多核系统中基于Global EDF 的在线节能实时调度算法

随着多核系统能耗问题日益突出,在满足时间约束条件下降低系统能耗成为多核实时节能调度研究中亟待解决的问题之一.现有研究成果基于事先已知实时任务属性的假设,而实际应用中,只有当任务到达之后才能够获得其属性.为此,针对一般任务模型,不基于任何先验知识提出一种多核系统中基于Global EDF在线节能硬实时任务调度算法,通过引入速度调节因子,利用松弛时间,结合动态功耗管理和动态电压/频率调节技术,降低多核系统中任务的执行速度,达到实时约束与能耗节余之间的合理折衷.所提出的算法仅在上下文切换和任务完成时进行动态电压/频率调节,计算复杂度小,易于在实时操作系统中实现.实验结果表明,该算法适用于不同类型的片上动态电压/频率调节技术,节能效果始终优于Global EDF算法,最多可节能15％～20％,最少可节能5％～10％.     

面向移动云计算任务调度的改进鸟群算法研究

针对移动云计算环境下的任务调度存在耗时长、设备能耗高的问题,提出了一种基于改进的鸟群算法(improved bird swarm algorithm,IBSA)的任务调度策略。首先,构建了以能耗和时间为主的移动云任务调度模型;其次,提出了自适应感知系数和社会系数,避免了算法陷入局部最优;构建了学习因子优化飞行行为,保证了个体寻优能力;最后,任务调度目标函数作为鸟群个体的适应度函数参与算法的迭代更新。仿真结果表明相比于蚁群算法、粒子群算法、鲸鱼算法等,改进的鸟群算法在移动云计算任务调度方面具有良好的效果,能够有效地节省时间和降低能耗。     

Rolling-horizon scheduling for energy constrained distributed real-time embedded systems

Energy-efficient scheduling approaches are critical to battery driven real-time embedded systems. Traditional energy-aware scheduling schemes are mainly based on the individual task scheduling. Consequently, the scheduling space for each task is small, and the schedulability and energy saving are very limited, especially when the system is heavily loaded. To remedy this problem, we propose a novel rolling-horizon (RH) strategy that can be applied to any scheduling algorithm to improve schedulability. In addition, we develop a new energy-efficient adaptive scheduling algorithm (EASA) that can adaptively adjust supply voltages according to the system workload for energy efficiency. Both the RH strategy and EASA algorithm are combined to form our scheduling approach, RH-EASA. Experimental results show that in comparison with some typical traditional scheduling schemes, RH-EASA can achieve significant energy savings while meeting most task deadlines (namely, high schedulability) for distributed real-time embedded systems with dynamic workloads.     

动态储位分配策略下仓储作业能耗优化调度

针对堆垛机式自动化立体仓库能耗优化任务调度问题,提出了一种动态储位分配策略下的任务调度集成优化方法。该策略允许在当前任务执行批次内重用拣货产生的空库位,产生了任务顺序约束;并结合不同运动状态下堆垛机的运行时间计算差异,建立了复合指令下以堆垛机任务总能耗最低为目标的调度模型。依据模型的特点,提出了一种改进帝国竞争算法进行求解。该算法通过设置校正机制使优化解能够满足任务顺序约束,并引入外来种群入侵的概念来避免算法陷入“早熟”。最后通过两组数据对提出的任务调度算法进行了验证,实验结果表明,改进算法在能耗和任务执行效率方面优于其他方法,能够有效减少堆垛机作业过程能耗,提升作业效率。     

云工作流系统中能耗感知的任务调度算法*

云工作流系统研究集中在工作流任务执行的时间效率优化,然而时间最优的任务调度方案可能存在不同能耗,因此,文中求解满足时间约束时能耗最优的调度方案。首先改进任务执行能耗模型,设计适用于评价任务调度方案执行能耗的适应度计算方法。然后基于精准调整粒子速度的自适应权重,提出解决任务调度能耗优化问题的自适应粒子群算法。实验表明,文中算法收敛稳定,调度方案执行能耗较低。     

