基于多核处理器的K线程低能耗的任务调度优化算法

针对具有独立DVFS的多核处理器系统,提出了一种K线程低能耗模型的并行任务调度优化算法(Tasks Optimization based on Energy-Effectiveness Model,TO-EEM)。与传统的并行任务节能调度相比,该算法的主要目标是不仅通过降低处理器频率来减少处理器瞬时功耗,而且结合并行任务间的同步互斥所造成的线程阻塞情况,合理分配线程资源来减少线程同步时间,优化并行性能;保证任务在一定的并行加速比性能前提下,提高资源利用率,减少能耗,达到程序能耗和性能之间的折衷。文中进行了大量模拟实验,结果证明提出的任务优化模型算法节能效果明显,能有效降低处理器的功耗,并始终保持线性加速比。     

基于多核处理器的关联任务并行感知调度算法

关联任务在多核处理器上并行调度所产生的通信时延,会对任务调度长度和处理器利用率造成负面影响,为了改善多核系统对关联任务的处理性能,针对关联任务在多核处理器上的调度特点,提出一种并行感知调度算法。计算各任务与终点间的最长路径值,按照该值的降序来分配任务调度次序,在分配处理器内核时兼顾关联度和任务最早可执行时间,设置最佳匹配评价函数。实验结果表明,与busHEFT和DTSV算法相比,该算法具有更短的任务调度时延、更少的通信量以及更高的处理器利用率。     

Power efficient rate monotonic scheduling for multi-core systems

More computational power is offered by current real-time systems to cope with CPU intensive applications. However, this facility comes at the price of more energy consumption and eventually higher heat dissipation. As a remedy, these issues are being encountered by adjusting the system speed on the fly so that application deadlines are respected and also, the overall system energy consumption is reduced. In addition, the current state of the art of multi-core technology opens further research opportunities for energy reduction through power efficient scheduling. However, the multi-core front is relatively unexplored from the perspective of task scheduling. To the best of our knowledge, very little is known as of yet to integrate power efficiency component into real-time scheduling theory that is tailored for multi-core platforms. In this paper, we first propose a technique to find the lowest core speed to schedule individual tasks. The proposed technique is experimentally evaluated and the results show the supremacy of our test over the existing counterparts. Following that, the lightest task shifting policy is adapted for balancing core utilization, which is utilized to determine the uniform system speed for a given task set. The aforementioned guarantees that: (i) all the tasks fulfill their deadlines and (ii) the overall system energy consumption is reduced.     

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％.     

An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment

The growth of energy consumption has been explosive in current data centers, super computers, and public cloud systems. This explosion has led to greater advocacy of green computing, and many efforts and works focus on the task scheduling in order to reduce energy dissipation. In order to obtain more energy reduction as well as maintain the quality of service by meeting the deadlines, this paper proposes a DVFS-enabled Energy-efficient Workflow Task Scheduling algorithm: DEWTS. Through merging the relatively inefficient processors by reclaiming the slack time, DEWTS can leverage the useful slack time recurrently after severs are merged. DEWTS firstly calculates the initial scheduling order of all tasks, and obtains the whole makespan and deadline based on Heterogeneous-Earliest-Finish-Time (HEFT) algorithm. Through resorting the processors with their running task number and energy utilization, the underutilized processors can be merged by closing the last node and redistributing the assigned tasks on it. Finally, in the task slacking phase, the tasks can be distributed in the idle slots under a lower voltage and frequency using DVFS technique, without violating the dependency constraints and increasing the slacked makespan. Based on the amount of randomly generated DAGs workflows, the experimental results show that DEWTS can reduce the total power consumption by up to 46.5 % for various parallel applications as well as balance the scheduling performance.     

Fault-Tolerant Dynamic Rescheduling for Heterogeneous Computing Systems

As the scale and complexity of heterogeneous computing systems grow, failures occur frequently and have an adverse effect on solving large-scale applications. Hence, fault-tolerant scheduling is an imperative step for large-scale computing systems. The existing fault-tolerant scheduling algorithms belong to static scheduling, and they allocate multiple copies of each task to several processors no matter whether processor failures affect the execution of tasks. Such active replication strategies not only waste resource but also sacrifice the makespan. What is more, they cannot guarantee the successful execution of applications. In this paper, we propose a fault-tolerant dynamic rescheduling algorithm named FTDR, which can overcome above drawbacks. FTDR keeps listening to the processor failure, and reschedules the suspended tasks once failures occur. Because FTDR reschedules the tasks that are suspended because of failures, it can tolerate an arbitrary number of failures. Randomly generated DAGs are tested in our experiments. Experimental results show that the proposed algorithm achieves good performance in terms of makespan and resource consumption compared with its direct competitors.     

