适用于偶发实时系统的过载控制策略

针对偶发实时系统中过载处理资源消耗大的问题,提出一种基于最小可推迟时间和非精确计算的过载控制策略。结合最早截止期优先调度算法,利用最大挪用时间与最小可推迟时间动态判断系统负载状态,根据系统负载状态舍弃部分非重要任务,解决系统过载问题。实验结果表明,该策略可缩短过载处理时间,提高系统资源利用率。     

一种基于反馈机制的自适应弹性调度算法

针对实时多任务系统中的临时过载问题,提出一种基于反馈机制的自适应弹性周期调度算法。考虑任务截止期小于或等于任务 周期的情况,参考弹性调度算法的基础模型,动态估计周期任务的执行时间。仿真实验结果表明,该算法可有效提高随机任务集的调度成 功率。     

基于大数据调度的OMS配网一体化调控算法研究

针对当前OMS配网一体化调控方法存在的带宽利用率低、数据传输时延高和数据丢包率高的问题,提出基于大数据调度的OMS配网一体化调控算法.根据任务的稀缺度和紧急度计算任务在OMS配网中的优先请求级别,采用历史信息统计法结合节点能力影响因素计算节点在OMS配网中的上传能力.在任务优先请求级别和节点上传能力的基础上,计算路径在OMS配网中的可用带宽和前向传输时延,并将最大优先算法应用到发生数据丢包的现象中,重新选择传输路径,避免配网中接收端出现乱序的现象,根据计算结果结合重选路策略实现OMS配网的一体化调控.实验结果表明,所提算法的带宽利用率高、数据传输时延低、数据丢包率低.     

Hadoop平台下基于加权轮转算法的改进任务调度算法

为提高Hadoop作业调度的效率,增加云平台的吞吐率,提出了一种基于Hadoop云计算平台的作业调度算法。该算法在加权轮转调度算法的基础上,针对MapReduce的运行特点,增加了改进map任务本地性调度的因素,使得作业调度仍然保持了相对的公平性,并通过提高轮转周期内的map任务数据本地性,减少了任务的执行时间。实验结果证明,该调度算法与加权轮转调度算法相比,较好地提高了任务本地执行的比例,缩短了云计算系统内作业的总执行时间。     

基于反馈控制的实时调度算法

实时系统中基于反馈的闭环调度算法通常用于过载发生后的情况。为此,提出一种基于反馈控制的实时调度算法。该算法利用回归模型与非精确计算模型对每个任务进行跟踪,并在系统瞬时利用率大于系统最大利用率时提前进行调整。实验结果表明,该算法能有效预防系统过载,保证任务实时性。     

云计算中一种多DAG工作流可抢占式调度策略

为解决多用户工作流调度过程中的公平性问题,提高资源利用率,满足不同用户DAG工作流的不同QoS需求,提出了抢占式多DAG工作流动态调度模型。该算法将DAG工作流按照QoS需求进行优先级划分,采用高优先级作业优先占有资源的原则调度作业。相同优先级DAG工作流的任务依据带有启发性信息的slowdown进行资源抢占,进一步提高了作业调度的公平性;对于不同优先级的作业调度,提出了基于阈值的回填算法,该算法在保证作业调度公平的同时提高了资源利用率。     

Hadoop平台下计算模型中调度策略的研究

针对Hadoop平台下默认调度算法FIFO、计算能力调度算法以及公平调度算法在调度过程中遵守严格的队列顺序,导致一些任务被调度到不满足数据本地性节点上的问题,提出一个基于本地性的调度算法——延时调度。该算法在维护公平性原则的同时,当一个被调度的作业无法启动一个本地的任务时,让这个任务等待一小段时间,调度其他作业先执行。实验结果表明,此调度算法缩短了作业平均响应时间,有效增加了集群系统的吞吐量,提高了集群资源利用率。     

基于奖惩共存收益模式的大数据作业调度器

为满足服务商获得最大收益、达到平台资源利用率最大的要求，提出一种基于奖惩共存收益模式的大数据作业调度器，该调度器中包括基于任务执行时间的确定轮数算法(TRN)和基于最大轮数的作业调度算法(MRNS)。TRN确定作业在不同奖惩阶段的Map和Reduce的最大轮数组合以及最大标准时间；MRNS选择具有局部最大收益的作业和该作业的任务最大轮数方案，制定出基于任务的作业调度策略。实验结果表明，提出的作业调度器对比已有的调度器，作业平均完成时间缩短了13.5%～25.9%、服务商收益提高了16.3%～26.4%,平台资源利用率平均提高了7.8%～10.3%,故该大数据作业调度器具有一定的高效性和可用性。     

