基于任务同步及节能的单机系统实时动态调度算法

提出了一种基于任务同步及节能的单机系统实时混合动态调度算法(HDSA), 以有效解决能耗及实时任务同步时的优先权反转所导致的缺乏实时可调度性的问题.HDSA包含静态算法及动态算法两部分.静态算法可在静态条件下, 固定临界区的运行速度, 并求出非临界区部分的静态速度. 动态调度算法在实际运行中, 在满足周期性任务实时可调度性及任务同步的条件下, 充分利用及回收任务运行时剩余的执行时间, 调节处理器的速度, 以有效降低能耗.同时也能避免高优先权任务被阻塞时, 低优先权任务的临界区继承高优先权任务的速度所造成的处理器电压开关的频繁切换, 故能有效地降低实时任务调度的成本.实验测试表明HDSA在调度性能上明显优于相关的有效算法.     

A hybrid task scheduling scheme for multicore processors based on improving the EDF algorithm

为解决多核处理器系统中的实时任务调度问题,尤其是实时任务和非实时任务的混合调度问题,在对最早截止时间优先(EDF)算法进行改进的基础上,提出多核处理器混合任务调度算法——EDF-segment算法.EDF-segment算法可以整理调度混合任务时出现的碎片,并通过对碎片的迁移、合并提高处理器的利用率,从而提高系统处理混合任务的性能.通过EDF-segment算法不但可以解决混合任务的调度问题,还可以避免使用EDF算法时造成的多核处理器利用率下降,在保证实时任务处理延迟的前提下提升多核处理器的利用率.经过理论推导和实验分析证明,EDF-segment算法可以有效地应用于多核处理器系统中.     

A reliability-cost and Makespan driven fault-tolerant scheduling algorithm for distributed systems

为解决异构分布式环境下采用主副版本策略的可靠性调度问题,提出一种基于优先级约束的可靠性代价和Makespan(调度时长)驱动的分布式容错调度算法DRCAMD.该算法可在满足系统可调度性的前提下,以异构分布式环境的节点、通信链路的可靠性与Makespan做为可调节局部目标函数,实现具有较高可靠性及较短执行时间的容错调度策略,避免将任务分配到失效率较高的节点上执行.另外,算法的副版本采用被动和主副重叠方式执行,使得容错调度算法具有较大的灵活性.仿真实验表明,该算法性能优于现有容错算法.     

基于改进A*算法的仓储物流移动机器人 任务调度和路径优化研究

为解决仓储物流中移动机器人执行订单任务过程中,调度系统难以快速准确地进行任务分配,且搬运路线并非最短最优路线的问题,根据移动机器人的运动方式和订单任务要求,构建具有可重构性的仓库空间模型和栅格地图模型,通过建立数学模型求解订单任务最短完工时间分配问题,改进传统A^*算法中3种常用距离算法的不足,并提出复杂对角线距离算法进行路径规划仿真。仿真结果表明,上述方法实现总任务完工时间最短的任务分配,使路径规划搜索节点数减少30%,路径长度缩短20%。     

面向无人机物流的分布式任务调度

为提高无人机物流任务调度系统的实时性、可靠性和扩展性,本文提出一种面向通用航空无人机物流的分布式自主任务调度系统,并阐述了该系统的概念、架构、机制和运行模式;为实现物流任务的高效分配,适用于常规物流及紧急物流任务,结合集中调度及边缘自主两种调度模式。实验证明：该系统可为通用航空无人机物流提供一种有效途径,成为未来无人机物流的主要调度模式之一。     

一类资源负荷均衡问题的优化调度算法

针对一类n个独立任务在m个不完全同等的处理机上处理，使处理机的最大负荷为最小的非抢先调度问题，提出了一种启发式算法－－最小平衡算法，并分析了它的时间复杂性，在此基础上，又将最小平衡算法和遗传算法结合起来，提出了基于遗传的最小平衡算法，并用实例证实了该算法的有效性。     