基于动态松弛时间回收的开销敏感节能实时调度算法

为适应实际系统中任务集的不断变化以及不可忽视状态切换开销的要求,针对多核多处理器系统中常见的周期任务模型,提出一种基于动态松弛时间回收的开销敏感节能实时调度算法DSROM,在每个TL面的初始时刻、任务提前完成时刻实现节能调度及动态松弛时间回收,在不违反周期任务集可调度性的基础上,达到实时约束与能耗节余之间的合理折衷。模拟实验结果表明,DSROM算法不仅保证了周期任务集的最优可调度性,而且当任务集总负载超过某一个值后,其节能效果整体优于现有方法,最多可节能近20%。     

Energy‐efficient scheduling algorithms for batch‐of‐tasks (BoT) applications on heterogeneous computing systems

One of the major design constraints of a heterogeneous computing system is optimal scheduling, that is, mapping of tasks on the processing nodes in order to optimize the QoS parameters. Because of the huge energy consumption by computing resources, negative environmental effects and reduced system reliability, energy has unavoidably been added as a new parameter to the list of QoS parameters. Energy optimization in scheduling strategies along with makespan makes it an even more challenging combinatorial optimization problem. This work proposes two energy‐aware scheduling algorithms G1 and G2 to schedule a batch‐of‐tasks, made of a collection of independent tasks, on heterogeneous processors in order to minimize the makespan and the energy consumption. The proposed algorithms schedule tasks based on weighted aggregation cost function to the appropriate processors followed by task migration phase designed to further minimize the makespan and the energy consumption. The study evaluates the performance of the proposed algorithms with some of the peers, that is, MinMin, MINSuff on account of makespan, energy consumption, flowtime, and utilization. An experimental study reveals that the proposed algorithm (G2) consistently performs better under various test conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

混合粒子群算法的异构多核处理器间任务调度

针对异构多核处理器间的任务调度问题,为了更好地发挥异构多核处理器间的平台优势,提出一种基于将有关联的且不在同一处理器上的任务进行复制的思想,从而使每个异构多核的处理器能独立执行任务,来减少不同处理器之间的通信开销,并且通过混合粒子群算法(HPSO)来调度异构多核处理器中的任务,避免由于当任意一个异构多核处理器由于任务分配过多而导致计算机不能及时且准确地得出结果.最后实验证明,对比传统的启发式分配方案和常见的遗传算法(GA),基于任务复制思想分配方案和混合粒子群算法(HPSO)具有更好的求解能力,并且可以提供执行时间更少的调度分配方案,具有较好的应用价值.     

Energy-Aware Scheduling for Tasks with Target-Time in Blockchain based Data Centres

Cloud computing infrastructures have intended to provide computing services to end-users through the internet in a pay-per-use model. The extensive deployment of the Cloud and continuous increment in the capacity and utilization of data centers (DC) leads to massive power consumption. This intensifying scale of DCs has made energy consumption a critical concern. This paper emphasizes the task scheduling algorithm by formulating the system model to minimize the makespan and energy consumption incurred in a data center. Also, an energy-aware task scheduling in the Blockchain-based data center was proposed to offer an optimal solution that minimizes makespan and energy consumption. The established model was analyzed with a target-time responsive precedence scheduling algorithm. The observations were analyzed and compared with the traditional scheduling algorithms. The outcomes exhibited that the developed solution incurs better performance with a response to resource utilization and decreasing energy consumption. The investigation revealed that the applied strategy considerably enhanced the effectiveness of the designed schedule.

     

Energy-Aware Scheduling on Multicore Heterogeneous Grid Computing Systems

We address a multicriteria non-preemptive energy-aware scheduling problem for computational Grid systems. This work introduces a new formulation of the scheduling problem for multicore heterogeneous computational Grid systems in which the minimization of the energy consumption, along with the makespan metric, is considered. We adopt a two-level model, in which a meta-broker agent (level 1) receives all user tasks and schedules them on the available resources, belonging to different local providers (level 2). The computing capacity and energy consumption of resources are taken from real multi-core processors from the main current vendors. Twenty novel list scheduling methods for the problem are proposed, and a comparative analysis of all of them over a large set of problem instances is presented. Additionally, a scalability study is performed in order to analyze the contribution of the best new bi-objective list scheduling heuristics when the problem dimension grows. We conclude after the experimental analysis that accurate trade-off schedules are computed by using the new proposed methods.     