单调速率任务分配算法利用率的界限分析

通过对单调速率任务分配算法调度策略和可调度条件的分析,在多处理器周期任务抢占调度模型基础上,细致刻画了任务分配算法如何分配任务的行为。依据Liu和Layland定理,给出多处理器下任务分配算法的最小RM利用率界的定理。仿真结果表明,分配算法的利用率界是不同特征任务集选择不同分配算法进行任务划分的关键,通过对任务集总利用率与算法利用率界的比较,判断使用该算法对任务集是否可以产生可行分配。     

一种改进的 Hadoop 多用户作业调度方法

针对目前 Hadoop 作业调度方法服务水平不高、资源利用率低的问题,提出了一种改进的 Hadoop 多用户作业调度算法。分析了 Hadoop 现行调度算法存在的不足,提出了基于服务质量（QoS）的作业选择量化和基于遗传算法的任务选择均衡化的方法,最后采用 Hadoop 平台对算法进行了仿真。仿真结果表明,该资源调度方法提高了作业的服务质量,实现了资源的合理调度。     

多租户集群中基于服务水平目标的调度机制

针对多租户集群中无法保证作业服务水平目标(SLO)的问题,提出了一种多租户场景下基于SLO的调度机制,其中包括优先调度算法和资源抢占算法。优先调度算法区别考虑超额使用资源的租户和未超额使用资源的租户,赋予后者的作业更高的优先级,在此前提下选择紧急度最高的作业,优先为其分配资源;资源抢占算法在资源受限的情况下,选择紧急度超过阈值的作业实施资源抢占,并根据租户的资源使用情况,在相应的运行作业范围内选择紧急度最低的作业,抢占其资源。实验结果表明,与现有保证公平的多租户调度器Capacity Scheduler相比,该调度机制可以在兼顾作业执行效率和租户间公平的前提下,显著提高作业的截止时间保证率,从而保证业务的服务水平目标。     

一种提高构件化嵌入式操作系统性能的方案

本文分析了现有构件化嵌入式操作系统所用调度算法存在的缺点,提出抢占阈值调度算法是更为合适的算法。通过仿真实验比较抢占阈值调度算法、非抢占式调度算法和FIFO(First-In-First-Out)调度算法的性能,证明了上述结论。通过分析现有嵌入式系统构件模型的特点,提出了一种构件模型以及将构件映射成任务的方式,还提出了一种设计方法。整个方案能提高构件化嵌入式操作系统的性能。     

A new MVA-based approximation for closed queueing networks with a preemptive priority server

We propose a new MVA-based noniterative algorithm for solving closed queueing network models of computer systems with a preemptive priority server. The algorithm attempts to capture synchronization error, delay error, and the effect of preemption on the interarrival time variability of lower priority jobs at FCFS servers. The principal feature of our approximation is that it attempts to capture the effects of preemption not only at the priority server, but also at other servers in the system. Numerical results indicate that the algorithm predicts the performance measures of low priority jobs more accurately than previously developed MVA-based algorithms.     

全局EDZL实时调度算法的可调度性判定

可调度性判定就是离线验证实时系统中所有任务是否可调度。通过可调度性判定,能够更好地保证实时系统的可靠性。有多种分析方法可用于可调度性判定,得到的结果有所不同。通过分析在多处理器实时系统中全局EDZL算法的任务需求,考虑带入作业和带出作业对处理器资源的需求。引入最大连续忙区间的概念,以确定带入作业的最大个数,得到了多处理器实时系统中全局EDZL算法的可调度性判定算法。实验结果说明了这种方法提高了通过可调度性判定的任务数量,是一种更紧密的可调度性判断方法。     

基于Hadoop的三队列作业调度算法

Hadoop集群单队列作业调度会产生短作业等待、资源利用率低的问题;采用多队列调度可兼顾公平、提高执行效率,但会带来手工配置参数、资源互占、算法复杂等问题。针对上述问题,提出三队列作业调度算法,利用区分作业类型、动态调整作业优先级、配置共享资源池、作业抢占等设计,达到平衡作业需求、简化一般作业调度流程、提升并行执行能力的目的。对短作业占比高,各作业占比均衡以及一般作业为主,偶尔出现长、短作业三种情况与先进先出(FIFO)算法进行了对比实验,结果三队列算法的运行时间均比FIFO算法要少。实验结果表明,在短作业聚集时,三队列算法的执行效率提升并不显著;但当各种作业并存且分布均衡时,效果很明显,这符合了算法设计时短作业优先、一般作业简化流程、兼顾长作业的初衷,提高了作业整体执行效率。     

