树型网格计算环境下的独立任务调度

任务调度是实现高性能网格计算的一个基本问题,然而,设计和实现高效的调度算法是非常具有挑战性的.讨论了在网格资源计算能力和网络通信速度异构的树型计算网格环境下,独立任务的调度问题.与实现最小化任务总的执行时间不同(该问题已被证明是NP难题),为该任务调度问题建立了整数线性规划模型,并从该线性规划模型中得到最优任务分配方案??各计算节点最优任务分配数.然后,基于最优任务分配方案,构造了两种动态的需求驱动的任务分配启发式算法:OPCHATA(optimization-based priority-computation heuristic algorithm for task allocation)和OPBHATA(optimization-basedpriority-bandwidth heuristic algorithm for task allocation).实验结果表明:在异构的树型计算网格环境下实现大量独立任务调度时,该算法的性能明显优于其他算法.     

低能耗软件设计中的性能无损电压调度技术研究

合理地运用动态电压调整技术可以有效降低软件运行所需的能耗·从归纳分析电压调整特征入手,针对程序执行中存在电压调整特征差异的情况,提出了性能无损的低能耗电压调度问题·把该问题形式化为一个混合整数规划模型(MILP),提出了基于剖析结果的PGS算法和基于分析结果的ADS算法·实例分析表明所提出的方法能够有效实现性能无损的低能耗软件设计,模拟实验表明启发式算法可实现较好的近似解·     

一种基于QoS的自适应网格失效检测器

失效检测器是构建可靠的网格计算环境所必需的基础组件之一.由于网格中存在大量对失效检测有着不同QoS需求的分布式应用,对于一个网格失效检测器来说,为保持其有效性和可扩展性,应该既能够准确提供应用程序所需的失效检测QoS,又能够避免为满足不同QoS而设计多套失效检测器所产生的多余负载.基于QoS基本评价指标,采用PULL模式主动检测策略实现了一种新的失效检测器--GA-FD(adaptive failure detector for grid),可以同时支持多个应用程序定量描述的QoS需求,不需要关于消息行为和时钟同步的任何假设.同时,证明了GA-FD在部分同步模型下可实现一个◇P类的失效检测器,并给出了相应的实验及数据.     

异构多核处理器的任务分配及能耗的研究*

异构多核处理器采用不同的任务分配与调度算法,会导致不同的时间消耗与能量消耗,采用合适的任务分配与调度算法能节省较多的能耗。目前普遍认为最有发展前途的任务分配与调度技术是先用启发式方法进行分组,然后再用遗传算法进行调度。在改进任务分组后,又首次提出了用遗传算法解决能耗问题。实验结果表明在实时要求不高的情况下,能以较小的时间代价来节省较多的能耗。     

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     

一个调度Out-Tree任务图的启发式算法

任务调度问题是并行分布式计算中的挑战性问题之一。大多数实际的调度算法是启发式的因而常常具有改进的余地。针对Out-Tree任务图这一基本结构提出一个基于任务复制的启发式调度算法,该算法在确保最短调度长度的同时,注重处理器的负载平衡,以达到节约处理器的目的。比较性实验的结果表明,该算法确保了最短调度长度且使用的处理器最少。因而,该算法提高了系统的利用率,避免消耗过多的资源,实际应用性更好。     

非对称多核处理器上的操作系统集成调度

相对于对称多核处理器,非对称多核处理器具有更高的效能,将成为未来并行操作系统中的主流体系结构.对于非对称多核处理器上操作系统的并行任务调度问题,现有的研究假设所有核心频率恒定,缺乏理论分析,也没有考虑算法的效能和通用性.针对该问题,该文首先建立非线性规划模型,分析得出全面考虑并行任务同步特性、核心非对称性以及核心负载的调度原则.然后,基于调度原则提出一个集成调度算法,该算法通过集成线程调度和动态电压频率调整来提高效能,并通过参数调整机制实现了算法的通用性.提出的算法是第一个在非对称多核处理器上结合线程调度和动态电压频率调整的调度算法.实际平台上的实验表明:该算法可适用于多种环境,且效能比其他同类算法高24%～50%.     

