实时多处理器系统中基于能量节约的动态调度算法

当前处理器由于较高的能量消耗。导致处理器热量散发的提高及系统可靠性的降低，已经成为目前计算机领域较为关心的问题．然而目前一些有效降低能量消耗的技术大多针对单处理器系统，较少考虑多处理器系统．本文提出的调度算法针对多处理器系统，以最短任务优先调度为基础，结合其它有效技术，如共享空闲时间回收等，使得实时任务在其截止期内完成的同时能够有效地减低整个系统的能量消耗．针对独立任务集及具有依赖关系的任务集，本文提出两种算法：STFBA1及STFBA2（Shortest Task First—Based Algorithm）．与目前所知的有效算法相比，我们的算法具有更好的性能（调度长度及能量消耗）．     

多处理器环境中基于节能及容错的实时动态调度算法

当前处理器由于较高的能量消耗,导致处理器热量散发的提高及系统可靠性的降低,同时任务实际运行中的错误也降低了系统的可靠性.因此同时满足节能性及容错性已经成为目前计算机领域较为关心的问题.提出的调度算法针对实时多处理器计算环境,以执行时间最短的任务优先调度为基础,结合其他有效技术(共享空闲时间回收及检查点技术),使得实时任务在其截止期内完成的同时,能够动态地降低整个系统的能量消耗及动态容错.针对独立任务集及具有依赖关系的任务集,提出两种算法:STFBA1及STFBA2(shortest task first based algorithm).通过实验与目前所知的有效算法相比,算法具有更好的性能(调度长度及能量消耗)及较低的通信时间复杂度.     

多处理器单调速率任务分配算法性能评价

多处理器任务分配调度算法是一类经典实时调度算法，然而目前研究在如何根据任务集特征选择任务分配算法方面少见指导性原则，不利于提高多处理器任务分配算法的可调度率及使用尽可能少的处理器达到最优调度结果。基于两种多处理器任务调度策略的比较，本文给出划分策略下的多处理器RM调度的可调度条件和任务分配算法夏分析。仿真结果表明，各任务分配算法所需处理器数与任务集总利用率成正比。同时，分析总结出各算法适用范围及如何根据任务集利用率选择合适算法的指导原则。最后结果还表明，实际算法性能与理论性能界存在差异。     

基于分批优化的实时多处理器系统的集成动态调度算法

提出了一种基于分批优化的实时多处理器系统的集成动态调度算法，该算法采用在每次扩充当前局部调度时，通过对所选取的一批任务进行优化分配的策略以及软实时任务的服务质量QoS（quality of service）降级策略，以统一方式实现了对实时多处理器糸统中硬、软实时任务的集成动态调度．进行了大量的模拟研究，结果表明．在多种任务参数取值下，新算法的调度成功率均高于近视算法（Myopic Algorithm）．     

多处理器系统动态调度负载均衡节约算法

将任务集与处理器处理能力之间的匹配关系作为研究调度算法性能的重要因素,建立了相应的任务-处理器模型,以描述多处理器系统的负载状况.描述了多处理器系统任务可调度的必要条件,设计实现了任务集的生成方法.对节约算法进行改进,提出了负载均衡的节约算法.所提出的算法可在保证调度成功率的前提下,缩短任务的平均响应时间和调度长度,并均衡地提高处理器的利用率.     

基于动态关键任务的多处理器任务分配算法

多处理器调度问题是影响系统性能的关键问题,基于任务复制的调度算法是解决多处理器调度问题较为有效的方法.文中分析了几个典型的基于任务复制算法,提出了基于动态关键任务(DCT)的多处理器任务分配算法.DCT算法以克服贪心算法不足为要点,调度过程中动态计算任务时间参数,准确确定处理器的关键任务,以关键任务为核心优化调度,逐步改善调度结果,最终取得最优的调度结果.分析和实验证明,DCT算法优于现有其它同类算法.     