基于混合蚁群优化的边缘计算细粒度任务调度方法

为缓解中心服务器的压力,制定合理的调度方案,基于混合蚁群优化算法提出了边缘计算细粒度任务调度方法。描述边缘计算任务调度问题,并设置假设条件,简化调度求解难度。通过计算任务的优先指数,按照从大到小的顺序排列后组成任务队列。分析边缘服务器性能特征,明确边缘服务器处理能力。构建能耗以及时延多目标函数,并设置约束条件,利用混合蚁群优化算法求解多目标函数,完成边缘计算细粒度任务调度方案设计。结果表明：该方法应用下的任务调度能耗和时延更小,说明所提方法性能更优,所获得的调度方案更合理。     

面向温度优化的片上网络任务调度方法

随着片上网络规模的扩大和研究的逐步深入,如何将芯片上众多的任务进行合理的调度成为系统温度优化的关键之一。针对片上网络任务调度问题, 提出一种基于最短曼哈顿距离的任务调度SMDS方案。该策略充分考虑核通信图中通信节点对之间最短曼哈顿路径,通过搜索算法寻找任务调度的目的节点,使用模拟退火算法确定任务调度对。实验结果显示,与传统的分布式任务调度 DTM策略相比,针对6*6、8*8和10*10的拓扑结构,SMDS实验方案在迁移次数方面的平均优化率分别为2208%、21.74%和23.02%。在平均跳数方面的平均优化率分别为24.04%、29.18%和23.04%,实现了系统温度优化。     

A multi-objective co-evolutionary algorithm for energy-efficient scheduling on a green data center

Nowadays, the environment protection and the energy crisis prompt more computing centers and data centers to use the green renewable energy in their power supply. To improve the efficiency of the renewable energy utilization and the task implementation, the computational tasks of data center should match the renewable energy supply. This paper considers a multi-objective energy-efficient task scheduling problem on a green data center partially powered by the renewable energy, where the computing nodes of the data center are DVFS-enabled. An enhanced multi-objective co-evolutionary algorithm, called OL-PICEA-g, is proposed for solving the problem, where the PICEA-g algorithm with the generalized opposition based learning is applied to search the suitable computing node, supply voltage and clock frequency for the task computation, and the smart time scheduling strategy is employed to determine the start and finish time of the task on the chosen node. In the experiments, the proposed OL-PICEA-g algorithm is compared with the PICEA-g algorithm, the smart time scheduling strategy is compared with two other scheduling strategies, i.e., Green-Oriented Scheduling Strategy and Time-Oriented Scheduling Strategy, different parameters are also tested on the randomly generated instances. Experimental results confirm the superiority and effectiveness of the proposed algorithm.     

云环境下超启发式能耗感知调度算法

能耗感知调度的研究对云计算数据中心的可持续发展有着重要意义。能耗感知调度是一个NP难的多目标优化问题,目前云环境下的任务调度算法较少考虑能耗问题,且不能实现对能耗的灵活管理,随机搜索算法是一种解决该问题的有效途径,但其计算开销大,收敛速度慢。将异构云环境下的能耗感知调度问题定义为一个带约束的问题,即在一定的完成时间下优化系统能耗,以实现对能耗的灵活管理。此外,提出了基于在线学习的超启发式算法（OLHH）,该算法结合电压调节技术,在设计了简单高效的启发式策略集的基础上,引进超启发式算法,并采用在线学习的方式跟踪启发式策略的表现,实现对启发式策略的合理管理,从而达到提高算法的收敛性能的目的。模拟实验表明,该算法能够实现系统能耗的灵活管理,且比传统的随机搜索算法有着更好的收敛性能。     

基于时间帧的处理器PFair调度改进算法

为了解决PFair算法进行交互任务调度时,由于忽略了不同阶段的周期性任务而导致多个线程之间任务的迁移问题以及空间和时间的浪费问题,提出了基于时间帧的处理器PFair调度改进算法。该算法基于周期性任务系统的特点,引入时间帧控制和改变本地周期性任务调度来限制任务迁移,从而实现对PFair算法的改进。为了评估算法的迁移开销和公平性,通过实验对普通PFair算法及本文所提出的改进算法ERfair进行对比实验,结果表明,改进算法ERfair能够通过时间帧内调度和分区控制大大降低任务在不同处理器间的迁移次数。基于时间帧的处理器PFair调度改进算法在保证公平性的同时,提高了系统的效率,应用于多核处理器上的任务调度是可行的、有效的。     

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.     