An infrastructure for embedded systems using task scheduling

Task scheduling in heterogeneous environments such as cloud data centers is considered to be an NP-complete problem. Efficient task scheduling will lead to balance the load on the virtual machines (VMs) thereby achieving effective resource utilization. Hence there is a need for a new scheduling framework to perform load balancing amid considering multiple quality of service (QoS) metrics such as makespan, response time, execution time, and task priority. Multi-core Web server is difficult to achieve dynamic balance in the process of remote dynamic request scheduling, so it is necessary to improve it based on the traditional scheduling algorithm to enhance the actual effect of the algorithm. This article do research on the multi-core Web server, Focusing on multi-core Web server queuing model. On this basis, the author draws the drawbacks of the multi-core Web server in the remote dynamic request scheduling algorithm, and improves the traditional algorithm with the demand analysis. Not only it overcomes the drawbacks of traditional algorithms, but also promotes the system threads carrying the same amount of tasks, and promotes the server being always in a dynamic balance. On the basis of this, it achieves an effective solution to customer requests.     

基于机器学习的异构感知多核调度方法

异构多核处理器已成为现代嵌入式系统的主流解决方案，而好的在线映射或调度方法对其充分发挥高性能和低功耗的优势起着至关重要的作用。针对异构多核处理系统上的应用程序动态映射和调度问题，提出一种基于机器学习、能快速准确评估程序性能和程序行为阶段变化的检测技术来有效确定重映射时机从而最大化系统性能的映射和调度解决方案。该方案一方面通过合理选择处理核和程序运行时的静态和动态特征来有效感知异构处理所带来的计算能力和工作负载运行行为的差异，从而能够构建更加准确的预测模型；另一方面通过引入阶段检测来尽可能减少在线映射计算的次数，从而能够提供更加高效的调度方案。最后，在SPLASH-2数据集上验证了所提出调度方案的有效性。实验结果表明，与Linux默认的完全公平调度（CFS）方法相比，所提出的方法在系统计算性能方面提高了52%，在CPU资源利用率上提高了9.4%。这表明所提方法在系统计算性能和CPU资源利用率方面具备优良的性能，可以有效提升异构多核系统的应用动态映射和调度效果。     

GreenSched: An intelligent energy aware scheduling for deadline-and-budget constrained cloud tasks

The constant growth of the energy crisis within the ICT Sector has persistently gained importance thereby prompting endeavors to curb growing energy demands and associated expenditures. This paper attempts to propose an intelligent energy aware task allocation and resource provisioning technique running in GreenSched model. The GreenSched model tends to exploit the heterogeneity of tasks and multi-core capacity of the varied nodes in the cloud environment and attempts to proactively schedule the deadline-and budget- constrained tasks on identified less energy consuming or energy aware nodes. It implements a Forward-only Counter Propagation Network (CPN) based intelligent scheduler unit that runs a scheduling technique to identify the best nodes for the task allocation process, one with least energy consumption and deadline- and budget -fulfilling capability. The nodes are clustered and classified by comparing their energy consumption values. The proposed algorithm has been implemented using the CloudSim toolkit and Kohonen and CP-ANN Toolbox with the help of Matlab^TM platform. The experimental results exhibit that the proposed technique offers reduced energy consumption along with an overall improvement in the performance by meeting the deadline-and-budget constraints imposed by the users.     

基于机器学习的异构感知多核调度方法

异构多核处理器已成为现代嵌入式系统的主流解决方案,而好的在线映射或调度方法对其充分发挥高性能和低功耗的优势起着至关重要的作用。针对异构多核处理系统上的应用程序动态映射和调度问题,提出一种基于机器学习、能快速准确评估程序性能和程序行为阶段变化的检测技术来有效确定重映射时机从而最大化系统性能的映射和调度解决方案。该方案一方面通过合理选择处理核和程序运行时的静态和动态特征来有效感知异构处理所带来的计算能力和工作负载运行行为的差异,从而能够构建更加准确的预测模型;另一方面通过引入阶段检测来尽可能减少在线映射计算的次数,从而能够提供更加高效的调度方案。最后,在SPLASH-2数据集上验证了所提出调度方案的有效性。实验结果表明,与Linux默认的完全公平调度（CFS）方法相比,所提出的方法在系统计算性能方面提高了52%,在CPU资源利用率上提高了9.4%。这表明所提方法在系统计算性能和CPU资源利用率方面具备优良的性能,可以有效提升异构多核系统的应用动态映射和调度效果。     

基于多核处理器系统多维限制的节能实时调度

介绍了多核处理器系统所面对的处理器实际限制、抢占调度实际限制和并行任务模型实际限制等多维限制挑战, 主要针对处理器开销模型、有限抢占模型和复杂并行任务模型等方面, 深入探讨了基于系统实际多维模型的多核节能实时调度研究, 为促进多核处理器系统在实时嵌入式领域的应用提供理论和技术参考.     

