面向互联网的计算资源共享平台容错机制研究

描述了面向互联网的计算资源共享平台下的新型容错机制.该框架采用服务器端、调度节点,志愿机的层次网络体系结构,把调度节点的主-从备份容错与志愿机端的面向子任务的容错结合起来,保证了系统的最终计算结果不受影响.通过运行电力系统潮流计算的并行程序,对该系统的稳定性与可扩展性进行实验,小规模测试验证了该容错机制的正确性与高效性.     

自愿计算环境下基于代理的自适应并行调度

描述了一个自愿计算环境下的基于代理的自适应并行调度模型,该调度代理处于服务器端与工作机端之间,可以缓解服务器端的访问竞争.在该调度模型中,服务器端直接把工作单元分配给调度代理,工作机从调度代理处获取工作单元;调度代理具有自适应并行性,高效率性,容错等特性.最后通过测试与性能分析验证了该调度模型的正确性与高效性.     

基于层次化的网格资源三层调度模型

在分析现有的资源调度方案及模型的基础上,提出了基于层次化的网格资源三层调度模型．它由主调度器、次级调度器和计算节点组成。主调度器根据任务的性质和需求,并参考下层次级调度器的执行情况,将部分任务分发到各次级调度器上,实现了主调度器与次级调度器之间的并行工作。基于该模型提出轮循任务分发策略。通过分析和模拟．该资源调度模型及任务分发策略在调度性能上明显优于集中式调度方案。     

基于层次化的网格资源三层调度模型

在分析现有的资源调度方案及模型的基础上,提出了基于层次化的网格资源三层调度模型,它由主调度器、次级调度器和计算节点组成.主凋度器根据任务的性质和需求,并参考下层次级调度器的执行情况,将部分任务分发到各次级调度器上,实现了主调度器与次级调度器之间的并行工作.基于该模型提出轮循任务分发策略.通过分析和模拟,该资源调度模型及任务分发策略在调度性能上明显优于集中式调度方案.     

基于Petri网的分布式实时调度模型研究

Petri网作为一种可视化的规格语言,越来越多地用于实时系统的评估和分析。该文提出了一种基于Petri网的分布式实时系统模型,并对该模型中的局部调度器和消息调度器进行了描述。根据该模型可以开发相应的分布式实时调度模拟器,这样就可以在系统的开发初期,利用模拟器来验证在给定的局部调度策略和消息调度策略下,系统任务的时间约束是否能够得到有效的保障。同时该模型还可以很容易地转化为系统的快速原型。     

RTAI混合任务调度器的设计与实现

针对RTAI系统只能调度单一类型任务的问题,提出一种可调度多类型任务的混合任务调度器。该调度器支持CBS调度策略,并采用LXRT机制关联软实时任务和CBS服务器,根据服务器调度策略对软实时任务进行调度。实验结果表明,该混合任务调度器在保证硬实时任务正常运行的同时提高了软实时任务的服务水平。     

一种MapReduce实时调度算法设计及实现

MapReduce是云计算中重要的批数据处理框架,多任务共享MapReduce机群并满足任务实时性要求是调度算法急需解决的问题。提出两阶段实时调度算法,将调度划分为任务间调度和任务内调度。对于任务间调度,使用抽样法和经验值法确定子任务执行时间,利用该参数建立资源分配模型,动态确定任务优先级进行调度;对于子任务使用延迟调度策略进行调度,保证计算的本地性。实验结果显示,两阶段实时调度算法相比公平调度算法和FIFO算法,在保证吞吐量的同时能够满足任务实时性要求。     

实时系统中传统调度方式的一种改进方法

介绍了在实时操作系统(RTOS)中两种典型的调度算法,速率单调调度(RMS)算法和最早死线优先(EDF)调度算法。指出了传统实时系统中调度机制的不足之处,给出一个通用调度框架模型(GSF)。该模型将调度器分成事务分派器和事务调度器两个部分,事务分派器根据任务的需求设置相应的属性,而事务调度器则根据任务的属性决定采用哪种调度算法。该模型使得调度更加灵活、有效。     

因特网上基于节点角色的计算资源共享平台——RB-CRSP

提出了因特网上基于节点角色的计算资源共享平台——RB-CRSP。设计时充分考虑节点的角色性和功能性,把因特网上的网络资源按照角色划分为服务器端节点、协调节点、工作机节点与客户机节点四类实体,通过配合RB-CRSP的应用编程模式,完成并行分布式计算。分析了RB-CRSP中的自适应资源调度策略,该策略考虑了节点的硬件信息与可信誉机制,实现了平台的负载均衡性;在动态的因特网环境下,利用面向工作机的容错方式保证了平台的可靠性。案例程序选择了典型的并行BenchMark程序：N皇后问题,测试结果表明,RB-CRSP可以方便聚集异构环境下的空闲计算资源,平台的性能与机器硬件条件和可靠性密切相关。     

