基于改进遗传算法的双层网格任务调度

合理的任务调度算法可以在很大程度上提高网格系统的利用率。针对网格环境异构、分布等特点,提出了基于改进遗传算法(MRNGA)的双层网格任务调度算法,在简单遗传算法的基础上改进选择算子并引入了小生境技术,对网格结构采用双层编码的方式,仿真实验表明,该方法具有良好的搜索能力和资源负载均衡度,对异构系统中的任务调度具有较好的处理结果。     

基于人工鱼群遗传算法的异构多核系统任务调度研究

异构多核系统的任务调度问题已经被证明是一个NP完全问题。人工鱼群算法在算法初期具有较快的收敛速度,后期收敛较慢,而遗传算法的种群初始化具有较强的鲁棒性,初始化种群的质量直接影响着遗传算法的性能。本文提出了一种将人工鱼群算法与遗传算法相结合的任务调度算法,首先分析了异构多核系统的任务调度问题的本质,使用改进的人工鱼群算法来构建遗传算法的初始化种群,并使用改进的遗传算法进行迭代进化,从而提高了算法的收敛速度。     

基于Min-Min遗传算法的网格任务调度方法

针对网格环境异构、分布等特点,在现有的任务调度算法的基础上,结合Min-Min算法和遗传算法的优点,提出了一种基于Min-Min遗传算法的任务调度方法。仿真实验表明:在网格环境下,该算法具有合理性和高效性。     

基于动态粒子群优化的网格任务调度算法*

提出了一种基于动态粒子群优化的网格任务调度算法。设计了网格任务调度问题的数学模型,给出了自适应变异的动态粒子群优化算法的框架,引入了自适应学习因子和自适应变异策略,从而使算法具有动态自适应性,能够较容易地跳出局部最优。实验结果表明,本文算法能有效地解决异构网格任务调度问题,具有较好的应用价值。     

基于融合进化计算的网格任务调度算法

在网格计算中，任务调度是一个重要的组成部分。针对网格环境异构、分布等特点，该文结合遗传算法与蚂蚁算法的优点，在双层进化结构基础上，提出了一种基于融合进化计算的网格任务调度算法。模拟实验结果表明：在网格环境下，调度算法具有明显的优势。     

混合算法在网格任务调度中的应用研究

研究网格任务优化调度问题,针对需求的复杂和网格系统具有异构性和动态性,导致网络任务调度过程相当困难.传统调度算法调度效率低、资源负载不平衡.为了提高任务调度效率,降低资源负载不平衡性,提出一种混合的网格任务调度优化算法.首先采用遗传算法全局搜索能力快速形成初始解,然后将遗传算法的调度结果作为蚁群算法的初始信息素分布,最后利用蚁群算法所正反馈性机制迅速地形成任务调度的最优解.仿真结果表明,混合算法减少网格任务调度系统任务完成时间,提高了任务调度效率,为网格设计提供了依据.     

异构分布式系统的负载均衡调度算法

提出一种异构分布式系统的负载均衡调度算法。对异构系统进行建模,使用染色体建立任务集合调度模型,根据该模型制定适应度函数,将其作为衡量负载均衡的标准,利用该标准对异构系统进行任务调度,并动态设定最大进化代数,以此改进动态遗传算法。实验结果表明,该算法具有较好的负载均衡性能。     

一种粒子群优化的异构多处理器任务调度算法

为提高异构多处理器任务调度的执行效率,充分发挥多处理器并行性能,提出一种基于粒子群优化的异构多处理器任务调度算法-PSOASA算法.PSOASA算法以求得任务最短完成时间为目标,首先采用整数矩阵对粒子进行编码,并定义交换操作更新粒子状态,实现粒子搜索空间到离散空间的映射,使连续的粒子群优化算法适用于离散的异构多处理器任务调度问题,同时引入模拟退火算法,克服粒子群算法的“早熟”收敛现象,避免求得的解陷入局部最优.实验结果表明,PSOASA算法的执行效率优于目前广泛采用的遗传算法,有效地降低任务执行时间,减少了迭代次数,适用于异构多处理器环境大规模任务调度.     

基于粒子群优化算法的异构多处理器任务调度

为提高异构多处理器任务调度的执行效率,充分发挥多处理器并行性能,提出一种基于粒子群优化的异构多处理器任务调度算法-PSOASA算法.PSOASA算法以求得任务最短完成时间为目标,首先通过建立新的编码方式和粒子更新公式实现粒子搜索空间到离散空间的映射,使连续的粒子群优化算法适用于离散的异构多处理器任务调度问题,同时通过引入模拟退火算法,克服粒子群算法的“早熟”收敛现象,避免求得的解陷入局部最优.实验结果表明,PSOASA算法的执行效率优于目前广泛采用的遗传算法,有效地降低任务的执行时间,减少了迭代次数,适用于异构多处理器环境大规模任务调度.     

基于改进遗传算法的网格任务调度算法

网格任务调度是一个NP完全问题,它关注大规模的资源和任务调度,要求采用的调度算法能够具有高效性.遗传算法被证明是解决这类小规模问题的有效算法,随着任务数和资源数的增加,遗传算法表现出慢速收敛的缺点.为了克服其缺点,提出将Min-min算法与遗传算法相结合的改进遗传算法,从而设计出很好的选择和交叉算子,提高了算法搜索能力和收敛速度.仿真结果表明该算法能更有效解决网格任务调度问题.     

