹性Web cache集群.在弹性Web cache集群中,为提升系统性能,降低费用,提出一种自适应的负载模型.该模型可以动态自适应地调整,能够有效适用于异构Web cache集群.考虑到云结点的网络延迟,修正该模型得到云结点负载模型.基于以上负载模型,构造弹性Web cache集群的自适应负载均衡策略.与其他负载均衡策略相比较,使用该自适应负载均衡策略能够在弹性Web cache集群中实现高效的负载均衡.     

集群系统中的负载均衡问题的研究

集群中的负载均衡技术是为了及时调整现有处理结构当中的不平衡问题,使每个结点都能发挥其最好的功能,使集群资源通过网络互联达到充分的共享而提出的.详细讨论了现有的负载均衡策略中可能会遇到的问题及产生原因,并且针对当前两种负载均衡策略,提出了动静态调度策略相结合,采用静态分配、动态监测、及时反馈调整的综合性管理的机制.采用这种混合性策略后,能够提高集群系统的吞吐量,加强数据处理能力,提高响应速度,以较低的成本消除网络瓶颈和提升系统总体性能.     

基于动态权重的一致性哈希微服务负载均衡优化

随着互联网技术的发展,互联网服务器集群的负载能力正在面临着前所未有的挑战,实现合理的负载均衡策略尤为重要。为了使负载均衡达到最佳的效率,可以使用一致性哈希算法来对集群负载均衡系统进行负载分配。针对微服务架构的服务器集群场景,对该集群负载均衡的特性进行分析,提出一种基于虚拟节点的一致性哈希环的设计与分割方法及基于动态权值的分配策略,在一致性哈希算法的基础上,实现服务集群之间的负载转移,解决微服务集群中服务负载增多,导致服务之间负载不均衡的问题,防止其中某些服务因为负载压力过大,导致服务崩溃的问题。经实验表明,与传统的一致性哈希算法相比,改进后的负载均衡策略负载不均衡的概率是原来的31%;并且动态分配策略具有良好的负载均衡性能,有效地解决了微服务分布式架构的负载均衡问题。     

PC集群环境下大规模矩阵乘法算法的研究

开发适合集群系统的并行数值算法,可以解决更多的科学与工程计算问题.在PC集群环境下,提出对Cannon算法的改进方案,采用重叠技术,使数据的计算和通信在时间上重叠,达到时延隐藏的目的,克服了网络传输的通信瓶颈问题;还提出一种自适应负载反馈平衡策略,很好地解决了集群负载平衡.该算法在PC集群系统环境下测试,取得了较好的并行效率,在4个结点上和8个结点上,加速比分别达到3.77和7.93.     

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。     

一种结构化P2P 协议中的自适应负载均衡方法

提出一种自适应负载均衡方法,方法采用一种被动式结点负载统计方法生成局部负载视图;一种文件访问统计方法生成局部文件访问视图;当系统内结点负载存在差异时,重载结点把指向自身的逻辑链路迁移至指向局部负载视图中的轻载结点,通过减小重载结点入度和增加轻载结点入度来减小结点间负载差异;当结点的请求负载较高时,通过局部文件访问视图计算需要缓存的热点文件及目标结点,降低承载热点文件的结点请求负载.实验结果表明,在用户查询服从Zipf 分布的环境下,自适应负载均衡方法可使系统负载达到较好的均衡;缓存方法虽然在一定程度上增加了缓存和更新开销,但在一定条件下比查询消息命中缓存节省的网络开销要小,降低了系统的整体负载.     

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.     

A Heuristic Algorithm for Task Scheduling Based on Mean Load on Grid

Efficient task scheduling is critical to achieving high performance on grid computing environment. The task scheduling on grid is studied as optimization problem in this paper. A heuristic task scheduling algorithm satisfying resources load balancing on grid environment is presented. The algorithm schedules tasks by employing mean load based on task predictive execution time as heuristic information to obtain an initial scheduling strategy. Then an optimal scheduling strategy is achieved by selecting two machines satisfying condition to change their loads via reassigning their tasks under the heuristic of their mean load. Methods of selecting machines and tasks are given in this paper to increase the throughput of the system and reduce the total waiting time. The efficiency of the algorithm is analyzed and the performance of the proposed algorithm is evaluated via extensive simulation experiments. Experimental results show that the heuristic algorithm performs significantly to ensure high load balancing and achieve an optimal scheduling strategy almost all the time. Furthermore, results show that our algorithm is high efficient in terms of time complexity.     

MORM: A Multi-objective Optimized Replication Management strategy for cloud storage cluster

Effective data management is an important issue for a large-scale distributed environment such as data cloud. This can be achieved by using file replication, which efficiently reduces file service time and access latency, increases file availability and improves system load balancing. However, replication entails various costs such as storage and energy consumption for holding replicas. This article proposes a multi-objective offline optimization approach for replica management, in which we view the various factors influencing replication decisions such as mean file unavailability, mean service time, load variance, energy consumption and mean access latency as five objectives. It makes decisions of replication factor and replication layout with an improved artificial immune algorithm that evolves a set of solution candidates through clone, mutation and selection processes. The proposed algorithm named Multi-objective Optimized Replication Management (MORM) seeks the near optimal solutions by balancing the trade-offs among the five optimization objectives. The article reports a series of experiments that show the effectiveness of the MORM. Experimental results conclusively demonstrate that our MORM is energy effective and outperforms default replication management of HDFS (Hadoop Distributed File System) and MOE (Multi-objective Evolutionary) algorithm in terms of performance and load balancing for large-scale cloud storage cluster.     