面向互联网计算资源共享的并行程序设计环境

构建了一个面向互联网计算资源共享的并行程序设计环境IPPE(internet-based parallel programming environment).该环境使用Java语言开发,通过利用Java运行系统与Java并行通信类库,在IPPE环境下可以书写具有并行处理能力的Java应用程序.IPPE具有平台独立性、容易使用、负载均衡性、容错性等特点.IPPE环境的平台独立性与易用性得益于其基于Java的字节码技术与对象序列化技术,IPPE的负载均衡性得益于针对任务的自适应并行调度算法及子任务级的容错策略.通过运行两个典型的BanchMark并行程序,表明了IPPE环境的高效性与稳定性.     

因特网上基于.NET的通用计算资源共享环境

为了高效与方便地聚集异构环境下的网络资源,提出一种基于.NET的通用计算资源共享环境GCRSE。GCRSE在体系结构上利用了节点功能角色的方式,由服务器功能实体、自愿机功能实体和客户机功能实体组成。它利用.NET的Web services技术,通过网页的方式提交、执行与传输并行分布式应用相关的任务和子任务。在编程模型上支持普通的主—从模式的编程模式和具有子任务递归迭代的分治模式编程。自愿机端的心跳消息和面向子任务的容错方式可以达到可靠性高的效果。GCRSE的测试环境通过局域网内的若干台自愿机组成,选择了具有数据依赖的分布式旅行商(TSP)问题进行测试,结果表明对于分布式计算中大量的子任务GCRSE环境可以很快地完成,具有高效性、可扩展性和通用性的特点。     

计算网格中基于时间均衡的并行粗粒度任务调度算法

考虑网格资源异构、自治、动态等特性,讨论本地用户具有强占优先权情况下的任务调度问题,提出了TBBS(Time-Balancing Based Scheduling Algorithm)算法.建立调度优化模型,以期望完成时间最小为目标选择执行任务的最佳资源组合.以时间均衡策略将任务分解并调度到资源上执行,减少了子任务同步时因等待而产生的延时,获得较好的并行计算性能.采用重复调度策略,适应计算网格中资源的特性.     

Task mapper and application-aware virtual machine scheduler oriented for parallel computing

We design a task mapper TPCM for assigning tasks to virtual machines, and an application-aware virtual machine scheduler TPCS oriented for parallel computing to achieve a high performance in virtual computing systems. To solve the problem of mapping tasks to virtual machines, a virtual machine mapping algorithm (VMMA) in TPCM is presented to achieve load balance in a cluster. Based on such mapping results, TPCS is constructed including three components: a middleware supporting an application-driven scheduling, a device driver in the guest OS kernel, and a virtual machine scheduling algorithm. These components are implemented in the user space, guest OS, and the CPU virtualization subsystem of the Xen hypervisor, respectively. In TPCS, the progress statuses of tasks are transmitted to the underlying kernel from the user space, thus enabling virtual machine scheduling policy to schedule based on the progress of tasks. This policy aims to exchange completion time of tasks for resource utilization. Experimental results show that TPCM can mine the parallelism among tasks to implement the mapping from tasks to virtual machines based on the relations among subtasks. The TPCS scheduler can complete the tasks in a shorter time than can Credit and other schedulers, because it uses task progress to ensure that the tasks in virtual machines complete simultaneously, thereby reducing the time spent in pending, synchronization, communication, and switching. Therefore, parallel tasks can collaborate with each other to achieve higher resource utilization and lower overheads. We conclude that the TPCS scheduler can overcome the shortcomings of present algorithms in perceiving the progress of tasks, making it better than schedulers currently used in parallel computing.     

一种基于能耗优化的云计算系统任务调度方法

服务器执行任务产生的能耗是云计算系统动态能耗的重要组成部分。为降低云计算系统任务执行的总能耗,提出了一种基于能耗优化的最早完成时间任务调度方法,建立了服务器动态功率计算模型,基于动态功率的服务器执行能耗模型,以及云计算系统的能耗优化模型。调度策略根据任务的截止时间要求和在不同服务器上的执行能耗,选择不同的调度算法,以获得最小任务执行总能耗。实验结果证明,提出的任务调度方法,能够较好地满足任务截止时间的要求,降低云计算系统任务执行的总能耗。     

DNS dispatching algorithms with state estimators for scalable Web‐server clusters