Criticality- and QoS-Based Multiresource Negotiation and Adaptation

This paper presents design, analysis, and implementation of a multiresource management system that enables criticality- and QoS-based resource negotiation and adaptation for mission-critical multimedia applications. With the goal of maximizing the number of high-criticality multimedia streams and the degree of their QoS, it introduces a dynamic scheduling approach using on-line QoS adjustment and multiresource preemption. An integrated multiresource management infrastructure and a set of scheduling algorithms for multiresource preemption and on-line QoS adjustment are presented. The optimality and execution efficiency of two preemption algorithms are analyzed. A primal-dual-algorithm-based approximation solution is shown (1) to be comparable to the linear-programming-based solution, which is near optimal; (2) to outperform a criticality-cognitive baseline algorithm; and (3) to be feasible for on-line scheduling. In addition, the dynamic QoS adjustment scheme is shown to greatly improve the quality of service for video streams. The multiresource management system is part of the Presto multimedia system environment prototyped at Honeywell for mission-critical applications.     

一种基于阈值的嵌入式实时系统调度算法

基于阈值的双优先级调度算法结合了抢占式与非抢占式调度算法的优点,可以提高任务集的调度成功率,并减少由于任务切换引起的系统开销。对阈值的分配是调度算法的核心。在基本优先级已知的条件下,基于回溯技术的阈值分配算法利用低端任务阈值单向影响高端任务最大响应时间的特性,可以在有限的时间内为任务集找出一组具有极大值特征的阈值。该组阈值可以将任务切换次数降至最低。     

Bandwidth Management for Supporting Differentiated Service Aware Traffic Engineering

This paper presents a bandwidth management framework for the support of differentiated-service-aware traffic engineering (DS-TE) in multiprotocol label switching (MPLS) networks. Our bandwidth management framework contains both bandwidth allocation and preemption mechanisms in which the link bandwidth is managed in two dimensions: class type (CT) and preemption priority. We put forward a Max-Min bandwidth constraint model in which we propose a novel "use it or lend it" strategy. The new model is able to guarantee a minimum bandwidth for each CT without causing resource fragmentation. Furthermore, we design three new bandwidth preemption algorithms for three bandwidth constraint models, respectively. An extensive simulation study is carried out to evaluate the effectiveness of the bandwidth constraint models and preemption algorithms. When compared with the existing constraint models and preemption rules, the proposed Max-Min constraint model and preemption algorithms improve not only bandwidth efficiency, but also robustness and fairness. They achieve significant performance improvement for the well-behaving traffic classes in terms of bandwidth utilization and bandwidth blocking and preemption probability. We also provide guidelines for selecting different DS-TE bandwidth management mechanisms.     

Min_c: Heterogeneous concentration policy for energy-aware scheduling of jobs with resource contention

In this paper, we address energy-aware online scheduling of jobs with resource contention. We propose an optimization model and present new approach to resource allocation with job concentration taking into account types of applications and heterogeneous workloads that could include CPU-intensive, diskintensive, I/O-intensive, memory-intensive, network-intensive, and other applications. When jobs of one type are allocated to the same resource, they may create a bottleneck and resource contention either in CPU, memory, disk or network. It may result in degradation of the system performance and increasing energy consumption. We focus on energy characteristics of applications, and show that an intelligent allocation strategy can further improve energy consumption compared with traditional approaches. We propose heterogeneous job consolidation algorithms and validate them by conducting a performance evaluation study using the Cloud Sim toolkit under different scenarios and real data. We analyze several scheduling algorithms depending on the type and amount of information they require.     

Opportunity cost algorithms for reduction of I/O and interprocess communication overhead in a computing cluster

Computing clusters (CC) consisting of several connected machines, could provide a high-performance, multiuser, timesharing environment for executing parallel and sequential jobs. In order to achieve good performance in such an environment, it is necessary to assign processes to machines in a manner that ensures efficient allocation of resources among the jobs. The paper presents opportunity cost algorithms for online assignment of jobs to machines in a CC. These algorithms are designed to improve the overall CPU utilization of the cluster and to reduce the I/O and the interprocess communication (IPC) overhead. Our approach is based on known theoretical results on competitive algorithms. The main contribution of the paper is how to adapt this theory into working algorithms that can assign jobs to machines in a manner that guarantees near-optimal utilization of the CPU resource for jobs that perform I/O and IPC operations. The developed algorithms are easy to implement. We tested the algorithms by means of simulations and executions in a real system and show that they outperform existing methods for process allocation that are based on ad hoc heuristics.