Adaptive data replication strategy in cloud computing for performance improvement

Cloud computing is becoming a very popular word in industry and is receiving a large amount of attention from the research community. Replica management is one of the most important issues in the cloud, which can offer fast data access time, high data availability and reliability. By keeping all replicas active, the replicas may enhance system task successful execution rate if the replicas and requests are reasonably distributed. However, appropriate replica placement in a large-scale, dynamically scalable and totally virtualized data centers is much more complicated. To provide cost-effective availability, minimize the response time of applications and make load balancing for cloud storage, a new replica placement is proposed. The replica placement is based on five important parameters: mean service time, failure probability, load variance, latency and storage usage. However, replication should be used wisely because the storage size of each site is limited. Thus, the site must keep only the important replicas.We also present a new replica replacement strategy based on the availability of the file, the last time the replica was requested, number of access, and size of replica. We evaluate our algorithm using the CloudSim simulator and find that it offers better performance in comparison with other algorithms in terms of mean response time, effective network usage, load balancing, replication frequency, and storage usage.     

分布式文件系统元数据服务的负载均衡框架

请求负载均衡,是分布式文件系统元数据管理需要面对的核心问题.以最大化元数据服务器集群吞吐量为目标,在已有元数据管理层之上设计实现了一种分布式缓存框架,专门管理热点元数据,均衡不断变化的负载.与已有的元数据负载均衡架构相比,这种两层的负载均衡架构灵活度更高,对负载的感知能力更强,并且避免了热点元数据重新分布、迁移引起的元数据命名空间结构被破坏的情况.经观察分析,元数据尺寸小、数量大,预取错误元数据带来的代价远远小于预取错误数据带来的代价.针对元数据的以上鲜明特点,提出一种元数据预取策略和基于预取机制的元数据缓存替换算法,加强了上述分布式缓存层的性能,这种两层的元数据负载均衡框架同时考虑了缓存一致性的问题.最后,在一个真实的分布式文件系统中验证了框架及方法的有效性.     

眼科医疗影像文件存取下的HDFS负载均衡

在HDFS系统中,集群负载分配不均衡成为影响分布式文件存取速度的瓶颈。针对医院信息系统的负载现状,提出多属性双阈值决策的动态负载均衡算法,对分布式集群中使用HDFS默认的单属性评价、单阈值的负载均衡策略存在的缺陷加以改进。通过负载性能测试,对比证明运用多属性双阈值的负载均衡算法相比较HDFS默认的负载均衡更有利于将大量的影像负载数据相对均衡地分配到服务器集群中的各个节点上,大幅度地缩短了增加数据规模时数据服务器节点的平均响应时间,有利于提高HDFS集群整体的工作效率。     

基于HDFS的海量视频数据重分布算法

基于视频数据的分布式计算与基于文本类型数据的分布式计算存在很大的差异。视频数据本身是非结构化的,并且对于同样大小的视频,若其内容不同会导致任务执行消耗的时间也不同。对于简单的结构化数据,HDFS默认的负载均衡器能够解决负载均衡的问题。但是视频文件存在热点访问以及复杂度不一致的问题。使用HDFS默认的数据分布机制不能很好地解决计算负载均衡问题。因此提出了一种基于HDFS的海量视频数据重分布算法。首先对视频文件的访问次数以及历史视频分析对视频文件的访问时间进行记录;然后对数据进行量化之后将其加权作为该视频文件的负载度;最后使用文件置换手段将负载高的视频与低的视频进行置换,直到每个节点的负载达到均衡为止。实验结果表明,使用提出的数据重分布算法可以减少海量视频数据的处理时间。     

独立任务调度的启发式算法

任务调度是一个NP-hard问题,而且是并行与分布式计算中一个必不可少的组成部分,特别是在网格计算环境下任务调度更加复杂。该文提出了满足负载均衡的一个启发式任务调度算法。给出了选择处理机和任务的方法,以提高算法的效率。实验表明该算法是一个高效率的调度算法,并且几乎总是找到了最优调度方案。     

虚拟盘阵系统中的自适应负载平衡

集群环境中经常采用虚拟盘阵方式来构建其存储系统。虚拟盘阵系统是一种并行系统,负载平衡对其性能影响非常大;同时虚拟盘阵系统一般都是异构的。本文研究了异构盘阵的负载平衡标准,并提出了基于请求的负载重构策略,在负载重构时机上对传统磁盘冷却算法进行了改进。模拟试验表明,该算法对虚拟异构盘阵是有效的。