基于Quartz的集团化调度任务分布部署研究

集团化应用系统中定时任务需要处理的数据量大、任务数量多,为即时快速对所有数据进行处理,需要将任务进行分布部署运行。通过对Quartz调度框架和集群分布部署技术的分析和研究,提出了集团化应用系统任务调度基于Quartz的集群分布部署方案,通过将Quartz和Spring集成并对Quartz调度任务容器进行扩展,在MemCache上进行服务器状态的更新,解决了大型应用系统定时任务的分布运行、故障处理问题。     

Spring的任务定时调度方法的研究比较

在Web应用中,经常需要将某个工作安排在某个特定时间点去做。Spring提供了丰富多样的任务执行调度,包括使用JDK Timer、使用SchedulingTaskExecutor接口的实现类、使用Quartz、使用TaskScheduler以及采用@Scheduled注解等多种形式。研究得出,JDK Timer仅支持简单的间隔定时执行;SchedulingTaskExecutor接口的实现类提供了并发任务的各类执行方式;Quartz和TaskScheduler支持基于Cron表达式的定时调度,后者还可通过@Scheduled注解定义任务定时。     

基于VxWorks的抢占式周期任务调度改进算法

对传统的周期任务调度算法进行了改进,通过辅助时钟对周期任务进行定时,在固有的周期任务调度算法的基础上引入了错峰机制,使得不同的周期任务能够以从高到低的顺序错峰执行,改善了周期任务的执行时机,有效的提高了应用系统的性能。     

任务定时调度在企业级开发中的研究

目前的Web应用多数具备任务调度的功能。文中研究了基于Java实现的几种任务调度方法。包括使用JDK Timer、实现ScheduledExecuter接口的子类以及使用Quartz调度器。根据不同的任务调度需求选择合适的任务调度方法,对于任务调度在企业的开发工作具有一定意义。     

综合数据业务平台定时任务调度组件的设计与实现

结合综合数据业务平台的一般应用场景,对其中的定时任务需求进行详细的分析,并在此基础上设计并实现定时任务调度组件,该组件能够实现对大量定时任务高效管理和调度。     

一种数据驱动定时调度的方法

运价发布业务产品是民航运价系统的数据基础,为航空公司提供数据维护、发布和查询业务,通过大量定时执行的应用任务或夜维程序,为航空公司导入大批量数据,处理后发布或导出到运价计算或运价搜索系统。不同航空公司有不同的调度需求,需要方便灵活管理作业的调度策略,也要易维护和高可用。基于此,提出一种利用数据库技术,将定时任务信息存入数据库表,基于数据驱动的定时调度方法,实现任务调度配置与业务代码之间的解耦,具有任务调度数据集中、便于维护等特点,可提供多种调度模式,并通过前后端分离的方式,通过前端页面方便地维护任务调度数据,极大地提升应用的处理效率和维护成本。     

分布式任务调度在集中MSS系统中的实践应用

随着中国电信集中MSS系统规模发展,定时任务数量日益增多,任务也变得越来越复杂,这些任务越来越占用系统资源,在系统繁忙时会影响到用户的正常使用.本文研究利用分布式任务调度技术,对集中MSS系统中的各项后台任务进行统一管理和统一调度,缓解集中MSS系统的压力,提升用户前台使用感知.     

VxWorks系统的任务调度机制

针对多任务系统而言，调度是指根据一定的算法．将CPU分配给符合条件的任务使用，不同的系统任务调度机制不同。本文介绍VxWorks系统的任务调度策略和算法．分析优先级倒置产生的原因并给出解决方案。重点阐述静态表方式的实现方法并给出C语言程序框架。     

分布式架构下的电力作业定时任务调度优化方法

为了降低定时任务的完成时间，提高电力作业整体效率，研究分布式架构下的电力作业定时任务调度优化方法。进行定时任务的精准同步，在粒子群优化算法基础上，改进DPSO(离散粒子群优化)算法，以电力作业定时任务总体完成时间最短作为目标函数，完成电力作业定时任务的调度优化。算例分析结果表明，该方法收敛速度较快，收敛后的粒子运动速度能够更快趋于平稳，且可寻得最优目标函数值，可显著降低电力作业定时任务的总体完成时间，达到调度优化目标，为提升整体电力作业效率奠定基础。     