一类资源负荷均衡问题的双最小平衡调度算法

针对一类n个独立任务在m个不完全同等的处理机上处理，使处理机的最大负荷为最大的非抢先调度问题，在最小平衡算法的基础上提出了双最小平衡算法，并分析了它的时间复杂性，在此基础上，提出了基于遗传的双最小平衡算法，并通过实例证实了它在结果上优于基于遗传的最小平衡算法。     

滤光片表面缺陷视觉检测系统多处理器调度优化

针对滤光片表面缺陷视觉检测系统中在线检测实时性需求对检测速度要求较高,研究一种有效利用可用硬件资源并行处理实时工作提高处理速度的调度优化策略。基于AOE图对滤光片表面缺陷视觉检测系统进行任务级分析,优化事件、活动拓扑关系与任务间冗余的数据相关性、资源相关性,建立并行任务模型;采用关联处理器调度算法(arbitrary processor affinities,APAs)进行并行多处理器调度,指定任务只能被某个处理器集合执行,将期限紧迫、缓存敏感的任务限制在单一处理器,提高资源利用率,改进检测系统实时性。试验结果表明:在尺寸为1.20mm×1.20mm、26×28个滤光片组成滤光片面板上,采用多处理器调度可使检测速度极大提升,采用APAs调度算法后,平均缺陷识别完成时间为常规检测系统时间的36.5%,可以满足在线实时要求,证明应用多处理器调度方法,可以极大提升检测仪器实时性能的有效性。     

规则启发式算法在大规模飞机脉动式总装生产线的应用研究

针对大规模飞机脉动式总装生产线计划辅助制定和执行的问题,本文在生产计划制定阶段采取二分迭代最小松弛度优先（BIMSLK）算法求站位间平衡的解,在计划执行阶段沿用反应性调度策略,采用最小松弛度优先（MSLK）算法计算站位内工序的最短工期。经过算例验证,在计划制定阶段,BIMSLK算法可以在有效时间内得到规模较大工艺的站位间平衡的解;在任务执行阶段,与最大总资源需求（TDR）算法和最多紧后工序数优先（MIS）算法相比,本文采用MSLK算法得到的解的工期更短。     

在Linux下改进EDF实时调度，

EDF（Earliest Deadline First）算法由于CPU利用率高，可调度的任务集较大等优点在实时系统中的得到广泛的应用，但是EDF算法无法保证重要任务的执行。本文在EDF算法的基础上结合任务的重要性，提出了SBID（Scheduler Based Importance and Deadline）调度算法。首先从理论的角度分析了SBID算法的可调度性，并针对SBID算法对Linux2．6系统内核作一些修改。然后通过实验数据对比表明了SBID算法在保证重要任务顺利运行方面具有较好的优越性。     

面向“货到人”拣选系统的一种随机调度策略

通过“货到人”拣选系统作业流程分析,提出了在分批下发订单任务的情况下的一种随机调度策略。以AGV (automated guided vehicle)完成所有任务的总时间最短为目标函数,以任务分配为决策变量,考虑进行调度时AGV所处的状态以及在完成任务过程中AGV在拣选台的排队等待时间,建立随机调度策略的数学规划模型。利用遗传算法进行求解,通过实例仿真,验证了随机调度策略较调度空闲AGV策略具有更高的拣选效率,同时解决了AGV调度与拣选序列问题,对AGV数量配置具有指导作用。     

BDCP:一个有效的相关任务调度算法

表调度算法ＢＤＣＰ采用动态关键路径技术并均衡考虑关键路径结点和非关键路径结点,使得对相关任务图调度长度影响最大的就绪得结点能够被优先调度,从而极大地缩短了调度长度。分析和实验结果表明,该算法要明显优于ＭＣＰ和ＥＴＦ算法。     

一类多资源调度问题的优化算法

若调度系统中的作业或任务需要一个或多个资源共同完成， 则这一类调度问题被称为多资源（或多处理机）调度问题。本文针对Jobshop中的这一调度问题，提出了基于遗传的优化调度算法，并用实例证实了该算法的有效性。     

A duplication heuristic for static scheduling of tasks on distributed memory multiprocessors

