基于CPU MEM的负载共享调度机制研究*

分布式系统中调度机制对负载共享系统性能有重要影响。基于CPU-MEM的负载共享策略考虑内存资源对系统性能的作用,降低了页失效次数,提高了资源利用率。在CPU-MEM负载共享机制基础上,考虑任务在执行过程中的变化特性,提出了在单节点上减少任务平均内存需求的多内存需求多时间片轮询策略(RR-MMMCS)和基于预测的多内存多时间片策略(MMMCS-P)。实验表明,无论是对计算密集型任务还是数据密集型任务,RR-MMMCS、MMMCS-P调度机制在平均响应时间方面具有较好的性能。     

基于预测机制的自适应负载均衡算法

工作负载特征对Web服务器集群中负载均衡调度算法的性能有重要影响。针对负载特征在调度算法所起作用的分析和讨论,提出基于预测机制的自适应负载均衡算法（RR_MMMCS-A-P）。通过监测工作负载,预测后续请求到达率和请求大小,快速调整相应参数,实现集群中各服务器之间的负载均衡。实验表明,无论是对计算密集型任务还是数据密集型任务,RR_MMMCS-A-P同基于CPU和CPU-MEM的调度算法相比在缩短平均响应时间方面具有较好的性能。     

一种基于实时负载的动态负载平衡策略

在分布式系统中采用动态负载平衡算法分配系统中的工作负载,能够提高系统的性能。在简述目前常用的几种动态负栽平衡策略的基础上,提出了一种基于实时负载的动态负载平衡策略,并给出了其调度算法。     

一种共享存储视频服务器集群的请求调度策略

在VOD服务器集群中,对用户服务请求的合理调度是提高集群整体性能的关键技术之一.本文针对共享存储结构下的服务器机群,在请求调度算法LoadCache-rep基础上进行改进,提出一种基于视频节目点播集中度的调度策略,该策略通过将相近的点播请求调度至相同服务器上以充分利用服务器的缓存机制,同时兼顾均衡各服务器间的负载.并根据实时负载变化对请求进行迁移以消除VCR操作对负载分布的影响.仿真试验表明,该策略能有效提高视频服务器集群的运行性能.     

基于中间件的动态负载均衡策略的研究

随着分布式系统应用的不断发展,采用负载均衡策略以提高分布式系统的整体性能越来越重要.在分析前人研究成果的基础上,实现了基于中间件技术的动态负载均衡策略.以资源利用率和节点的调和性能平均值为评价指标,该策略综合衡量系统的负载,量化异构系统中各节点的性能,使得负载具有可比性.通过实时监测各节点的负载情况,并及时调整负载策略的相关参数,可使系统运转达到最优.     

面向Web Service的动态负载均衡设计与实现

负载均衡是分布式系统的资源管理模块,它的主要功能是合理和透明地在服务器之间分配系统负载,以达到系统的综合性能最优。基于中间件的负载均衡技术在整合异构系统、透明访问和扩展能力等方面具有优势。在中间模块上可以灵活实现多种负载均衡算法,来调整和满足不同应用的负载均衡需求,提高系统的扩展性、配置性、健壮性。本文参考分布式系统中的模型,构建了基于Web Service的负载均衡器,实现了动态负载均衡调度策略。为了准确地描述系统的负载,选择CPU利用率、内存利用率、系统响应时间、输入输出流量和进程数作为衡量系统负载的参量;为了避免因监测服务器而加大均衡器的压力,策略中将负载信息采集程序运行在机群内的各服务结点上,当相邻采集周期的负载差值超过标准值后调用均衡器上的Web服务,报告自身的负载状况,使均衡器根据负载信息进行同步操作,更换服务序列。最后通过仿真实验证明本策略在异构Web平台中具有可行性。     

负载感知的存储子系统调优研究

存储IO的性能远远低于CPU、内存的性能，而且它们之间的差距还在扩大。因此，对于越来越多的数据密集型应用系统来说，存储系统往往是系统瓶颈。存储的性能不仅与存储子系统体系结构以及子系统中各部件的性能相关，还与系统的工作负载和应用环境相关。负载感知的性能调优指存储子系统通过对负载特征的分析实现对应用环境的动态感知，并根据负载特征动态调整系统运行策略。负载感知的性能调优使得存储子系统能够更合理地调度存储系统资源，从而提高IO性能。     

面向大规模数据接入系统的负载平衡机制

当前分布式系统负载平衡算法存在问题：1）算法建立的系统中各节点角色固定,系统不具有自适应性;2）算法的通用性不高;3）负载迁移任务巨大,且负载平衡周期过长等。针对这些问题,提出了混合式负载平衡算法。首先,设计了一个分布式系统接收模型。模型将系统任务分为三层：接收层、处理层和存储层。在接收层使用了自定义的通信协议提高系统的接收性能。然后,负载平衡算法采用随机负载迁移策略,根据系统中节点的负载状态,对负载任务进行随机迁移。通过这种策略解决负载平衡周期过长和负载回迁问题。最后,通过分布式控制节点选择策略,使系统中节点具有自适应性。实验结果显示,在百万数据源以下,系统各层平均延迟处于毫秒级,系统负载平衡平均耗时在3 min以下。实验证明了所提出的负载平衡机制具有周期短、任务响应迅速等特点,能够提高分布式系统的接收性能。     

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。     

基于多路规划遗传算法的负载均衡方法

在负载均衡问题中,负载调度方法足核心,它的好坏直接影响均衡系统的性能.提出一种基于多路规划遗传算法的服务器端负载均衡算法.该方法借鉴生物界自然选择和自然遗传机制,模拟自然进化过程搜索最优解,为负载均衡问题提供了新的计算模型.同时,多路规划(多次交叉或变异)后取最优策略的应用,使得多路规划遗传算法的优化性能大为提高.该方法降低了服务器端请求的响应时间,提高了服务器端CPU的利用率,从而改善了系统性能.数据实例表明,该方法是可行的、正确的和有效的.     