基于改进烟花算法的密集任务成像卫星调度方法

传统卫星调度模型一般比较简单,当问题规模较大、任务比较集中时,往往会出现任务之间相互排斥,任务收益较低等缺点。针对这个问题,提出一种基于改进烟花算法（IFWA）的密集任务成像卫星调度方法。该方法在分析密集任务处理及成像卫星观测特点的基础上,首先对任务进行合成约束分析,然后基于合成任务综合考虑成像卫星可观测时间、任务间姿态调整时间、成像卫星能量和容量等约束因素,建立基于任务合成的多星密集任务调度约束满足问题（CSP）模型,最后改进烟花算法对该模型进行求解,利用精英选择策略在保证种群多样性同时加快了算法的收敛,得到较优的卫星调度方案。仿真结果表明该模型相比没有考虑任务合成因素,收益平均增加30%~35%,改进算法后效率上提升32%~45%,有效保证了调度方案的可行性和有效性。     

改进蚁群算法的云计算任务调度方法

针对蚁群算法在云计算任务调度问题求解过程存在的不足,以找到最佳的云计算任务调度方案为目标,提出了一种基于改进蚁群算法的云计算任务调度方法.首先对当前云计算任务调度研究现状进行分析,并对问题进行了具体描述,然后采用蚁群算法对云计算任务调度问题进行求解,并针对标准蚁群算法缺陷进行改进,最后在CloudSim平台对该方法的性能进行测试.结果表明,改进蚁群算法可以找到较好的云计算任务问题调度方案,加快云计算任务完成速度,具有一定的实际应用价值.     

A scheduling algorithm for applications in a cloud computing system with communication changes

This paper proposes a scheduling algorithm to solve the problem of task scheduling in a cloud computing system with time‐varying communication conditions. This algorithm converts the scheduling problem with communication changes into a directed acyclic graph (DAG) scheduling problem for existing fuzzy communication task nodes, that is, the scheduling problem for a communication‐change DAG (CC‐DAG). The CC‐DAG contains both computation task nodes and communication task nodes. First, this paper proposes a weighted time‐series network bandwidth model to solve the indefinite processing time (cost) problem for a fuzzy communication task node. This model can accurately predict the processing time of a fuzzy communication task node. Second, to address the scheduling order problem for the computation task nodes, a dynamic pre‐scheduling search strategy (DPSS) is proposed. This strategy computes the essential paths for the pre‐scheduling of the computation task nodes based on the actual computation costs (times) of the computation task nodes and the predicted processing costs (times) of the fuzzy communication task nodes during the scheduling process. The computation task node with the longest essential path is scheduled first because its completion time directly influences the completion time of the task graph. Finally, we demonstrate the proposed algorithm via simulation experiments. The experimental results show that the proposed DPSS produced remarkable performance improvement rate on the total execution time that ranges between 11.5% and 21.2%. In view of the experimental results, the proposed algorithm provides better quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment than HEFT, PEFT, and CEFT algorithms.     

Scheduling with dynamic voltage/speed adjustment using slack reclamation in multiprocessor real-time systems

The high power consumption of modern processors becomes a major concern because it leads to decreased mission duration (for battery-operated systems), increased heat dissipation, and decreased reliability. While many techniques have been proposed to reduce power consumption for uniprocessor systems, there has been considerably less work on multiprocessor systems. In this paper, based on the concept of slack sharing among processors, we propose two novel power-aware scheduling algorithms for task sets with and without precedence constraints executing on multiprocessor systems. These scheduling techniques reclaim the time unused by a task to reduce the execution speed of future tasks and, thus, reduce the total energy consumption of the system. We also study the effect of discrete voltage/speed levels on the energy savings for multiprocessor systems and propose a new scheme of slack reservation to incorporate voltage/speed adjustment overhead in the scheduling algorithms. Simulation and trace-based results indicate that our algorithms achieve substantial energy savings on systems with variable voltage processors. Moreover, processors with a few discrete voltage/speed levels obtain nearly the same energy savings as processors with continuous voltage/speed, and the effect of voltage/speed adjustment overhead on the energy savings is relatively small.     