Abstract

A task duplication heuristic, DSH, was proposed in [11]. The underlying concept of the task duplication heuristic is duplicating some tasks on processors such that the earliest start time of tasks on processors can be reduced, that is, tasks on processors can be executed sooner. This leads to a better scheduling length. In this paper, we propose a more general task duplication heuristic, bottom‐up top‐down duplication heuristic (BTDH), for static scheduling of directed‐acyclic graphs (DAGs) on distributed memory multiprocessors. The key difference between BTDH and DSH is the method used for duplicating tasks. BTDH allows tasks to be duplicated on processors even though the duplication of tasks will temporarily increase the earliest start time of some tasks. DSH only allows those duplications which will reduce the earliest start time of tasks. Simulation results show that, for coarse‐grain DAGs, the scheduling length of BTDH is almost the same as the scheduling length of DSH. However, for medium‐grain and fine‐grain DAGs, BTDH produces better scheduling length than DSH.     

A genetic algorithm for the multi-stage and parallel-machine scheduling problem with job splitting – A case study for the solar cell industry

This paper studies a multi-stage and parallel-machine scheduling problem with job splitting which is similar to the traditional hybrid flow shop scheduling (HFS) in the solar cell industry. The HFS has one common hypothesis, one job on one machine, among the research. Under the hypothesis, one order cannot be executed by numerous machines simultaneously. Therefore, multiprocessor task scheduling has been advocated by scholars. The machine allocation of each order should be scheduled in advance and then the optimal multiprocessor task scheduling in each stage is determined. However, machine allocation and production sequence decisions are highly interactive. As a result, this study, motivated from the solar cell industry, is going to explore these issues. The multi-stage and parallel-machine scheduling problem with job splitting simultaneously determines the optimal production sequence, multiprocessor task scheduling and machine configurations through dynamically splitting a job into several sublots to be processed on multiple machines. We formulate this problem as a mixed integer linear programming model considering practical characteristics and constraints. A hybrid-coded genetic algorithm is developed to find a near-optimal solution. A preliminary computational study indicates that the developed algorithm not only provides good quality solutions but outperforms the classic branch and bound method and the current heuristic in practice.     

Improvised Seagull Optimization Algorithm for Scheduling Tasks in Heterogeneous Cloud Environment

Well organized datacentres with interconnected servers constitute the cloud computing infrastructure. User requests are submitted through an interface to these servers that provide service to them in an on-demand basis. The scientific applications that get executed at cloud by making use of the heterogeneous resources being allocated to them in a dynamic manner are grouped under NP hard problem category. Task scheduling in cloud poses numerous challenges impacting the cloud performance. If not handled properly, user satisfaction becomes questionable. More recently researchers had come up with meta-heuristic type of solutions for enriching the task scheduling activity in the cloud environment. The prime aim of task scheduling is to utilize the resources available in an optimal manner and reduce the time span of task execution. An improvised seagull optimization algorithm which combines the features of the Cuckoo search (CS) and seagull optimization algorithm (SOA) had been proposed in this work to enhance the performance of the scheduling activity inside the cloud computing environment. The proposed algorithm aims to minimize the cost and time parameters that are spent during task scheduling in the heterogeneous cloud environment. Performance evaluation of the proposed algorithm had been performed using the Cloudsim 3.0 toolkit by comparing it with Multi objective-Ant Colony Optimization (MO-ACO), ACO and Min-Min algorithms. The proposed SOA-CS technique had produced an improvement of 1.06%, 4.2%, and 2.4% for makespan and had reduced the overall cost to the extent of 1.74%, 3.93% and 2.77% when compared with PSO, ACO, IDEA algorithms respectively when 300 vms are considered. The comparative simulation results obtained had shown that the proposed improvised seagull optimization algorithm fares better than other contemporaries.     

