一种动态并行I／O调度新策略的研究

并行I／O已经成为提高分布式计算系统性能的一种重要方式，论文在研究集群计算系统并行I／O数据调度策略的基础上，提出了一种应用于分布式计算系统中的二次调度自主维护负载平衡的动态I／O调度算法（DIO_TSMB），实验结果表明了算法的有效性，最后分析了并行I／O数据调度的发展趋势。     

RAID的并行I／O调度算法分析

由于越来越多的应用受限于I／O，存储系统正起着越来越重要的作用，磁盘阵列RAID是一种提供高性能I／O的最常见存储设备，本文分析了RAID并行I／O调度算法的I／O执行时间和磁盘利用率，为合理配置高性能阵列提供了依据。     

自主协助动态并行I/O调度策略研究

随着处理器速度和网络传输速度的快速提升，I／O成为限制计算机性能的一大瓶颈，并行I／O提供了解决这一问题的有效途径。在并行I／O的实现中，数据访问调度策略对系统的性能有着重要的影响。文章通过对集群计算中现有I／O数据访问调度策略的研究，针对调度器瓶颈问题，提出了自主协助动态I／O调度策略，实验结果表明了所提出策略的有效性。     

一种动态并行I/O调度新策略的研究

并行I/O已经成为提高分布式计算系统性能的一种重要方式,论文在研究集群计算系统并行I/O数据调度策略的基础上,提出了一种应用于分布式计算系统中的二次调度自主维护负载平衡的动态I/O调度算法(DIO_TSMB),实验结果表明了算法的有效性,最后分析了并行I/O数据调度的发展趋势。     

视频服务器存储子系统的I/O优化

视频服务器存储子系统的I／O性能决定了视频服务器的总体性能。本文提出的一种新的实时磁盘调度算法（LLF－Window)能有效地服务各种编码格式的视频流，并对传统的SCSI总线不均衡调度机制进行了改造，实验结果表明，新的磁盘调度算法和改造后的SCSI总线调度机制有效地改进了视频服务器存储子系统的I／O性能，保证了视频流的连续播放。     

一种集群计算系统中并行I/O文件存储分配策略

现代计算机系统性能已经由受限于CPU转变为受限于I／O，近年来研究人员对高性能计算中的并行I／O问题进行了深入研究．本论文的主要工作是对集群计算中的并行I／O子系统进行研究，提出一种文件拆分与存储分配的新策略，即：已知知识的文件拆分与分配方法(KKFDA)，该方法保证文件存储方式与访问方式的一致性，达到了提高文件访问时的本地命中率、缩短访盘响应时间的目的．算法设计与实验证明了方法的有效性．     

pSOS实时系统RAID驱动及调度实现

介绍了pSOS实时操作系统RAID（独立的冗余磁盘阵列）I/O调度策略的实现，并给出了几种高性能的RAID结构，最后是有关性能分析及测试方法。     

一种自适应的基于预测的I／O性能优化方法

目前计算机的I／O性能已经成为严重制约计算机整体性能提升的瓶颈。在现有技术条件下，I／O性能的优化方法对于提升I／O性能显得尤为重要。本文提出了一种自适应的基于预测的I／O性能的优化方法，该方法采用了基于预测的数据预读策略，改进I／O读操作的响应速度；采用基于预测的缓存分配策略，改进了I／O写操作的响应速度，从而在整体上提高了计算机系统的I／O性能。从实验测试的结果可以看出，这种方法适用于多种不同的负载类型，能够大幅度提升计算机的I／O性能．是一种通用的、效果显著的I／O性能优化方法。     

I／O调度策略实时化分析和设计

本文首先分析了ＵＮＩＸ的Ｉ／Ｏ调度策略，研究了调度机制和算法，并根据实时化的目标提出的新高度策略，然后分析和评价了新Ｉ／Ｏ调度策略，最后新策略的不足提出进一步的研究方案。     

可伸缩分布共享大规模并行I／O系统设计

如何有效地解决I／O瓶颈问题，一直是高性能并行计算机有待研究解决的关键技术。我们提出了一种可伸缩分布共享并行I／O系统方案，并自行研制了结点控制器芯片和路由器芯片，研制了原型系统SDSP604。为实现系统的计算、通讯和I／O性能随着系统规模均衡扩展的目标，该系统基于CC-NUMA系统结构，采用了合理的分布共享并行I／O系统结构。     

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.     

Energy-efficiency enhanced virtual machine scheduling policy for mixed workloads in cloud environments

Virtualization technology is an effective approach to improving the energy-efficiency in cloud platforms; however, it also introduces many energy-efficiency losses especially when I/O virtualization is involved. In this paper, we present an energy-efficiency enhanced virtual machine (VM) scheduling policy, namely Share-Reclaiming with Collective I/O (SRC-I/O), with aiming at reducing the energy-efficiency losses caused by I/O virtualization. The proposed SRC-I/O scheduler allows VMs to reclaim extra CPU shares in certain conditions so as to increase CPU utilization. Meanwhile, it separates I/O-intensive VMs from CPU-intensive ones and schedules them in a collective manner, so as to reduce the context-switching cost when scheduling mixed workloads. Extensive experiments are conducted on various platforms to investigate the performance of the proposed scheduler. The results indicate that when the system is in presence of mixed workloads, SRC-I/O scheduler outperforms many existing VM schedulers in terms of energy-efficiency and I/O responsiveness.     

