基于cron的计划任务时间管理的设计与实现

为了实现通信系统中大量的计划任务的时间管理的目的,在分析了Linux8乙Unix系统下的crontab的基础上,通过对crontab文件的优化,将计划任务以时间分类的方法。将执行时间相同的计划任务做成schedule形式,实现了对系统中大量同时刻或时段的计划任务进行统一的管理,改善了crontab文件的冗余问题,减少了cron服务重启次数;减轻了系统的开销。     

基于价值优化的相控阵雷达任务调度算法

针对相控阵雷达时间资源分配问题,该文提出一种基于价值优化的任务调度算法。首先建立任务调度属性参数,对跟踪任务队列进行可行性分析和筛选操作,确定跟踪任务调度属性。其次,根据任务最大价值及其变化斜率,建立关于实际执行时刻的动态任务价值函数,并基于此构建任务调度的价值优化模型,对跟踪任务执行时刻进行分配,以更好满足及时性原则。最后,利用执行跟踪任务间的空闲时间片对搜索任务进行调度。仿真结果表明,该文算法有效减小了时间偏移量,提升了实现价值率。     

Task Scheduling Algorithm for the Communication,Ocean, and Meteorological Satellite

In this paper, we propose an efficient single‐resource task scheduling algorithm for the Communication, Ocean, and Meteorological Satellite. Among general satellite planning functions such as constraint check, priority check, and task scheduling, this paper focuses on the task scheduling algorithm, which resolves conflict among tasks which have an exclusion relation and the same priority. The goal of the proposed task scheduling algorithm is to maximize the number of tasks that can be scheduled. The rationale of the algorithm is that a discarded task can be scheduled instead of a previously selected one depending on the expected benefit acquired by doing so. The evaluation results show that the proposed algorithm enhances the number of tasks that can be scheduled considerably.     

Scheduling algorithm for real-time imprecise computations tominimise maximum weighted errors using the linear programmingmethod

An optimal scheduling algorithm for imprecise systems is presented. The proposed algorithm aims at minimising the maximum weighted errors. A novel property of the algorithm is that the errors are evenly distributed among scheduled tasks. The complexity of the proposed algorithm is O(N³) in the worst case, where N is the number of tasks     

多功能组网雷达可变参数任务规划方法

针对任意周期雷达任务(侦查、干扰、探测等)在多功能组网雷达中的规划问题,提出了一种可变参数的任务规划方法。首先,分析雷达任务特性,建立可变参数雷达任务模型。然后,设定组网雷达中各雷达任务规划时间窗宽度和开槽宽度,使组网雷达可执行任务的周期与现有任务周期最接近。接着,针对雷达任务任意周期的特点,以任务周期变化量最小为原则对任务周期进行调整,保证多个任务在同一部雷达中不重叠,并且具有周期性。最后,利用启发式算法,以任务驻留时间的平均隶属度最大为目标完成任务规划。仿真结果与实例证明了该方法的有效性。     

A Modelling of Context-Aware Elderly Healthcare Eco-System-(CA-EHS) Using Signal Analysis and Machine Learning Approach

In cloud computing, more often times cloud assets are underutilized because of poor allocation of task in virtual machine (VM). There exist inconsistent factors affecting the scheduling tasks to VMs. In this paper, an effective scheduling with multi-objective VM selection in cloud data centers is proposed. The proposed multi-objective VM selection and optimized scheduling is described as follows. Initially the input tasks are gathered in a task queue and tasks computational time and trust parameters are measured in the task manager. Then the tasks are prioritized based on the computed measures. Finally, the tasks are scheduled to the VMs in host manager. Here, multi-objectives are considered for VM selection. The objectives such as power usage, load volume, and resource wastage are evaluated for the VMs and the entropy is calculated for the measured objectives and based on the entropy value krill herd optimization algorithm prioritized tasks are scheduled to the VMs. The experimental results prove that the proposed entropy based krill herd optimization scheduling outperforms the existing general krill herd optimization, cuckoo search optimization, cloud list scheduling, minimum completion cloud, cloud task partitioning scheduling and round robin techniques.

     

The influence of the number of users on the reliability of a branched computer network

A hierarchical computer network with branched structure containing three data processing levels and realizing, in fixed time intervals, a determined set of tasks is considered as a class of such networks. The influence of the number of users on the number of realized tasks and on the assumed reliability measure is discussed.     