Energy-aware task migration for multiprocessor real-time systems

A task migration method is proposed for energy savings in multiprocessor real-time systems. The method is based on the portioned scheduling technique which classifies each task as a fixed task or a migratable task. The basic task migration problem with specific parameters is formulated as a linear programming problem to minimize average power. Then, the method is extended to more general case with a complete migration algorithm. Moreover, a scheduling algorithm is proposed for migratable tasks. Simulation results on two processor models demonstrated significant energy savings over existing methods.     

An efficient list-based task scheduling algorithm for heterogeneous distributed computing environment

The task scheduling in heterogeneous distributed computing systems plays a crucial role in reducing the makespan and maximizing resource utilization. The diverse nature of the devices in heterogeneous distributed computing systems intensifies the complexity of scheduling the tasks. To overcome this problem, a new list-based static task scheduling algorithm namely Deadline-Aware-Longest-Path-of-all-Predecessors (DA-LPP) is being proposed in this article. In the prioritization phase of the DA-LPP algorithm, the path length of the current task from all its predecessors at each level is computed and among them, the longest path length value is assigned as the rank of the task. This strategy emphasizes the tasks in the critical path. This well-optimized prioritization phase leads to an observable minimization in the makespan of the applications. In the processor selection phase, the DA-LPP algorithm implements the improved insertion-based policy which effectively utilizes the unoccupied leftover free time slots of the processors which improve resource utilization, further least computation cost allocation approach is followed to minimize the overall computation cost of the processors and parental prioritization policy is incorporated to further reduce the scheduling length. To demonstrate the robustness of the proposed algorithm, a synthetic graph generator is used in this experiment to generate a huge variety of graphs. Apart from the synthetic graphs, real-world application graphs like Montage, LIGO, Cybershake, and Epigenomic are also considered to grade the performance of the DA-LPP algorithm. Experimental results of the DA-LPP algorithm show improvement in performance in terms of scheduling length ratio, makespan reduction rate , and resource reduction rate when compared with other algorithms like DQWS, DUCO, DCO and EPRD. The results reveal that for 1000 task set with deadline equals to two times of the critical path, the scheduling length ratio of the DA-LPP algorithm is better than DQWS by 35%, DUCO by 23%, DCO by 26 %, and EPRD by 17%.     

一种非对称多核SDR的任务调度和分配算法

针对软件无线电(SDR)应用同步数据流的特点,提出一种非对称多核SDR的任务调度和分配算法。该算法综合考虑任务之间的通信时间和任务固定流水,保证任务调度和分配的通用性和并行性。利用整数线性规划(ILP)方法对任务调度和分配进行建模,采用任务拆分方法优化调度和分配的结果,进一步提高任务调度和分配的执行效率。在目标SDR平台上实现IEEE 802.11a频偏估计处理的任务调度和分配,实验结果表明,该算法能提高5.97%的软件无线电平台吞吐量和3.03%的处理器核平均利用率,并减少34.31%的处理器核最长空闲等待时间。     

Power-aware fixed priority scheduling for sporadic tasks in hard real-time systems

In this paper, we consider the generalized power model in which the focus is the dynamic power and the static power, and we study the problem of the canonical sporadic task scheduling based on the rate-monotonic (RM) scheme. Moreover, we combine with the dynamic voltage scaling (DVS) and dynamic power management (DPM). We present a static low power sporadic tasks scheduling algorithm (SSTLPSA), assuming that each task presents its worst-case work-load to the processor at every instance. In addition, a more energy efficient approach called a dynamic low power sporadic tasks scheduling algorithm (DSTLPSA) is proposed, based on reclaiming the dynamic slack and adjusting the speed of other tasks on-the-fly in order to reduce energy consumption while still meeting the deadlines. The experimental results show that the SSTLPSA algorithm consumes 26.55–38.67% less energy than that of the RM algorithm and the DSTLPSA algorithm reduces the energy consumption up to 18.38–30.51% over the existing DVS algorithm.