Energy Efficient Scheduling of Real-Time Tasks on Multicore Processors

Multicore processors deliver a higher throughput at lower power consumption than unicore pro- cessors. In the near future, they will thus be widely used in mobile real-time systems. There have been many research on energy-efficient scheduling of real-time tasks using DVS. These approaches must be modified for multicore processors, however, since normally all the cores in a chip must run at the same performance level. Thus blindly adopting existing DVS algorithms which do not consider the restriction will result in a waste of energy. This article suggests Dynamic Repartitioning algorithm based on existing partitioning approaches of multiprocessor systems. The algorithm dynamically balances the task loads of multiple cores to optimize power consumption during execution. We also suggest Dynamic Core Scaling algorithm which adjusts the number of active cores to reduce leakage power consumption under low load conditions. Simulation results show that Dynamic Repartitioning can produce energy savings of about 8% even with the best energy-efficient partitioning algorithm. The results also show that Dynamic Core Scaling can reduce energy consumption by about 26% under low load conditions.     

抢占阈值调度的功耗优化

DVS(Dynamic Voltage Scaling)技术的应用使得任务执行时间延长进而使得处理器的静态功耗(由CMOS电路的泄露电流引起)迅速增加.延迟调度(Procrastination Scheduling)算法是近年提出用于减少静态功耗的有效方法,它通过推迟任务的正常执行来尽可能长时间地让处理器处于睡眠或关闭状态,从而避免过多的静态功耗泄露.文中针对可变电压处理器上运用抢占阈值调度策略的周期性任务集合,将节能调度和延迟调度结合起来,提出一种两阶段节能调度算法,先使用离线算法来计算每个任务的最优处理器执行速度,而后使用在线模拟调度算法来计算每个任务的延迟时间,从而动态判定处理器开启/关闭时刻.实例研究和仿真实验表明,作者的方法能够进一步降低抢占阈值任务调度算法的功耗.     

基于多频段传感器辅助认知无线电网络的高能效传感器调度算法

在传感器协助认知无线电网络中,传统的高能效传感器调度问题只考虑了一个频段。多频段的传感器调度问题有许多新的研究领域。建立了一种多频段传感器调度问题的模型,提出了一种用于提高认知网络通信容量的基于遗传算法的高能效调度算法。模型考虑了传感器切换频段的能量消耗。在问题模型中,认知基站基于提高能效的目标为每个频段分配一组传感器进行协作感知。基于遗传算法的高能效调度算法通过优化传感器的调度使认知网络达到最大的通信容量,从而达到高能效的目标。仿真结果表明,本文的算法可以比贪心算法以及其他算法取得更高的网络通信容量。     

Energy efficient scheduling of parallel tasks on multiprocessor computers

In this paper, scheduling parallel tasks on multiprocessor computers with dynamically variable voltage and speed are addressed as combinatorial optimization problems. Two problems are defined, namely, minimizing schedule length with energy consumption constraint and minimizing energy consumption with schedule length constraint. The first problem has applications in general multiprocessor and multicore processor computing systems where energy consumption is an important concern and in mobile computers where energy conservation is a main concern. The second problem has applications in real-time multiprocessing systems and environments where timing constraint is a major requirement. Our scheduling problems are defined such that the energy-delay product is optimized by fixing one factor and minimizing the other. It is noticed that power-aware scheduling of parallel tasks has rarely been discussed before. Our investigation in this paper makes some initial attempt to energy-efficient scheduling of parallel tasks on multiprocessor computers with dynamic voltage and speed. Our scheduling problems contain three nontrivial subproblems, namely, system partitioning, task scheduling, and power supplying. Each subproblem should be solved efficiently, so that heuristic algorithms with overall good performance can be developed. The above decomposition of our optimization problems into three subproblems makes design and analysis of heuristic algorithms tractable. A unique feature of our work is to compare the performance of our algorithms with optimal solutions analytically and validate our results experimentally, not to compare the performance of heuristic algorithms among themselves only experimentally. The harmonic system partitioning and processor allocation scheme is used, which divides a multiprocessor computer into clusters of equal sizes and schedules tasks of similar sizes together to increase processor utilization. A three-level energy/time/power allocation scheme is adopted for a given schedule, such that the schedule length is minimized by consuming given amount of energy or the energy consumed is minimized without missing a given deadline. The performance of our heuristic algorithms is analyzed, and accurate performance bounds are derived. Simulation data which validate our analytical results are also presented. It is found that our analytical results provide very accurate estimation of the expected normalized schedule length and the expected normalized energy consumption and that our heuristic algorithms are able to produce solutions very close to optimum.     

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.     

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％.     

