基于希尔排序的动态优先级调度算法

实时调度策略中,EDF算法应用最为广泛,但其在系统过载的情况下,仅由任务截止期决定任务执行顺序,使得截止期错失率非常高,且系统收益小.近年来,出现了一些改进的EDF算法,综合考虑了时间和执行价值,但未加入能量因素,对于能量有限的系统,充分利用能量是极其重要的.针对这一问题,提出一种基于希尔排序的动态优先级调度算法,在系统过载时,综合考虑任务截止时间、执行价值、消耗能量三种因素确定任务优先级,通过希尔排序算法选出优先级高的任务加入优先调度子集,进行率先调度.实验结果表明,该算法不仅能降低任务截止期错失率,还能提高系统执行收益.     

多功能相控阵雷达实时任务调度研究

针对多功能相控阵雷达资源调度问题,建立了合理的雷达任务模型并提出一种新的调度算法.在雷达任务模型中将每一类驻留请求合并为一种任务,这样可以为调度处理提供最大的灵活性,同时基于此任务模型分析了调度器的时间负载.所提出的调度算法综合考虑了任务的工作方式优先级和截止期两个参数,可以较好地适应不同的负载情况.给出了算法的具体实现步骤,并以截止期错失率作为评估指标进行了仿真验证.仿真结果表明,本文所提出的调度算法能够有效降低任务的截止期错失率,对调度性能有明显的改善.     

异构多处理器系统的混合任务调度算法

针对实时异构系统的任务调度问题,提出了一种异构多处理器系统的混合实时任务调度算法.该算法采用带有非周期服务器的EDF( Earliest Deadline First)算法来调度单处理器上的任务集,可充分利用处理器的计算带宽.采用启发式搜索算法来进行任务的分配,以最大剩余计算带宽为搜索指标,可确保各处理器的负载尽量平衡...     

多功能一体化雷达任务调度算法研究

雷达、电子战、通信等多功能电子系统一体化是雷达的发展方向之一,资源管理与调度技术是一体化雷达的关键技术。针对基于孔径分割实现雷达、电子对抗、通信等多种功能的一体化系统的任务调度问题,对系统任务建模、调度算法设计、算法评价指标进行了探讨。在研究常规相控阵雷达调度策略的基础上,提出了采用多任务并行EDF(Earliest Deadline First)算法来实现系统的自适应调度。最后对比常规多功能雷达的自适应调度进行了仿真比较,且对仿真结果进行了定量分析,结果表明采用多任务并行EDF(MTPEDF)算法的基于孔径分割的一体化雷达系统具有一定的优越性。     

最早截止期优先算法在CAN FD网络中的改进

针对应用于CAN FD网络中的调度算法,平均分区编码方式的最早截止期算法对报文进行非抢占调度时,其对大范围的截止期编码能力有限,报文易出现较大概率优先级反转以及总线负载较高等问题。通过分析造成报文传递延迟的各种原因并结合之前相关分区调度算法的不足,文中提出了基于指数–幂函数分区的最早截止期优先算法对报文进行调度的改进方式,即在对报文的截止期进行指数分区的基础上,进一步采用幂函数分区细分。文中对该算法的可调度性进行了分析,并使用CANoe进行了仿真验证。实验表明,与现有的平均分区调度算法相比,改进后的算法扩大了截止期的表示范围,降低了总线负载,优化了优先级反转问题,达到了更好的调度效果。     

汽车BCM的故障诊断设计及实现

本文针对BCM的负载控制功能设计了一种电流反馈电路及过载、短路、开路故障诊断方法,能够实时监测负载的工作电流,根据负载电流的变化情况及驱动方案选型,准确判断出是否发生过载故障、短路故障及开路故障,以正常启动或关断负载,不仅能够实现既定的负载控制功能,还能够在发生过载、短路故障时及时关断保护.     

基于系统负载概率分布的实时低功耗策略

针对以往实时低功耗策略只考虑单一任务的概率分布,而没有考虑任务间耦合关系的不足,本文提出了一种基于系统负载概率分布的实时低功耗算法(Frequency Adjustment to Reduce Power,FARP).FARP算法分两个步骤:(1)根据系统负载的概率分布是正在运行任务概率分布的卷积的结论,在每个任务释放的时候计算当前系统负载的概率分布;(2)根据离线状态下任务释放时系统负载最长的运行时间,并结合系统负载的概率分布,得到系统负载所需的频率分配,并获得其最低的统计功耗.另外,FARP算法根据实际情况作了一定的修正以满足应用的需要.实验结果表明,FARP算法与同类算法相比,至少可以降低30%的功耗,同时可以满足系统实时性的要求.     