基于DAG图解-重构的机群系统静态调度算法

机群系统静态任务调度是NP-完全问题,通常的算法是通过一些启发式算法得到多项式次优 解.该文提出的图解-子图重构算法实现了对分布在有向无环图(directed acyclic graph, 简称DAG)上的并行任务的快速有效调度.该算法的复杂性为O(log^|V|×(|V|+| E|)),采用递归方法实现了对任务图的有效分解和子图重构,生成任务群,完成任务调度,并 且初步实现了对处理机的优化.通过实例分析以及与其他启发式调度算法的性能比较,证明该 算法是一种快速、有效、可     

Task graph pre-scheduling, using Nash equilibrium in game theory

Prescheduling algorithms are targeted at restructuring of task graphs for optimal scheduling. Task graph scheduling is a NP-complete problem. This article offers a prescheduling algorithm for tasks to be executed on the networks of homogeneous processors. The proposed algorithm merges tasks to minimize their earliest start time while reducing the overall completion time. To this end, considering each task as a player attempting to reduce its earliest time as much as possible, we have applied the idea of Nash equilibrium in game theory to determine the most appropriate merging. Also, considering each level of a task graph as a player, seeking for distinct parallel processors to execute each of its independent tasks in parallel with the others, the idea of Nash equilibrium in game theory can be applied to determine the appropriate number of processors in a way that the overall idle time of the processors is minimized and the throughput is maximized. The communication delay will be explicitly considered in the comparisons. Our experiments with a number of known benchmarks task graphs and also two well-known problems of linear algebra, LU decomposition and Gauss–Jordan elimination, demonstrate the distinguished scheduling results provided by applying our algorithm. In our study, we consider ten scheduling algorithms: min–min, chaining, A ^?, genetic algorithms, simulated annealing, tabu search, HLFET, ISH, DSH with task duplication, and our proposed algorithm (PSGT).     

一个调度Fork-Join任务图的新算法

任务调度是影响工作站网络效率的关键因素之一.Fork-Join任务图可以代表很多并行结构,但其他已有调度Fork-Join任务图算法忽略了在非全互连工作站网络环境中通信之间不能并行执行的问题,有些效率高的算法又没有考虑节省处理器个数的问题.因此,专门针对该任务图,综合考虑调度长度、非并行通信和节省处理器个数问题,提出了一个基于任务复制的静态调度算法TSA_FJ.通过随机产生任务的执行时间和通信时间,生成了多个Fork-Join任务图,并且采用TSA_FJ算法和其他调度算法对生成的任务图进行调度.结果表明,     

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.     

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.     

基于多目标全局约束的任务分配和调度算法

针对嵌入式系统中大多数任务执行算法不考虑目标成本问题,提出了一种基于多目标全局约束的任务分配和调度算法。算法使用约束逻辑编程来对任务执行资源如处理单元、通信设备以及代码和数据存储量的使用进行多目标全局约束。算法假设ROM和RAM分别用于代码存储和数据存储,算法还考虑数据在数据存储器中的位置。实验结果表明,尽管在多个约束条件下,提出的任务分配和调度算法无论在代码存储和数据存储量使用方面,还是在对任务有效求解方面都能取得比普遍采用的贪婪调度算法更好的结果。     

Degree-of-Node Task Scheduling of Fine-Grained Parallel Programs on Heterogeneous Systems

Processor specialization has become the development trend of modern processor industry. It is quite possible that this will still be the main-stream in the next decades of semiconductor era. As the diversity of heterogeneous systems grows, organizing computation efficiently on systems with multiple kinds of heterogeneous processors is a challenging problem and will be a normality. In this paper, we analyze some state-of-the-art task scheduling algorithms of heterogeneous computing systems and propose a Degree of Node First (DONF) algorithm for task scheduling of fine-grained parallel programs on heterogeneous systems. The major innovations of DONF include:1) simplifying task priority calculation for directed acyclic graph (DAG) based fine-grained parallel programs which not only reduces the complexity of task selection but also enables the algorithm to solve the scheduling problem for dynamic DAGs; 2) building a novel communication model in the processor selection phase that makes the task scheduling much more efficient. They are achieved by exploring finegrained parallelism via a dataflow program execution model, and validated through experimental results with a selected set of benchmarks. The results on synthesized and real-world application DAGs show a very good performance. The proposed DONF algorithm significantly outperforms all the evaluated state-of-the-art heuristic algorithms in terms of scheduling length ratio (SLR) and efficiency.     

Interval-indexed formulation based heuristics for single machine total weighted tardiness problem

In this paper, we solve the single machine total weighted tardiness problem by using integer programming and linear programming based heuristic algorithms. Interval-indexed formulation is used to formulate the problem. We discuss several methods to form the intervals and different post-processing methods. Then, we show how our algorithm can be used to improve a population of a genetic algorithm. We also provide some computational results that show the effectiveness of our algorithm. Many aspects of our heuristic algorithm are quite general and can be applied to other scheduling and combinatorial optimization problems.     