基于Pyspider的网络爬虫架构的分析与优化

为了从互联网海量数据中获取数据资源,实现对前沿技术动态的监控,使用Pyspider框架定时爬取大批网站的信息,并通过python脚本定时将爬取的数据导入到业务系统中。最初,系统采用了Pyspider官方推荐的分布式架构,在使用中发现存在任务队列容易堵塞、受网络质量影响大、获得的数据不利于统计和使用等问题。针对上述问题和业务需要,对Pyspider框架进行优化,将分布式架构转变为集群式架构;剥离与网页内容获取和保存不相关的任务,在Pyspider框架外独立完成。经测试,改进后的架构工作效率和稳定性大幅提升,达到了预期的效果。     

大规模网络管理中的任务分解与调度

集中式的管理模式和简单的基于移动agent的网络管理方法都已不能满足大型网络管理的需要,为此,在给出基于多agent的网络管理框架的基础上,提出了一种基于任务依赖关系的任务分解算法,把网络管理任务分解为具有不同优先级的子任务,处于同一优先层次的子任务可以并行执行,根据子任务的优先级产生结合网络管理特点的调度策略,理论分析和实验表明:在大规模网络管理中采用任务分解技术能够提高时间效率和减轻网络负载。     

分布式开放实时系统调度策略研究

为有效管理分布式开放实时系统(Distributed Open Real-Time System,DORTS)的CPU和网络资源,提供实时应用端到端延时确保,提出一种基于资源预留的分层调度策略.对于任务的调度,首先利用最大需求任务优先与最小可利用率节点适应算法将任务分配到各节点,然后在节点上采用基于服务器的两层调度架构...     

Run-time management of systems with partially reconfigurable FPGAs

Partial reconfiguration (PR) of FPGAs can be used to dynamically extend and adapt the functionality of computing systems by swapping in and out HW tasks. To coordinate the on-demand task execution, we propose and implement a Run-Time System Manager (RTSM) for scheduling software (SW) tasks on available processor(s) and hardware (HW) tasks on any number of reconfigurable regions (RRs) of a partially reconfigurable FPGA. Fed with the initial partitioning of the application into tasks, the corresponding task graph, and the available task mappings, the RTSM controls system operation considering the status of each task and region (e.g. busy, idle, scheduled for reconfiguration/execution, etc). Our RTSM supports task reuse and configuration prefetching to minimize reconfigurations, task movement among regions to efficiently manage the FPGA area, and region reservation for future reconfiguration and execution. We validate the correctness and portability of our RTSM executing an image processing application on two Xilinx-based platforms: ZedBoard and XUPV5. We also perform a more extensive evaluation of its features using a simulation framework, and find that – despite the technology limitations – our approach can give promising results in terms of scheduling quality. Since our RTSM supports also the scheduling of parallel SW tasks, we use it to manage the execution of the entire parallel Edge Detection application on a desktop; we compare the application execution time with that using the OpenMP framework and find that with our RTSM execution is 2.4 times faster than the unoptimized OpenMP version. When processor affinity optimization is enabled for OpenMP, our RTMS and the OpenMP are on par, indicating that the scheduling efficiency of our RTSM is competitive to this state-of-the-art scheduler, while supporting in addition the management of HW tasks.     

Optimal preventive maintenance scheduling in semiconductor manufacturing

Preventive maintenance (PM) scheduling is a very challenging task in semiconductor manufacturing due to the complexity of highly integrated fab tools and systems, the interdependence between PM tasks, and the balancing of work-in-process (WIP) with demand/throughput requirements. In this paper, we propose a two-level hierarchical modeling framework. At the higher level is a model for long-term planning, and at the lower level is a model for short-term PM scheduling. Solving the lower level problem is the focus of this paper. We develop mixed-integer programming (MIP) models for scheduling all due PM tasks for a group of tools, over a planning horizon. Interdependence among different PM tasks, production planning data such as projected WIP levels, manpower constraints, and associated PM time windows and costs, are incorporated in the model. Results of a simulation study comparing the performance of the model-based PM schedule with that of a baseline reference schedule are also presented.     