无线传感器网络中带复杂联盟的自适应任务分配算法

针对无线传感器网络任务调度的实时性及节点计算及能量受限的特点,根据任务截止期赋予任务优先级,优先考虑高优先级任务,设计了一个无线传感器网络中带复杂联盟的自适应任务分配算法。为尽最大努力确保任务在截止期前完成,对截止期较为紧迫的任务采用历史信息生成历史联盟,并执行快速子任务分配算法;而对截止期较为宽裕的任务,在满足任务截止期约束条件下,以节点能耗和网络能量分布平衡为优化目标,采用矩阵的二进制编码形式,设计了一种离散粒子群优化算法以并行生成联盟,并执行基于负载和能量平衡的子任务分配算法。仿真实验结果表明所构造的自适应算法是有效的,在局部求解与全局探索之间能够取得较好的平衡,并能够在较短的时间内取得满意解。     

基于临界触发的LTE负载均衡算法

考虑LTE用户信噪比,借助物理资源块的服务能力推算小区负载的变化状态,提出的CTLB算法能够动态地根据每个小区的负载,自动计算需要进行用户转移的小区。引入均衡因子,使所有小区最终达到预先设定的收敛目标,从而有效利用网络资源。最后,通过MATLAB搭建了一个仿真平台,验证算法的收敛性能、过载用户数及小区吞吐量的变化情况。     

基于邻区集的LTE负载均衡算法

考虑LTE用户信噪比,借助物理资源块的服务能力推算小区负载的变化状态,提出的NSLB算法能够动态地根据每个小区的负载,自动计算其邻区集需要进行用户转移的小区。引入均衡因子,使所有小区最终达到预先设定的收敛目标,有效利用网络资源。最后,通过matlab搭建了一个仿真平台,验证算法的收敛性能、过载用户数及小区吞吐量的变化情况。     

Efficient scheduling discipline for Hierarchical Diff‐EDF

Packet networks are currently enabling the integration of traffic with a wide range of characteristics that extend from video traffic with stringent quality of service (QoS) requirements to the best‐effort traffic requiring no guarantees. QoS guarantees can be provided in conventional packet networks by the use of proper packet‐scheduling algorithms. As a computer revolution, many scheduling algorithms have been proposed to provide different schemes of QoS guarantees, with Earliest Deadline First (EDF) as the most popular one. With EDF scheduling, all flows receive the same miss rate regardless of their traffic characteristics and deadlines. This makes the standard EDF algorithm unsuitable for situations in which the different flows have different miss rate requirements since in order to meet all miss rate requirements it is necessary to limit admissions so as to satisfy the flow with the most stringent miss rate requirements. In this paper, we propose a new priority assignment scheduling algorithm, Hierarchal Diff‐EDF (Differentiate Earliest Deadline First), which can meet the real‐time needs of these applications while continuing to provide best‐effort service to non‐real time traffic. The Hierarchal Diff‐EDF features a feedback control mechanism that detects overload conditions and modifies packet priority assignments accordingly. Copyright © 2007 John Wiley & Sons, Ltd.     

EDF调度算法抢占行为的研究及其改进

通过对采用抢占式EDF算法的嵌入式系统中各实时任务抢占行为的分析,建立了一个周期性任务集的抢占模型,从数学上描述了抢占关系、可调度性、调度开销与实时任务的周期、执行时间、最终期限、启动时间等属性之间的关系.依据该抢占模型,提出了一个改进的抢占式EDF调度算法,通过将基于遗传算法的优化方法离线计算得到的实时任务启动时间作为目标系统的一个调度参数,减少抢占次数,改变抢占关系,从而提高系统的可调度能力和实时性能.最后用实验验证了改进的抢占式EDF调度算法的有效性.     

Application-directed voltage scaling

Clock (and voltage) scheduling is an important technique to reduce the energy consumption of processors that support voltage scaling. It is difficult, however, to achieve good results using only statistics from the operating system level when applications show bursty (unpredictable) behavior. We take the approach that such applications must be made power-aware and specify their average execution time (AET) and the deadline to the scheduler controlling the clock speed and processor voltage. This paper describes our energy priority scheduling (EPS) algorithm supporting power-aware applications. EPS orders tasks according to how tight their deadlines are and how often tasks overlap. Low-priority tasks are scheduled first, since they can be easily preempted to accommodate for high-priority tasks later. The EPS algorithm does not always yield the optimal schedule, but has a low complexity. We have implemented EPS on a StrongARM-based variable-voltage platform. We conducted experiments with a modified video decoder that estimates the AET of each frame. Measurements show that application-directed voltage scaling reduces processor power consumption with 50% for the bursty video decoder without missing any frame deadlines.     