Energy efficiency analysis of IEEE 802.15.6 based wireless body area networks in scheduled access mode

This paper investigates the energy efficiency of IEEE 802.15.6 based wireless body area networks in the scheduled access mode. We assume that the hub operates in beacon mode with superframes and the nodes obtain scheduled allocation intervals consisting of finite number of allocation slots from the hub. In this paper, first of all, we present analytical models to compute the energy efficiency of the network for various scheduled allocation and acknowledgement policies assuming ideal channel conditions. The numerical and simulation results show that energy efficiency can be improved by (1) increasing the number of uploads in an active superframe, (2) increasing the payload size, (3) adopting block acknowledgement policy instead of immediate acknowledgement policy or (4) by decreasing the periodicity of allocations. We then present an analytical model to evaluate the energy efficiency in the presence of channel error. An approximate analytical solution for optimal frame size that maximize the energy efficiency of the network in error prone channel is obtained. For each node, we also provide analytical expression for the optimal allocation interval per superframe that maximize the energy efficiency of the network. Through extensive simulation studies, we establish that, in an error prone channel, the energy efficiency can be improved if the nodes make use of computed optimal frame size and optimal allocation interval for the uplink data transfer.     

RM算法的运行时开销研究与算法改进

RM算法是经典的固定优先级实时调度算法.而在嵌入式实时系统中,系统的工作负荷往往是由很多频率快、执行时间较短的任务组成.因此,直接使用RM算法进行任务调度会由于实时操作系统中任务的上下文切换开销而导致嵌入式系统资源利用率的降低.分析了基于RM算法调度的任务之间的抢占关系,并建立了以任务属性为参数的上下文切换开销模型.在该模型的基础上,通过优化任务的释放时间来降低RM算法导致的系统运行时任务切换开销.最后的实验结果验证了该策略的有效性.     

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.     

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.     

孔径分割多功能雷达的任务调度研究

针对孔径分割多功能雷达实时任务调度中资源利用不充分的问题,提出了一种基于扩展域的任务自适应调度方法。方法以时间指针为对象,对该时刻的任务分析范围从当前调度间隔扩展到整个调度时间轴,从整个调度时间轴上所有满足时间指针处执行条件的任务中,选择一个优先级最高的任务,可实现任务间的紧密安排,有效减少空闲资源的浪费,使得孔径分割雷达能够在有限的资源内调度执行更多的任务。最后,将该方法与传统多任务并行调度方法进行对比仿真,仿真结果证明了该方法的有效性和优越性。     

Distributed Active Sensor Scheduling for Target Tracking in Ultrasonic Sensor Networks

Active ultrasonic sensors for target tracking application may suffer from inter-sensor-interference if these highly dense deployed sensors are not scheduled, which can degrade the tracking performance. In this paper, we propose a dynamic distributed sensor scheduling (DSS) scheme, where the tasking sensor is elected spontaneously from the sensors with pending sensing tasks via random competition based on Carrier Sense Multiple Access (CSMA). The channel will be released immediately when sensing task is completed. Both simulation results and testbed experiment demonstrate the effectiveness of DSS scheme for large scale sensor networks in terms of system scalability and high tracking performance.     

Run-time Task Overlapping on Multiprocessor Platforms

Today’s embedded applications often consist of multiple concurrent tasks. These tasks are decomposed into sub-tasks which are in turn assigned and scheduled on multiple different processors to achieve the Pareto-optimal performance/energy combinations. Previous work introduced systematical approaches to make performance-energy trade-offs explorations for each individual task and used the exploration results at run-time to fulfill system-level constraints. However, they did not exploit the fact that the concurrent tasks can be executed in an overlapped fashion. In this paper, we propose a simple yet powerful on-line technique that performs task overlapping by run-time subtask re-scheduling. By doing so, a multiprocessor system with concurrent tasks can achieve better performance without extra energy consumption. We have applied our algorithm to a set of randomly-generated task graphs, obtaining encouraging improvements over non-overlapped task, and also having less overall energy consumption than a previous DVS method for real-time tasks. Then, we have demonstrated the algorithm on real-life video- and image-processing applications implemented on a dual-processor TI TMS320C6202 board: We have achieved a reduction of 22–29% in the application execution time, while the impact of run-time scheduling overhead proved to be negligible (1.55%).     