System level fixed priority energy management algorithm for embedded real time application

Dynamic power management (DPM) and dynamic voltage scaling (DVS) are crucial techniques to reduce the energy consumption in embedded real-time systems. Many previous studies have focused on the energy consumption of the processor or I/O devices. In this paper, we focus on the problem of energy management integrating DVS and DPM techniques for periodic embedded real-time applications with rate monotonic (RM) policy and present a system level fixed priority energy-efficient scheduling (SLFPEES) algorithm. The SLFPEES algorithm consists of I/O device scheduling and job scheduling. I/O device scheduling is based on the dynamic power management with rate monotonic (DPM-RM) policy which puts devices into the sleep state when the idle interval is larger than devices break even time. Job scheduling is based on the RM policy and uses stack resource protocol (SRP) to guarantee exclusive access to the shared resources. For energy efficiency, the SLFPEES algorithm schedules the task with a lower speed and a higher speed. The experimental result shows that the SLFPEES algorithm can yield significantly energy savings with respect to the existing techniques.     

一种动态优先级排序的虚拟机I/O调度算法

I/O任务调度是影响I/O密集型虚拟机性能的重要因素。现有调度方法主要是针对虚拟机整机I/O带宽的优化,较少兼顾各虚拟域与全局性能,也无法满足域间差异化服务的要求。针对现有方法的不足,提出了一种动态优先级排序的虚拟机I/O调度算法DPS。该算法基于多属性决策理论,以离差最大化方法计算I/O任务的优先级评估属性权重,对I/O任务优先级进行综合评估;通过引入任务所在虚拟域价值,体现云计算环境下虚拟域重要性差异。在Xen系统中通过实验评测DPS调度虚拟化网卡的性能,结果表明,DPS能够有效提高指定域与全局的I/O任务截止期保证率、整机I/O带宽,并能为不同虚拟域的I/O应用提供差异化服务。     

Energy-aware disk scheduling for soft real-time I/O requests

In this work, we develop energy-aware disk scheduling algorithm for soft real-time I/O. Energy consumption is one of the major factors which bar the adoption of hard disk in mobile environment. Heat dissipation of large scale storage system also calls for an energy-aware scheduling technique to further increase the storage density. The basic idea in this work is to properly determine the I/O burst size so that device can be in standby mode between consecutive I/O bursts and that it can satisfy the soft real-time requirement. We develop an elaborate model which incorporates the energy consumption characteristics, overhead of mode transition in determining the appropriate I/O burst size and the respective disk operating schedule. Efficacy of energy-aware disk scheduling algorithm greatly relies on not only disk scheduling algorithm itself but also various operating system and device firmware related concerns. It is crucial that the various operating system level and device level features need to be properly addressed within disk scheduling framework. Our energy-aware disk scheduling algorithm successfully addresses a number of outstanding issues. First, we examine the effect of OS and hard disk firmware level prefetch policy and incorporate its effect in our disk scheduling framework. Second, our energy aware scheduling framework can allocate a certain fraction of disk bandwidth to handle sporadically arriving non real-time I/O’s. Third, we examine the relationship between lock granularity of the buffer management and energy consumption. We develop a prototype software with energy-aware scheduling algorithm. In our experiment, proposed algorithm can reduce the energy consumption to one fourth if we use energy-aware disk scheduling algorithm. However, energy-aware disk scheduling algorithm increases buffer requirement significantly, e.g., from 4 to 140 KByte. We carefully argue that the buffer overhead is still justifiable given the cost of DRAM chip and importance of energy management in modern mobile devices. The result of our work not only provides the energy efficient scheduling algorithm but also provides an important guideline in capacity planning of future energy efficient mobile devices. This paper is funded by KOSEF through Statistical Research Paper for Complex System at Seoul National University.     

一种新颖的并行程序配置优化算法

本文提出了一种新颖的并行程序配置优化算法。这种算法利用黑板系统将配置优化问题分解组织为不同层次的知识领域,并利用A^＊算法对决策树进行搜索。研究了并行程序的任务调度、存储服务器的数据分配、自适应分片、协同I／O和数据筛选五个知识领域。     

视频服务器中多网络I/O的调度与接纳控制

在视频服务器中,由于网络I/O带宽比磁盘I/O带宽相对丰富而未得到研究者的重视,但当服务达到一定的规模、网络输出成为瓶颈时,在多网络I/O情况下,网络输出的量化计算则不可避免.着重探讨了视频服务器在多网络I/O并行输出的情况下,如何进行最大输出调度的问题,给出了3个调度算法及相应的接纳计算公式,特别是一个带缓冲的优化调度模型.实验结果显示,调度模型及规划性能是优越而有效的.该项工作对视频服务器的调度设计、资源的有效配置具有指导作用.     

负载自适应的存储池数据重构策略

大数据时代各应用领域对计算机存储系统的性能和可靠性需求与日俱增。新型存储介质为计算机存储系统的性能提升提供了良好的机遇,基于固态盘的存储阵列（RAIS）已在各种存储系统中广泛使用。传统RAIS系统中当一块固态盘出现故障时,通过数据重构操作恢复故障盘的数据,重构时间长,且影响对上层应用提供I/O访问服务的能力。针对该问题,设计实现了基于多线程并发处理的存储池架构,该架构能够并发处理存储池中的I/O请求,提高用户I/O和数据重构I/O的访问性能。提出了一种负载自适应的I/O调度策略,能够在保证用户I/O服务质量的同时,提升数据重构效率。实验结果表明,基于存储池的多线程并发I/O处理架构能够提升数据重构性能,负载自适应的I/O调度策略能够根据用户I/O的负载情况动态调整用户I/O和数据重构I/O的调度比例,在保证用户I/O服务质量的同时,提升数据重构效率。