ForCES网络中的资源调度算法和经济模型(英文)

Resource scheduling algorithm for ForCES(Forwarding and Control Element Separation) networks need to meet the flexibility,programmability and scalability of node resources.DBC(Deadline Budget Constrain) algorithm relies on users select cost or time priority,then scheduling to meet the requirements of users.However,this priority strategy of users is relatively simple,and cannot adapt to dynamic change of resources,it is inevitable to reduce the QoS.In order to improve QoS,we refer to the economic model and resource scheduling model of cloud computing,use SAL(Service Level Agreement) as pricing strategy,on the basis of DBC algorithm,propose an DABP(Deadline And Budget Priority based on DBC) algorithm for ForCES networks,DABP combines both budget and time priority to scheduling.In simulation and test,we compare the task finish time and cost of DABP algorithm with DP(Deadline Priority) algorithm and BP(Budget Priority) algorithm,the analysis results show that DABP algorithm make the task complete with less cost within deadline,benifical to load balancing of ForCES networks.     

Energy Efficient Real-Time Task Scheduling for Embedded Systems with Hybrid Main Memory

Available energy becomes a critical design issue for the increasingly complex real-time embedded systems. Phase Change Memory (PCM), with high density and low idle power, has recently been extensively studied as a promising alternative of DRAM. Hybrid PCM-DRAM main memory architecture has been proposed to leverage the low power of PCM and high speed of DRAM. In this paper, we propose energy-aware real-time task scheduling strategies for hybrid PCM-DRAM based embedded systems. Given the execution time variation when a task is loaded into PCM or DRAM, we re-design the static table-driven scheduling for a set of fixed tasks, as well as the Rate-Monotonic (RM) and Earliest Deadline First (EDF) scheduling policies for periodic task sets. Furthermore, since the actual execution time can be much shorter than the worst-case execution time in the actual execution, we propose online schedulers which migrates the tasks between PCM and DRAM to optimize the energy consumption by utilizing the slack time resulted from the completed tasks. All the proposed algorithms minimize the number of task migrations from PCM to DRAM by ensuring that aperiodic tasks are not migrated while each periodic task instance can be migrated at most once. Experimental results show our proposed scheduling algorithms satisfy the real-time constraints and significantly reduce the energy consumption.     

Synthesis of Hard Real-Time Application Specific Systems

This paper presents a system level approach for the synthesis of hard real-time multitask application specific systems. The algorithm takes into account task precedence constraints among multiple hard real-time tasks and targets a multiprocessor system consisting of a set of heterogeneous off-the-shelf processors. The optimization goal is to select a minimal cost multi-subset of processors while satisfying all the required timing and precedence constraints. There are three design phases: resource allocation, assignment, and scheduling. Since the resource allocation is a search for a minimal cost multi-subset of processors, we adopted an A* search based technique for the first synthesis phase. A variation of the force-directed optimization technique is used to assign a task to an allocated processor. The final scheduling of a hard-real time task is done by the task level scheduler which is based on Earliest Deadline First (EDF) scheduling policy. Our task level scheduler incorporates force-directed scheduling methodology to address the situations where EDF is not optimal. The experimental results on a variety of examples show that the approach is highly effective and efficient.     

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.     

Guaranteed Dynamic Priority Assignment Schemes for Real‐Time Tasks with (m,k)‐Firm Deadlines

We present guaranteed dynamic priority assignment schemes for multiple real‐time tasks subject to (m, k)‐firm deadlines. The proposed schemes have two scheduling objectives: providing a bounded probability of missing (m, k)‐firm constraints and maximizing the probability of deadline satisfactions. The second scheduling objective is especially necessary in order to provide the best quality of service as well as to satisfy the minimum requirements expressed by (m, k)‐firm deadlines. We analytically establish that the proposed schemes provide a guarantee on the bounded probability of missing (m, k)‐firm constraints. Experimental studies validate our analytical results and confirm the effectiveness and superiority of the proposed schemes with regard to their scheduling objectives.     

Multi-objective Energy Aware Scheduling of Deadline Constrained Workflows in Clouds using Hybrid Approach

Cloud computing is undoubtedly one of the most significant advances in the domain of information technology. It facilitates elastic and on-demand provisioning of high performance computing capabilities employing pay-per-use model that has snowballed its adoption by scientists and engineers over the past few years. They often exploit workflows to represent their massive applications. Workflow scheduling in cloud has been devoted considerable investigation by researchers owing to its NP-complete nature of problem. Most of the previous studies targeted optimization of schedule length and execution cost within given deadlines/budget restrictions, or both. However, enormous energy consumption in the cloud data centers is not only negatively impacting the environment but also resulting in increased operational costs and thus cannot be ignored. Efficient scheduling strategies can significantly lessen the energy usage while complying with the user’s Quality of Service limitations. This research study proposes a Hybrid Approach for Energy aware scheduling of Deadline constrained workflows (HAED) using Intelligent Water Drops algorithm and Genetic Algorithm, which provides non-dominated solutions to the user. In particular, it focuses on multiple objectives i.e. reduction of schedule length, execution cost and energy usage within deadlines specified by the user. Its performance has been assessed on four scientific workflows from diverse domains using hypervolume and set coverage. The results achieved with the simulations demonstrate that the solutions produced by HAED are of better quality in terms of accuracy and diversity than non-dominated sorting genetic algorithm and hybrid particle swarm optimization.