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。     

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。     

Scheduling precedence constrained task graphs with non-negligibleintertask communication onto multiprocessors

The multiprocessor scheduling problem is the problem of scheduling the tasks of a precedence constrained task graph (representing a parallel program) onto the processors of a multiprocessor in a way that minimizes the completion time. Since this problem is known to be NP-hard in the strong sense in all but a few very restricted eases, heuristic algorithms are being developed which obtain near optimal schedules in a reasonable amount of computation time. We present an efficient heuristic algorithm for scheduling precedence constrained task graphs with nonnegligible intertask communication onto multiprocessors taking contention in the communication channels into consideration. Our algorithm for obtaining satisfactory suboptimal schedules is based on the classical list scheduling strategy. It simultaneously exploits the schedule-holes generated in the processors and in the communication channels during the scheduling process in order to produce better schedules. We demonstrate the effectiveness of our algorithm by comparing with two competing heuristic algorithms available in the literature     

能量敏感实时系统能量管理分析

便携系统越来越广泛的应用使得电池使用问题日益突出。对能量敏感实时系统的能量管理进行了分析和探讨,通过对任务执行过程中的电压进行调整以减少实时任务的能量消耗,给出了能量敏感实时系统的静态能量管理和动态能量管理的分析方法,并提出了具有截止时间限制的实时任务减少能量消耗的调度机制。     

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.     

Feedback fuzzy-DVS scheduling of control tasks

To consider the energy-aware scheduling problem in computer-controlled systems is necessary to improve the control performance, to use the limited computing resource sufficiently, and to reduce the energy consumption to extend the lifetime of the whole system. In this paper, the scheduling problem of multiple control tasks is discussed based on an adjustable voltage processor. A feedback fuzzy-DVS (dynamic voltage scaling) scheduling architecture is presented by applying technologies of the feedback control and the fuzzy DVS. The simulation results show that, by using the actual utilization as the feedback information to adjust the supply voltage of processor dynamically, the high CPU utilization can be implemented under the precondition of guaranteeing the control performance, whilst the low energy consumption can be achieved as well. The proposed method can be applied to the design in computer-controlled systems based on an adjustable voltage processor.     

面向能耗优化的容错实时系统任务调度模型研究

为了支持面向能耗优化的容错实时任务调度算法研究,提出一种频率相关的时间Petri网—FRTPN.FRTPN引入用于动态电压调整的变迁频率设置空间以及和频率相关的静态引发时域,以支持调度算法的能耗评估及优化;同时它增加一类抑制弧刻画容错故障恢复过程.通过对基于检查点的容错实时能耗优化任务调度进行建模证明了FRTPN的有效性.     

A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems

In this paper, we investigate the problem of scheduling precedence-constrained parallel applications on heterogeneous computing systems (HCSs) like cloud computing infrastructures. This kind of application was studied and used in many research works. Most of these works propose algorithms to minimize the completion time (makespan) without paying much attention to energy consumption.We propose a new parallel bi-objective hybrid genetic algorithm that takes into account, not only makespan, but also energy consumption. We particularly focus on the island parallel model and the multi-start parallel model. Our new method is based on dynamic voltage scaling (DVS) to minimize energy consumption.In terms of energy consumption, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of completion time, the obtained schedules are also shorter than those of other algorithms. Furthermore, our study demonstrates the potential of DVS.     

An Integrated Approach to Locality-Conscious Processor Allocation and Scheduling of Mixed-Parallel Applications

Complex parallel applications can often be modeled as directed acyclic graphs of coarse-grained application tasks with dependences. These applications exhibit both task and data parallelism, and combining these two (also called mixed parallelism) has been shown to be an effective model for their execution. In this paper, we present an algorithm to compute the appropriate mix of task and data parallelism required to minimize the parallel completion time (makespan) of these applications. In other words, our algorithm determines the set of tasks that should be run concurrently and the number of processors to be allocated to each task. The processor allocation and scheduling decisions are made in an integrated manner and are based on several factors such as the structure of the task graph, the runtime estimates and scalability characteristics of the tasks, and the intertask data communication volumes. A locality-conscious scheduling strategy is used to improve intertask data reuse. Evaluation through simulations and actual executions of task graphs derived from real applications and synthetic graphs shows that our algorithm consistently generates schedules with a lower makespan as compared to Critical Path Reduction (CPR) and Critical Path and Allocation (CPA), two previously proposed scheduling algorithms. Our algorithm also produces schedules that have a lower makespan than pure task- and data-parallel schedules. For task graphs with known optimal schedules or lower bounds on the makespan, our algorithm generates schedules that are closer to the optima than other scheduling approaches.     