Replication of information across a server cluster provides a promising way to support popular Web sites. However, a Web‐server cluster requires some mechanism for the scheduling of requests to the most available server. One common approach is to use the cluster Domain Name System (DNS) as a centralized dispatcher. The main problem is that WWW address caching mechanisms (although reducing network traffic) only let this DNS dispatcher control a very small fraction of the requests reaching the Web‐server cluster. The non‐uniformity of the load from different client domains, and the high variability of real Web workload introduce additional degrees of complexity to the load balancing issue. These characteristics make existing scheduling algorithms for traditional distributed systems not applicable to control the load of Web‐server clusters and motivate the research on entirely new DNS policies that require some system state information. We analyze various DNS dispatching policies under realistic situations where state information needs to be estimated with low computation and communication overhead so as to be applicable to a Web cluster architecture. In a model of realistic scenarios for the Web cluster, a large set of simulation experiments shows that, by incorporating the proposed state estimators into the dispatching policies, the effectiveness of the DNS scheduling algorithms can improve substantially, in particular if compared to the results of DNS algorithms not using adequate state information. This revised version was published online in August 2006 with corrections to the Cover Date.     

Minimizing the application execution time through scheduling ofsubtasks and communication traffic in a heterogeneous computing system

In a heterogeneous computing (HC) environment consisting of different types of machines, an application program is decomposed into subtasks, each of which is computationally homogeneous. The goal is to execute subtasks on the machines in such a way that the total program execution time is minimized. A mathematical framework is presented that models the matching of subtasks to machines, scheduling of subtasks' computation, scheduling of intermachine communication steps, and selection of sources of shared data items for intermachine communication (data relocation). The goal of this work is to generate a provably optimal scheme for communicating shared data among subtasks as an enhancement to any given matching and scheduling. Initially, it is assumed that at any instant in time, only one machine is being used for program execution and only one subtask is being executed. Based on this assumption, a polynomial algorithm is introduced to optimize scheduling and data relocation with respect to any given matching of subtasks to machines. The data relocation scheme is then extended to reduce intermachine data communication time in an HC environment with a given matching and scheduling of subtasks' computation where: multiple subtasks' computations can be performed concurrently on different machines; subtask computation steps can be overlapped with other subtasks' communication steps for intermachine data transfers; and machines in the HC suite are interconnected by a shared-bus type of network     

SLA based resource allocation policies in autonomic environments

Nowadays, large service centers provide computational capacity to many customers by sharing a pool of IT resources. The service providers and their customers negotiate utility based Service Level Agreement (SLA) to determine the costs and penalties on the base of the achieved performance level. The system is often based on a multi-tier architecture to serve requests and autonomic techniques have been implemented to manage varying workload conditions. The service provider would like to maximize the SLA revenues, while minimizing its operating costs. The system we consider is based on a centralized network dispatcher which controls the allocation of applications to servers, the request volumes at various servers and the scheduling policy at each server. The dispatcher can also decide to turn ON or OFF servers depending on the system load. This paper designs a resource allocation scheduler for such multi-tier autonomic environments so as to maximize the profits associated with multiple class SLAs. The overall problem is NP-hard. We develop heuristic solutions by implementing a local-search algorithm. Experimental results are presented to demonstrate the benefits of our approach.     

基于任务复制的网格任务调度算法

网格中资源之间存在着通信延迟，通过任务复制的冗余，可以减少任务之间的通信开销，缩短整个计算程序的计算时间。目前网格中的任务调度算法基本上是没有考虑任务复制的；而基于任务复制调度算法往往会产生过多的复制任务，增大系统开销，甚至有可能延迟计算时间。由于基于任务复制的任务调度是一个NP问题，因此本文提出了一种基于任务复制的网格资源调度算法，以减少调度长度为主要目标、减少任务复制量和资源占用量为次要目标。该算法在调度长度和任务复制数量以及占用资源数量方面都等于或优于其它算法。     

一种开放混合实时系统的开放自适应调度算法

开放计算环境下的实时与非实时任务不确定并发,以及多种实时约束混合的复杂约束系统,即开放混合实时系统的需求越来越广泛.通过引入接收控制、调度服务器、自适应调节机制,提出一种开放环境下的自适应实时系统调度架构--OARtS(open adaptive real-time scheduling).它能适应开放计算环境的不确定性,有控制地接受实时任务运行;可根据系统空闲计算带宽变化,自适应地调节任务的实时等级,使得系统运行在最优的实时性能上;对于软实时任务,可根据其计算带宽需求变化,自适应地调节其计算带宽分配,以适应任务执行时间时变引起的实时不确定性.