Multi-agent-based approach to solve part selection and task allocation problem in flexible manufacturing systems

Agent technology is currently being considered as an important approach for developing intelligent manufacturing systems. It offers a new way of thinking about many of the classical problems in manufacturing engineering. A multi-agent-based approach for solving the part allocation problems in flexible manufacturing systems (FMS) is presented that can easily cope with the dynamic environment. Four agents were involved in carrying out the tasks of allocating parts on different machines: communicator, machine, part and material handling device (MHD). Upon arrival in the manufacturing facility, the part informs the communicator agent about the task requirements. The communicator agent divides the task into subtasks and sends a call-for-bids message to the machine and MHD agents. Each machine responds in accordance with its process capabilities and buffer limit. This response may be for the whole task or for one or more subtasks and it contains the price and cost details for these subtasks along with the performance index and acceptance ratio of the machine. The final allocation is made based on the objective function that includes processing and transportation costs and time. An algorithm is presented that is used by the communicator agent for allocating parts to different machines. An illustrative example is given to solve the task allocation on five machines, with each machine having different performance index and acceptance ratio.     

Task Scheduling Optimization in Cloud Computing by Rao Algorithm

Cloud computing is currently dominated within the space of high-performance distributed computing and it provides resource polling and on-demand services through the web. So, task scheduling problem becomes a very important analysis space within the field of a cloud computing environment as a result of user's services demand modification dynamically. The main purpose of task scheduling is to assign tasks to available processors to produce minimum schedule length without violating precedence restrictions. In heterogeneous multiprocessor systems, task assignments and schedules have a significant impact on system operation. Within the heuristic-based task scheduling algorithm, the different processes will lead to a different task execution time (makespan) on a heterogeneous computing system. Thus, a good scheduling algorithm should be able to set precedence efficiently for every subtask depending on the resources required to reduce (makespan). In this paper, we propose a new efficient task scheduling algorithm in cloud computing systems based on RAO algorithm to solve an important task and schedule a heterogeneous multiple processing problem. The basic idea of this process is to exploit the advantages of heuristic-based algorithms to reduce space search and time to get the best solution. We evaluate our algorithm's performance by applying it to three examples with a different number of tasks and processors. The experimental results show that the proposed approach significantly succeeded in finding the optimal solutions than others in terms of the time of task implementation.     

An Optimized Resource Scheduling Strategy for Hadoop Speculative Execution Based on Non-cooperative Game Schemes

Hadoop is a well-known parallel computing system for distributed computing and large-scale data processes. “Straggling” tasks, however, have a serious impact on task allocation and scheduling in a Hadoop system. Speculative Execution (SE) is an efficient method of processing “Straggling” Tasks by monitoring real-time running status of tasks and then selectively backing up “Stragglers” in another node to increase the chance to complete the entire mission early. Present speculative execution strategies meet challenges on misjudgement of “Straggling” tasks and improper selection of backup nodes, which leads to inefficient implementation of speculative executive processes. This paper has proposed an Optimized Resource Scheduling strategy for Speculative Execution (ORSE) by introducing non-cooperative game schemes. The ORSE transforms the resource scheduling of backup tasks into a multi-party non-cooperative game problem, where the tasks are regarded as game participants, whilst total task execution time of the entire cluster as the utility function. In that case, the most benefit strategy can be implemented in each computing node when the game reaches a Nash equilibrium point, i.e., the final resource scheduling scheme to be obtained. The strategy has been implemented in Hadoop-2.x. Experimental results depict that the ORSE can maintain the efficiency of speculative executive processes and improve fault-tolerant and computation performance under the circumstances of Normal Load, Busy Load and Busy Load with Skewed Data.     

Solving the spatial scheduling problem: a two-stage approach

The spatial scheduling problem that arises in hull block assembly shops occurs when scheduling and spatial allocation of the blocks must be considered simultaneously. We present a two-stage approach to this type of problem. The first stage aims to reduce the number of blocks. The second stage optimises the scheduling and spatial allocation of blocks using nonlinear mixed integer programming (NMIP) methods. The procedure proposed in this paper uses an agglomeration algorithm (AA) for the blocks. This procedure is based on the space–time coupling mechanism. The AA is a three-dimensional classification used to cluster blocks linked closely in time and space into virtual blocks. Extensive computational results from real cases are presented to demonstrate the effectiveness of the proposed approach, demonstrating a significant improvement over results obtained from existing methods.