节能及信任驱动的虚拟机资源调度

针对节能机制和信任驱动的资源调度机制相分离的特点,提出了一种应用到云计算数据中心中的节能及信任驱动的虚拟机资源调度TD energy-aware-Opt算法。该算法利用任务和虚拟机资源之间的信任机制进行任务和虚拟机资源之间的匹配,并通过最小化迁移算法对虚拟机进行实时迁移,以达到保证用户任务性能和数据中心节能的目的。对该算法进行大规模和多角度的仿真实验,结果表明:该算法与传统的基于信任驱动的最小完成时间TD min-min算法、基于信任驱动的最大完成时间TD max-min算法相比,能节省大量电能并且具有较优的平均信任效益、总信任效益和较低的服务等级协议违反率。     

Power and performance management for parallel computations in clouds and data centers

We address scheduling independent and precedence constrained parallel tasks on multiple homogeneous processors in a data center with dynamically variable voltage and speed as combinatorial optimization problems. We consider the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint on multiple processors. Our approach is to use level-by-level scheduling algorithms to deal with precedence constraints. We use a simple system partitioning and processor allocation scheme, which always schedules as many parallel tasks as possible for simultaneous execution. We use two heuristic algorithms for scheduling independent parallel tasks in the same level, i.e., SIMPLE and GREEDY. We adopt a two-level energy/time/power allocation scheme, namely, optimal energy/time allocation among levels of tasks and equal power supply to tasks in the same level. Our approach results in significant performance improvement compared with previous algorithms in scheduling independent and precedence constrained parallel tasks.     

Some observations on optimal frequency selection in DVFS-based energy consumption minimization

In recent years, the issue of energy consumption in parallel and distributed computing systems has attracted a great deal of attention. In response to this, many energy-aware scheduling algorithms have been developed primarily using the dynamic voltage-frequency scaling (DVFS) capability which has been incorporated into recent commodity processors. Majority of these algorithms involve two passes: schedule generation and slack reclamation. The former pass involves the redistribution of tasks among DVFS-enabled processors based on a given cost function that includes makespan and energy consumption, while the latter pass is typically achieved by executing individual tasks with slacks at a lower processor frequency. In this paper, a new slack reclamation algorithm is proposed by approaching the energy reduction problem from a different angle. Firstly, the problem of task slack reclamation by using combinations of processors’ frequencies is formulated. Secondly, several proofs are provided to show that (1) if the working frequency set of processor is assumed to be continues, the optimal energy will be always achieved by using only one frequency, (2) for real processors with a discrete set of working frequencies, the optimal energy is always achieved by using at most two frequencies, and (3) these two frequencies are adjacent/neighbouring when processor energy consumption is a convex function of frequency. Thirdly, a novel algorithm to find the best combination of frequencies to result the optimal energy is presented. The presented algorithm has been evaluated based on results obtained from experiments with three different sets of task graphs: 3000 randomly generated task graphs, and 600 task graphs for two popular applications (Gauss-Jordan and LU decomposition). The results show the superiority of the proposed algorithm in comparison with other techniques.     

Compositional power-aware real-time scheduling with discrete frequency levels

Power consumption remains a hot issue in all areas of computing ranging from embedded systems that rely on batteries to large scale data centers where reducing the power consumption of computing devices directly affects not only the management cost, but also contributes to a greener computing environment. The power-aware real-time scheduling problem has recently been addressed for a compositional framework with periodic task model under the assumption that a processor can continuously vary its operating frequency and voltage. However, in practice, this technique is only suboptimal and still produce the waste of computational resources. This paper introduces new frequency scaling schemes that statically determine optimal processor speeds at system, component, and task levels with the objective of minimizing the total energy consumption of the entire framework. Since real-world processors support only a finite set of operating frequencies, our algorithms also consider only discrete speed levels and guarantee still that each task meets its deadline. We implemented and evaluated the performance of a prototype framework that incorporates our algorithms on top of the RT-Xen hypervisor in order to provide power-aware compositional real-time scheduling framework to virtual machines.     

EPTS:一种实时动态电压调整的抢占阈值调度器

低功耗目前已成为嵌入式实时系统设计中非常重要的性能需求。动态电压调度DVS机制通过动态调整处理器电压进而有效降低系统功耗,正在逐渐得到广泛应用。抢占阈值调度策略实现双优先级系统,每个任务具有两个优先级,任务优先级被用于任务之间竞争处理器,而抢占阈值作为任务开始运行后实际使用的优先级,从而减少现场切换次数,降低系统功耗,同时也提高整个任务集合的可调度性。本文提出一种在线节能调度算法EPTS,拓展抢占阈值调度模型,在任务执行过程中动态调节处理器电压,力求在保证任务集合可调度性的前提下尽可能减少系统功耗,提高系统性能。而后在AMDAthlon4处理器和RT-Linux平台上实现了EPTS调度器,实验证明对于实际任务集合能够有效节能,提高了处理器的利用率,改善了RT-Linux的实时性能。