Xen中VCPU调度算法分析

为了降低虚拟化环境中虚拟机的性能开销,提高虚拟化实施效率,在综合考虑虚拟处理器在虚拟机调度过程中的需求的基础上,对Xen中基于信用度的调度算法进行了分析,该算法在处理器密集型应用、多处理器调度和QoS控制方面具有明显的优势.针对目前调度算法在多处理器和新型虚拟机监控器结构下存在的性能问题,提出了自旋锁优先和处理器绑定等优化措施.实例表明,该措施能够提高虚拟处理器的调度效率.     

多处理器混合关键性系统中的划分调度策略

多核处理器正越发广泛地应用到现代嵌入式系统的设计与实现当中,其强大的计算能力为将多个不同关键性级别的功能子系统集成到统一的共享资源平台提供了支持.混合关键性系统的调度问题即便在单处理器平台中都极具挑战性,在多处理器平台则更为困难.将目前资源利用率最高的单处理器混合关键性调度算法EY-VD扩展到多处理器平台中.首先,结合传统的划分调度策略提出了适用于多处理器混合关键性系统的MC-PEDF(mixedcriticality partitioned earliest deadline first)划分调度算法.尽管比之前的算法有更好的可调度性能,但传统的划分策略不能有效地平衡不同关键性级别下的负载,故其不完全适用于混合关键性系统.为了克服传统策略的不足,提出了划分调度策略OCOP(one criticality one partition).OCOP允许系统在关键性模式切换时对实时任务集进行重新划分,进而更好地平衡各个处理器在不同关键性模式中的资源利用率.基于OCOP,提出了第2种划分调度算法MC-MP-EDF(mixed-criticality multi-partitioned EDF).基于随机生成任务集的仿真实验结果表明,与MC-PEDF和已有的算法相比,MC-MP-EDF能够显著地提高系统的可调度性,尤其是在处理器数量较多的系统中.     

基于任务图的并行调度MEWFM算法

随着多核处理器体系结构在计算机领域的广泛应用,如何合理地对计算任务进行调度成为人们广泛讨论的问题。目前已经有针对多处理器的任务调度算法,但是这些算法在执行时要经过多次迭代,执行效率比较低。提出一种改进的波前调度算法MEWFM,它是一种执行时间短,加速比接近处理器核数的一种算法。这种算法主要包括任务图分层,层内调度和误差下降调度三个子算法。详细分析了这些算法的特点和执行流程。实验评测表明,算法在多处理器环境下的任务调度方面具有执行速度快,性能高等优势。     

云计算平台中面向车联网应用的能耗感知调度算法

针对面向车联网应用的云计算平台的高能耗问题,提出一种采用节能整合策略的能耗感知调度算法——任务集整合算法(Task Set Consolidation Algorithm)。该算法的主要思想是通过减少活跃物理服务器的数目,有效降低云平台的能量消耗。建立了云平台模型、车联网任务集模型和能耗模型,确定了云平台的节能目标函数和变量因子。仿真实验通过模拟多维资源多并发任务集的云平台环境,以物理服务器的活跃时间和活跃数目、云平台的能量消耗作为性能指标,将任务集整合算法与现有算法进行了比较。实验结果表明,TSC算法能够在避免任务集资源发生冲突的情况下,使面向车联网应用的云平台激活的物理服务器数量达到最少,能耗降到最低。     

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.     

基于图匹配的多处理机调度算法

本文对具有高通讯延迟的多处理机系统（机群系统）上的任务调度算法进行了研究，与以往算法主要考虑任务图的关键路径不同，本文给出了任务图的调度与其偶图匹配的对应关系，并由此提出了一种新的启发式算法，通过模拟试验显示本算法具有较好的调度效果。     

异构实时多处理器系统的动态调度算法研究

实时多处理器系统的动态调度算法一直是实时系统中的重要研究课题.根据异构实时多处理器的特点,提出了一种新的异构实时动态调度算法P_IEFT.该算法采用了一个新的处理器分配策略——将任务分配到能最早完成任务的处理器上.该策略能够缩短调度长度,提高后继任务被接受的可能性,从而能够提高成功调度率.模拟结果表明,该调度算法的成功调度率高于近视算法和节约算法的成功调度率.     

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.     