Lower bounds on memory requirements for statically scheduled DSP programs

This paper presents novel techniques for computing the minimum number of memory locations in statically scheduled digital signal processing (DSP) programs. Two related problems are considered. In the first problem, we compute the minimum number of memory locations required for a scheduled program assuming that no circuit transformations (such as pipelining and retiming) are to be performed after scheduling. For this problem, we consider memory minimization for theoperation-constrained, processor-constrained andunconstrained memory models which represent various restrictions on how data can be allocated to memory. Then we consider the second problem, where memory minimization for a scheduled program is considered simultaneously with retiming using a variation of the retiming problem referred to as theminimum physical storage location (MPSL) retiming. While both problems consider memory minimization for scheduled programs, the second problem minimizes memory using retiming whereas the first problem performs no retiming. The scheduling results obtained from the MARS design system are used to compare memory requirements in the context of both of these problems. Our experiments show that MARS performs an optimal retiming for the schedule it generates. These memory requirements are then compared with an integer linear programming solution to the scheduling problem which is optimal under the unconstrained memory model. It is concluded that the schedule obtained by the MARS system achieves optimality or near-optimality with respect to register minimization.     

μCos-II在嵌入式通信信号源中的应用

嵌入式通信信号源采用MCU控制DDS芯片的结构,产生FSK、LFM(线性调频)和MSK等多种信号模式。运行实时操作系统μCos-II,将应用程序按功能划分为几个核心任务,由实时内核进行调度,实现了多任务的并行执行,提高了嵌入式系统开发的效率、实时性和可靠性。通过以实时嵌入式操作系统μCos-II为软件平台的嵌入式通信信号源的系统设计,介绍了μCos-II的系统开销、移植和配置、多任务编程以及任务间同步、通信的实现方法。     

Die neue Zentrale Leitstelle in Innsbruck

The new central network control center of Innsbruck went into operation on November 1997 as scheduled. The control center co-ordinates the application of the power plants, the operative and dispositive functions of the control system and the monitoring of the 16 2/3-Hz-energy supply. The fundamental considerations which led to new tasks and technical solutions regarding the system which had to be taken into account when renewing the network control center are discussed. Described are realization, project management and new functional structure of operation.     

On the hard-real-time scheduling of embedded streaming applications

In this paper, we consider the problem of hard-real-time (HRT) multiprocessor scheduling of embedded streaming applications modeled as acyclic dataflow graphs. Most of the hard-real-time scheduling theory for multiprocessor systems assumes independent periodic or sporadic tasks. Such a simple task model is not directly applicable to dataflow graphs, where nodes represent actors (i.e., tasks) and edges represent data-dependencies. The actors in such graphs have data-dependency constraints and do not necessarily conform to the periodic or sporadic task models. In this work, we prove that the actors in acyclic Cyclo-Static Dataflow (CSDF) graphs can be scheduled as periodic tasks. Moreover, we provide a framework for computing the periodic task parameters (i.e., period and start time) of each actor, and handling sporadic input streams. Furthermore, we define formally a class of CSDF graphs called matched input/output (I/O) rates graphs which represents more than 80 % of streaming applications. We prove that strictly periodic scheduling is capable of achieving the maximum achievable throughput of an application for matched I/O rates graphs. Therefore, hard-real-time schedulability analysis can be used to determine the minimum number of processors needed to schedule matched I/O rates applications while delivering the maximum achievable throughput. This can be of great use for system designers during the Design Space Exploration (DSE) phase.     

Resource provisioning for survivable WDM networks under a sliding scheduled traffic model

Resource allocation in WDM networks, under both the static and dynamic traffic models have been widely investigated. However, in recent years there has been a growing number of applications with periodic bandwidth demands. Resources for such applications can be scheduled in advance, leading to a more efficient utilization of available network capacity. The setup and teardown times of the scheduled demands may be fixed, or may be allowed to slide within a larger window. A number of optimal integer linear program (ILP) solutions for the first problem (fixed setup/teardown times) have been presented in the literature. In this paper we present two new ILP formulations for the more general sliding scheduled traffic model, where the setup and teardown times may vary within a specified range. We first consider wavelength convertible networks and then extend our model to networks without wavelength conversion. Our ILP formulations jointly optimize the problem of scheduling the demands (in time) and allocating resources for the scheduled lightpaths. The fixed window model can be treated as a special case of our formulations. Our formulations are able to generate optimal solutions for practical sized networks. For larger networks, we have proposed a fast two-step optimization process. The first step schedules the demands optimally in time, so that the amount of overlap is minimized. The second step uses a connection holding time aware heuristic to perform routing and wavelength assignment for the scheduled demands.     

基于高效负载均衡的网络任务分配技术

针对传统的网格在分配任务的过程中,当多任务发生竞争的时候,由于多条路径的任务负载不均衡,造成传输速度慢的问题,提出一种基于遗传算法负载均衡的网格工作流算法.通过模拟自然界的生物进化过程对任务空间进行随机化搜索,根据预定任务的适应度函数,并用全局并行搜索方式找到最优节点,避免了传统方法的负载不均衡问题.实验证明,该算法实现了负载的快速均衡,提高了网格工作流系统的工作效率.