分时EDF算法及其在多媒体操作系统中的应用

提出了一种新的CPU调度算法－－分时EDF（Earliest Deadine First)算法,该算法能保证硬实时任务不丢失死线,并易于在分时系统中实现。以分时EDF算法为基础,提出一种新的CPU层次调度算法－－HRFSFQ,该算法用于多媒体操作系统时能保证各类任务的QoS。最后通过大量实验证明了上述算法的有效性和正确性。     

Linux实时调度方案的设计与实现

作为一个分时系统，Linux的设计目标为提高吞吐率和平均响应时间，因此采取了基于时间片的调度策略，显然这种机制无法保证实时任务得到及时响应和调度．为了改进Linux操作系统的实时性能，探讨了基于Linux的实时操作系统RFRTOS中的实时调度机制．实验验证，所做改进有效地提高了Linux的调度精度，满足了软实时方面的需求．     

网格环境中节点CPU负载的分形预测

网格环境下，常常需要知道网格资源在未来某一时刻具有什么样的性能，比如，调度器需要该性能估测以便进行高效的资源调度、提供满足要求的QoS以及保证整个网格系统的负载平衡。正如在其他任何计算环境中一样，计算能力是所有网格资源中最为重要的资源，通常用CPU负载来刻画节点主机的忙碌程度、衡量节点所能提供的计算能力。已有的研究表明CPU负载具有自相似性和长相关性，这启发我们使用本文介绍的分形的方法进行CPU负载的预测。实验结果证明该方法具有较高的预测精度，因而具有较好的实用价值。     

Fair Scheduling Algorithms in Grids

In this paper, we propose a new algorithm for fair scheduling, and we compare it to other scheduling schemes such as the earliest deadline first (EDF) and the first come first served (FCFS) schemes. Our algorithm uses a max-min fair sharing approach for providing fair access to users. When there is no shortage of resources, the algorithm assigns to each task enough computational power for it to finish within its deadline. When there is congestion, the main idea is to fairly reduce the CPU rates assigned to the tasks so that the share of resources that each user gets is proportional to the users weight. The weight of a user may be defined as the users contribution to the infrastructure or the price he is willing to pay for services or any other socioeconomic consideration. In our algorithms, all tasks whose requirements are lower than their fair share CPU rate are served at their demanded CPU rates. However, the CPU rates of tasks whose requirements are larger than their fair share CPU rate are reduced to fit the total available computational capacity in a fair manner. Three different versions of fair scheduling are adopted in this paper: the simple fair task order (SFTO), which schedules the tasks according to their respective fair completion times, the adjusted fair task order (AFTO), which refines the SFTO policy by ordering the tasks using the adjusted fair completion time, and the max-min fair share (MMFS) scheduling policy, which simultaneously addresses the problem of finding a fair task order and assigning a processor to each task based on a max-min fair sharing policy. Experimental results and comparisons with traditional scheduling schemes such as the EDF and the FCFS are presented using three different error criteria. Validation of the simulations using real experiments of tasks generated from 3D image- rendering processes is also provided. The three proposed scheduling schemes can be integrated into existing grid computing architectures.     

An efficient scheduling scheme using estimated execution time for heterogeneous computing systems

Computing systems should be designed to exploit parallelism in order to improve performance. In general, a GPU (Graphics Processing Unit) can provide more parallelism than a CPU (Central Processing Unit), resulting in the wide usage of heterogeneous computing systems that utilize both the CPU and the GPU together. In the heterogeneous computing systems, the efficiency of the scheduling scheme, which selects the device to execute the application between the CPU and the GPU, is one of the most critical factors in determining the performance. This paper proposes a dynamic scheduling scheme for the selection of the device between the CPU and the GPU to execute the application based on the estimated-execution-time information. The proposed scheduling scheme enables the selection between the CPU and the GPU to minimize the completion time, resulting in a better system performance, even though it requires the training period to collect the execution history. According to our simulations, the proposed estimated-execution-time scheduling can improve the utilization of the CPU and the GPU compared to existing scheduling schemes, resulting in reduced execution time and enhanced energy efficiency of heterogeneous computing systems.     

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     

Adaptive hierarchical scheduling policy for enterprise grid computing systems

In an enterprise grid computing environments, users have access to multiple resources that may be distributed geographically. Thus, resource allocation and scheduling is a fundamental issue in achieving high performance on enterprise grid computing. Most of current job scheduling systems for enterprise grid computing provide batch queuing support and focused solely on the allocation of processors to jobs. However, since I/O is also a critical resource for many jobs, the allocation of processor and I/O resources must be coordinated to allow the system to operate most effectively. To this end, we present a hierarchical scheduling policy paying special attention to I/O and service-demands of parallel jobs in homogeneous and heterogeneous systems with background workload. The performance of the proposed scheduling policy is studied under various system and workload parameters through simulation. We also compare performance of the proposed policy with a static space–time sharing policy. The results show that the proposed policy performs substantially better than the static space–time sharing policy.