异构集群下的任务调度算法研究

针对异构集群下高效节能的任务调度算法进行了研究, 提出了一种基于复制的任务调度算法, 在任务初始分配的基础上, 分别从能源感知和性能—能源平衡两个角度考虑任务的复制。建立了由计算和通信造成的能源消耗的数学模型, 并进行了大量的实验。实验结果表明, 与已有的BEATA算法相比, 该算法能明显地减少异构集群处理并行应用的调度长度和能耗。分析结果发现, 任务复制的方法在减少调度长度的同时会增加相应的能耗, 能同比优化调度长度和能耗的任务调度方法是今后的研究方向。     

异构系统中基于可用性的粒子群任务调度算法

任务调度技术是并行分布式系统中的关键技术之一,对系统的性能起着重要作用,但通常情况下大型系统的任务调度问题属于NP问题。而现代启发式生物进化算法是找出很多NP问题近似解的有效方法。本文将粒子群算法应用于基于可用性的网格系统调度中,提出了一种调度算法,对算法的性能进行了理论分析和模拟实验。结果表明:和最近文献中的基于可用性的调度算法SSAC相比,所提出的新算法在保证系统资源具有同样的可用性条件下,能够产生更好的调度长度。     

基于GSPN和人工免疫算法的并行测试任务调度研究

针对ATS中并行测试任务调度复杂、难以优化的问题,提出了一种广义随机Petri网和人工免疫算法相结合的任务调度优化算法.首先对并行测试系统建立广义随机Petri网(GSPN)模型,然后将激发的变迁序列集作为并行测试任务调度路径;将免疫克隆选择算法(ICSA)应用到并行测试系统任务调度问题中,并提出一种自适应克隆选择算子,搜索最优任务调度路径,得到以测试时间最短为目标的最优任务调度方案.用某型雷达接收机并行测试系统对该算法进行仿真验证,结果表明,与改进的混合遗传算法(IHGA)相比,该算法能够便捷地得到任务调度最优序列,且测试效率更高.     

基于多尺度量子谐振子算法的云计算任务调度

合理地分配虚拟计算资源以进行有效的任务调度是云计算中的一个核心问题。为了更好地利用虚拟计算资源,高效地完成服务需求,提出了一种基于多尺度量子谐振子算法（MQHOA）的任务调度算法。首先,该算法将每一个调度方案当成一个采样位置,利用高斯采样的随机性在当前尺度下搜索局部最优解;其次,判断算法是否处于能级稳定状态,如果稳定,则进入能级降低过程,最坏的调度方案将被替换;最后,算法进入尺度下降的过程,算法由全局搜索过渡到局部搜索,迭代多次之后,算法停止并输出找到的最优结果。通过在CloudSim平台上进行仿真实验,与现有的先来先服务（FCFS）算法和粒子群优化（PSO） 算法对比,MQHOA总任务完成时间减少10%以上,负载不均值下降0.4以上。实验结果表明,基于MQHOA的任务调度算法能够快速收敛,有良好的全局收敛性和自适应能力,在云计算任务调度过程中,能够起到减少总任务完成时间和均衡负载的作用。     

非对称多核处理器上的操作系统集成调度

相对于对称多核处理器,非对称多核处理器具有更高的效能,将成为未来并行操作系统中的主流体系结构.对于非对称多核处理器上操作系统的并行任务调度问题,现有的研究假设所有核心频率恒定,缺乏理论分析,也没有考虑算法的效能和通用性.针对该问题,该文首先建立非线性规划模型,分析得出全面考虑并行任务同步特性、核心非对称性以及核心负载的调度原则.然后,基于调度原则提出一个集成调度算法,该算法通过集成线程调度和动态电压频率调整来提高效能,并通过参数调整机制实现了算法的通用性.提出的算法是第一个在非对称多核处理器上结合线程调度和动态电压频率调整的调度算法.实际平台上的实验表明:该算法可适用于多种环境,且效能比其他同类算法高24%～50%.