Cluster-based multicore real-time mixed-criticality scheduling

Cluster-based scheduling is recently gaining importance to be applied to mixed-criticality real-time systems on multicore processors platform. In this approach, the cores are grouped into clusters, and tasks that are partitioned among different clusters are scheduled by global scheduler in each cluster. This research work introduces a new cluster-based task allocation scheme for the mixed-criticality real-time task sets on multicore processors. For task allocation, smaller clusters sizes (sub-clusters) are used for mixed-criticality tasks in low criticality mode, while relatively larger cluster sizes are used for high criticality tasks in high criticality mode. In this research paper, the mixed-criticality task set is allocated to clusters using worst-fit heuristic. The tasks from each cluster are also allocated to its sub-clusters, using the same worst-fit heuristic. A fixed-priority response time analysis approach based on Audsley’s approach is used for the schedulability analysis of tasks in each cluster and sub-cluster. If the high criticality job is not completed after its worst case execution time in low mode, then the system is switched to high criticality mode. After mode switch, all the low criticalities tasks are discarded and only high criticality tasks are further executed in high criticality mode. Simulation results indicate that the percentage of schedulable task sets significantly increases under cluster scheduling as compared to partitioned and global mixed-criticality scheduling schemes.     

一个调度Out-Tree任务图的新算法

Out-Tree任务图代表分治算法的一大类问题。本文专门针对该类任务图，提出了一个新的调度算法。它利用fork结构的最优调度为各任务定义优先级，准确的反映了任务对调度的影响，保证了任务的正确调度顺序，得到优的调度长度。并在不改变调度长度的情况下，将结点尽可能地分配到已用处理器上，节省了处理器。实验表明，本文算法的调度性能优于现有同类算法。     

针对移动云计算任务迁移的快速高效调度算法

计算量较大的应用程序由于需要大量的能耗,因此在电池容量有限的移动设备上运行时十分受限。云计算迁移技术是保证此类应用程序在资源有限的设备上运行的主流方法。针对无线网络中应用程序任务图的调度和迁移问题,提出了一种快速高效的启发式算法。该算法将能够迁移到云端的任务都安排在云端完成这种策略作为初始解,通过逐次计算可迁移任务在移动端运行的能耗节省量,依次将节省量最大的任务迁移到移动端,并依据任务间的通讯时间及时更新各个任务的能耗节省量。为了寻找全局最优解,构造了适用于此问题的禁忌搜索算法,给出了相应的编码方法、禁忌表、邻域解以及算法终止准则。构造的禁忌搜索算法以提出的启发式解为初始解进行全局搜索,并实现对启发解的进一步优化。通过 实验 将所提方法与无迁移、随机迁移、饱和迁移3类算法进行对比,结果表明提出的启发式算法能够快速有效地给出能耗更小的解。例如,在宽度为10的任务图上,当深度为8时,无迁移、随机迁移与饱和迁移的能耗分别为5461、3357和2271能量单位,而给出的启发解对应的能耗仅为2111。在此基础上禁忌搜索算法又将其能耗降低到1942, 这进一步说明了提出的启发式算法能够产生高质量的近似解。     

基于蚁群算法的分布多处理机系统任务调度研究

本文主要基于现代蚁群算法讨论分布式系统调度。蚁群算法是一种构造型启发算法,在离散优化问题中得到广泛应用。分布式系统调度属于NP-hard,为了提高算法性能,把问题任务图的优先级作为启发信息。最后,采用随机产生的任务图将调度结果和模拟退火算法、遗传算法等进行了比较。     

On exploiting task duplication in parallel program scheduling

One of the main obstacles in obtaining high performance from message-passing multicomputer systems is the inevitable communication overhead which is incurred when tasks executing on different processors exchange data. Given a task graph, duplication-based scheduling can mitigate this overhead by allocating some of the tasks redundantly on more than one processor. In this paper, we focus on the problem of using duplication in static scheduling of task graphs on parallel and distributed systems. We discuss five previously proposed algorithms and examine their merits and demerits. We describe some of the essential principles for exploiting duplication in a more useful manner and, based on these principles, propose an algorithm which outperforms the previous algorithms. The proposed algorithm generates optimal solutions for a number of task graphs. The algorithm assumes an unbounded number of processors. For scheduling on a bounded number of processors, we propose a second algorithm which controls the degree of duplication according to the number of available processors. The proposed algorithms are analytically and experimentally evaluated and are also compared with the previous algorithms