基于禁忌算法的双层结构网格任务调度研究

合理的任务调度算法可以在很大程度上提高网格系统的利用率。针对网格环境异构、分布等特点,提出了基于禁忌搜索算法（TS）的双层结构网格任务调度算法,仿真实验表明,该方法具有良好的解质量和搜索能力,对异构系统中的任务调度具有较好的处理结果。     

Improving scheduling of tasks in a heterogeneous environment

Optimal scheduling of parallel tasks with some precedence relationship, onto a parallel machine is known to be NP-complete. The complexity of the problem increases when task scheduling is to be done in a heterogeneous environment, where the processors in the network may not be identical and take different amounts of time to execute the same task. We introduce a task duplication-based scheduling algorithm for network of heterogeneous systems (TANH), with complexity O(V/sup 2/), which provides optimal results for applications represented by directed acyclic graphs (DAGs), provided a simple set of conditions on task computation and network communication time could be satisfied. The performance of the algorithm is illustrated by comparing the scheduling time with an existing "best imaginary level scheduling (BIL)" scheme for heterogeneous systems. The scalability for a higher or lower number of processors, as per their availability is also discussed. We have shown to provide substantial improvement over existing work on the task duplication-based scheduling algorithm (TDS).     

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.     

异构系统中一种基于可用性的抢占式任务调度算法*

针对大多数现有的异构系统调度算法没有考虑由多类任务特别是抢占式任务所引起的可用性需求的不足,在现有基于可用性的非抢占式任务调度算法的基础上,通过计算任务的平均等待时间来确定优先级等级,对异构系统中多类抢占式任务的可用性约束的调度问题进行了探索,提出了一种基于可用性的抢占式优先调度算法P-SSAC。该算法在不增加硬件代价的前提条件下通过调度增加了系统的可用性,缩短了任务的平均等待时间,同时该算法可对抢占式的任务进行有效调度。仿真实验结果表明,该算法有效实现了异构系统可用性和任务等待时间之间的折中。     

一种批优化调度策略的实时异构系统的集成动态调度算法

针对实时异构多任务调度的特点,提出了软、硬实时任务形式化描述非精确计算的统一任务模型,在此基础上,提出了一种基于批优化调度策略的实时异构系统的集成动态调度算法.该算法以启发式搜索为基础,引入软实时任务服务质量降级策略,在每次扩充当前局部调度时,按制定的规则选取一批任务,计算其在各处理器上运行的目标函数,采用指派问题解法对任务优化分配.模拟实验表明,该算法与同类算法相比,提高了调度成功率.     

A hybrid list-based task scheduling scheme for heterogeneous computing

The Journal of Supercomputing - Efficient task scheduling is required to attain high performance in both homogeneous and heterogeneous computing systems. An application can be considered as a task...     

Stochastic DAG scheduling using a Monte Carlo approach

In heterogeneous computing systems, there is a need for solutions that can cope with the unavoidable uncertainty in individual task execution times, when scheduling DAGs. When such uncertainties occur, static DAG scheduling approaches may suffer, and some rescheduling may be necessary. Assuming that the uncertainty in task execution times is modelled in a stochastic manner, we may be able to use this information to improve static DAG scheduling considerably. In this paper, a novel DAG scheduling approach is proposed to solve this stochastic scheduling problem, based on a Monte Carlo method. The approach is built on the top of a classic static DAG scheduling heuristic and evaluated through extensive simulation. Empirical results show that a significant improvement of average application performance can be achieved by the proposed approach at a reasonable execution time cost.     

实时异构系统的动态调度算法研究

实时多处理器系统是解决复杂时应用的有效手段,目前对实时多处理器调度算法的研究却大多集中在同构系统上,对实时异构系统的调度则研究得比较少,提出了一种新的实时异构系统的动态调度算法,该算法采用了集中式的调度方案,同时,引入了一个新的任务分配策略,从而通过提高任务可行性而提高了算的调度成功率,此外,为了评估该算法的性能,还进行了大量的模拟研究,由于近视算法经简单修改便可以应用到实时异构系统的动态调度中,因此,在模拟研究中,以近视算法作为基准,将其应用于实时异构系统动态调度时的性能与新算法进行了比较,模拟结果显示,在多种任务参数的取值下,新算法的调度成功率均高于近视算法。     

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.     

实时异构系统中的积极复制容错调度算法

在设计实时异构系统中的容错调度算法时,既要考虑到实时性的约束,又要最大化系统的可靠性.此外,异构系统中的并行应用调度问题已经被证明了是NP完全问题.现有的容错调度算法大多采用复制技术来提升系统的可靠性,但是任务的多次执行会导致应用执行时间变长,系统实时性下降.为此,提出了一个基于积极复制技术的容错调度算法,该算法连续的复制任务集中对当前系统实时性影响最小的任务,然后将任务集中的所有任务调度至最早完成的处理器,用以在满足实时性约束的同时,提升系统的可靠性.实验表明,相比于同样着眼于实时异构系统的DB-FTSA算法,该算法在实时性约束严格的情况下,可靠性有较大提升.