容错多处理机中一种高效的实时调度算法

针对基于主副版本容错的多处理机中独立的、抢占性的硬实时任务,提出了一种高效的调度算法——TPFTRM(task partition based fault tolerant rate-monotonic)算法.该算法将单机实时RM 算法扩展到容错多处理机上,并且调度过程中从不使用主动执行的任务副版本,而仅使用被动执行和主副重叠方式执行的任务副版本,从而最大限度地利用副版本重叠和分离技术提高了算法调度性能.此外,TPFTRM 根据任务负载不同将任务集合划分成两个不相交的子集进行分配;还根据处理机调度的任务版本不同,将处理机集合划分成3 个不相交的子集进行调度,从而使TPFTRM 调度算法便于理解、实现以及减少了调度所需要的运行时间.模拟实验对各种具有不同周期和任务负载的任务集合进行了调度测试.实验结果表明,TPFTRM与目前所知同类算法相比,在调度相同参数的任务集合时不仅明显减少了调度所需要的处理机数目,还减少了调度所需要的运行时间,从而证实了TPFTRM 算法的高效性.     

Performance Analysis of Power-Aware Task Scheduling Algorithms on Multiprocessor Computers with Dynamic Voltage and Speed

Task scheduling on multiprocessor computers with dynamically variable voltage and speed is investigated as combinatorial optimization problems, namely, the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint. The first problem has applications in general multiprocessor 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 where timing constraint is a major requirement. These problems emphasize the tradeoff between power and performance and are defined such that the power-performance product is optimized by fixing one factor and minimizing the other. It is found that both problems are equivalent to the sum of powers problem and can be decomposed into two subproblems, namely, scheduling tasks and determining power supplies. Such decomposition makes design and analysis of heuristic algorithms tractable. We analyze the performance of list scheduling algorithms and equal-speed algorithms and prove that these algorithms are asymptotically optimal. Our extensive simulation data validate our analytical results and provide deeper insight into the performance of our heuristic algorithms.     

Design of Fast and Efficient Energy-Aware Gradient-Based Scheduling Algorithms Heterogeneous Embedded Multiprocessor Systems

In this paper, we present two heuristic energy-aware scheduling algorithms (EGMS and EGMSIV) for scheduling task precedence graphs in an embedded multiprocessor system having processing elements with dynamic voltage scaling capabilities. Unlike most energy-aware scheduling algorithms that consider task ordering and voltage scaling separately from task mapping, our algorithms consider them in an integrated way. EGMS uses the concept of energy gradient to select tasks to be mapped onto new processors and voltage levels. EGM-SIV extends EGMS by introducing intra-task voltage scaling using a Linear Programming (LP) formulation to further reduce the energy consumption. Through rigorous simulations, we compare the performance of our proposed algorithms with a few approaches presented in the literature. The results demonstrate that our algorithms are capable of obtaining energy-efficient schedules using less optimization time. On the average, our algorithms produce schedules which consume 10% less energy with more than 47% reduction in optimization time when compared to a few approaches presented in the literature. In particular, our algorithms perform better in generating energy-efficient schedules for larger task graphs. Our results show a reduction of up to 57% in energy consumption for larger task graphs compared to other approaches.     

Data‐aware task scheduling on heterogeneous hybrid memory multiprocessor systems

In this paper, we propose a method about task scheduling and data assignment on heterogeneous hybrid memory multiprocessor systems for real‐time applications. In a heterogeneous hybrid memory multiprocessor system, an important problem is how to schedule real‐time application tasks to processors and assign data to hybrid memories. The hybrid memory consists of dynamic random access memory and solid state drives when considering the performance of solid state drives into the scheduling policy. To solve this problem, we propose two heuristic algorithms called improvement greedy algorithm and the data assignment according to the task scheduling algorithm, which generate a near‐optimal solution for real‐time applications in polynomial time. We evaluate the performance of our algorithms by comparing them with a greedy algorithm, which is commonly used to solve heterogeneous task scheduling problem. Based on our extensive simulation study, we observe that our algorithms exhibit excellent performance and demonstrate that considering data allocation in task scheduling is significant for saving energy. We conduct experiments on two heterogeneous multiprocessor systems. Copyright © 2016 John Wiley & Sons, Ltd.