A Noise-Robust Convex-Optimized Positioning System Based on Code-Aided RSS Estimation and Virtual Base Station Transform

Parallelization of Digital Signal Processing (DSP) software is an important trend in Multiprocessor System-on-Chip (MPSoC) implementation. The performance of DSP systems composed of parallelized computations depends on the scheduling technique, which must in general allocate computation and communication resources for competing tasks, and ensure that data dependencies are satisfied. In this paper, we formulate a new type of parallel task scheduling problem called Parallel Actor Scheduling (PAS) for MPSoC mapping of DSP systems that are represented as Synchronous Dataflow (SDF) graphs. In contrast to traditional SDF-based scheduling techniques, which focus on exploiting graph level (inter-actor) parallelism, the PAS problem targets the integrated exploitation of both intra- and inter-actor parallelism for platforms in which individual actors can be parallelized across multiple processing units. We first address a special case of the PAS problem in which all of the actors in the DSP application or subsystem being optimized are parallel actors (i.e., they can be parallelized to exploit multiple cores). For this special case, we develop and experimentally evaluate a two-phase scheduling framework with three work flows that involve particle swarm optimization (PSO) — PSO with a mixed integer programming formulation, PSO with simulated annealing, and PSO with a fast heuristic based on list scheduling. Then, we extend our scheduling framework to support the general PAS problem, which considers both parallel actors and sequential actors (actors that cannot be parallelized) in an integrated manner. We demonstrate that our PAS-targeted scheduling framework provides a useful range of trade-offs between synthesis time requirements and the quality of the derived solutions. We also demonstrate the performance of our scheduling framework from two aspects: simulations on a diverse set of randomly generated SDF graphs, and implementations of an image processing application and a software defined radio benchmark on a state-of-the-art multicore DSP platform.     

前言

人们对后3G的要求是:在全球范围内实现无缝覆盖,进行包括语音、文本、图像、视频等在内的高速多媒体通信。为此,在有限频谱资源条件下,必须缩短无线信号的传输半径,极大限度地复用频谱资源,提高单位空间的信道容量。采用各种先进的无线传输技术的无线传输网络则在中、小范围内提供高速率、高质量的无线移动通信服务。因而WLAN和WPAN的需求和应用在不断增长,超宽带(UWB,ultra wide-band)等短距离、高空间容量的技术日益兴起,成为目前无线通信领域的热点。UWB的核心是冲激无线电技术,即利用持续时间非常短(纳秒、亚纳秒级)的脉冲波形来…     

一种基于HDTV信源集成解码芯片的RTOS的设计与实现

随着系统集成芯片（SOC）设计复杂度和上市时间等要求的提高，实时操作系统已成为SOC设计中的重要组成部分。HDTV信源集成解码芯片中的实时操作系统负责对进出芯片的数据进行管理，芯片上实现多个实时任务的同步和调度。本文介绍一个以较小内核实现的多任务实时操作系统Iota，它将总线的调度部分的转移到硬件实现，并针对解码芯片设计并实现了多任务混合调度算法。为提高系统的健壮性，Iota还具有一个简单的存储保护策略。     

Multicriteria decision‐making techniques for avoiding similar task scheduling conflict in cloud computing

An efficient task scheduling approach shows promising way to achieve better resource utilization in cloud computing. Various task scheduling approaches with optimization and decision‐making techniques have been discussed up to now. These approaches ignored scheduling conflict among the similar tasks. The conflict often leads to miss the deadlines of the tasks. The work studies the implementation of the MCDM (multicriteria decision‐making) techniques in backfilling algorithm to execute deadline‐based tasks in cloud computing. In general, the tasks are selected as backfill tasks, whose role is to provide ideal resources to other tasks in the backfilling approach. The selection of the backfill task is challenging one, when there are similar tasks. It creates conflict in the scheduling. In cloud computing, the deadline‐based tasks have multiple parameters such as arrival time, number of VMs (virtual machines), start time, duration of execution, and deadline. In this work, we present the deadline‐based task scheduling algorithm as an MCDM problem and discuss the MCDM techniques: AHP (Analytical Hierarchy Process), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to avoid similar task scheduling conflicts. We simulate the backfilling algorithm along with three MCDM mechanisms to avoid scheduling conflicts among the similar tasks. The synthetic workloads are considered to study the performance of the proposed scheduling algorithm. The mechanism suggests an efficient VM allocation and its utilization for deadline‐based tasks in the cloud environment.