模糊环境中工作流任务分配的多级模型

对工作流实例中各用户型任务进行自动优化分配是提高工作流管理系统运行效率的关键技术之一 .在详细分析了影响用户型任务分配的多种因素及其语意模糊性后,提出了一个可组合的任务分配多级模型以及相应的任务分配方法,详细讨论了具有自适应能力的影响因素权重设计方法 .最后,通过与其他任务分配方法对比,分析了该任务分配方法的性能 .     

基于信任度的合一系统社会任务分配

在多智体社会网络中,传统的任务分配模型一般采用直接面向任务执行者的分配机制.它们不考虑社会网络组织结构对任务分配性能的巨大影响,也很少透彻地研究不可靠社会中的任务分配.针对这些问题,本文开创性地研究了软硬件合一系统的任务分配,即按递阶、分层的思想设计了协作组织模型,并基于此提出了面向社区基于社会协调“软件人”的任务分配模型.模型研究过程中,提出了基于直接信任度和社区声誉的社区信任度评估机制、基于社区信任度和社区物理能力的节点选择机制、基于负载均衡的社区内任务分配机制和基于上下文资源的任务再分配策略.实验结果表明：与常见的直接面向任务执行者和基于资源的任务分配模型相比,所提出的模型具有更优的任务分配性能,且对社会任务环境变化具有更好的鲁棒性;社区内基于负载均衡的分配机制和基于上下文资源的再分配策略也有效提高了分配性能,降低了网络中的通信密度.     

基于MPI的主从式并行任务分配的研究与实现

并行计算中的任务分配和负载平衡是衡量程序性能的两个重要因素.针对任务分配问题,先用求连通分量的算法将任务分配成若干个任务组,再利用队列的性质,将每组任务动态地分配给空闲的从进程,从而一定程度上减少了问题解决的时间,同时达到了负载平衡的目的.     

多Agent层次任务分配方法

提出了一种层次任务分配方法,用于解决动态环境中的任务分配问题.利用全局分配方法为Agent分配合适的任务,当环境发生变换时,通过局部调整来解决任务和Agent之间的匹配问题,使得每个Agent能够根据局部信息选择理想的任务来执行,提高了分配算法的鲁棒性和多Agent整体效用.仿真实验结果表明,该方法是可行且有效的,能够解决动态环境中的任务分配问题.     

基于页着色的多核处理器共享Cache动态分区

随着多核/众核成为处理器结构发展的主流,并行任务间共享地使用Cache而导致的冲突越来越成为性能提升的瓶颈.利用页着色可以实现对Cache的分区管理,减少共享Cache导致的冲突.页着色的原理是利用内存与Cache之间的组相联映射关系,通过控制分配固定区域的内存而达到分配固定区域Cache的目的,这一方面限制了任务能够请求的物理内存范围,另一方面调整程序使用的Cache空间需要做大量的内存拷贝,带来了不可忽视的开销.为了克服页着色的缺点,文中通过动态内存分配的方式,只对动态分配的页进行着色,在不修改内核和程序源码的前提下实现了动态Cache分区.文中提出的动态内存分配策略(CachePM)会根据运行时环境为任务分配内存,避免不同任务间共享Cache的冲突和同一任务内出现Cache的访问热点,通过合理划分程序运行时动态分配的内存达到Cache分区的目的.当任务的运行环境改变时,CachePM自适应地改变已经分配的堆中数据在物理内存中的布局,以实现Cache分区的动态调节.为进一步降低动态页着色的开销,作者采用了减少和延迟内存拷贝的策略.实验表明,该方法能够有效实现动态Cache分区,从而提高并行运行的任务的性能;同时由于动态内存分配策略避免了同一任务内出现Cache访问热点,单独运行的任务的性能也较在libc下运行有所提升.     

基于粒子群算法的移动边缘计算任务分配方法

为提高移动终端任务分配效率,降低计算能量损耗,提出基于粒子群算法的移动边缘计算任务分配方法。通过构建异构网络获取完整的需要分配的任务,明确任务分配时所需的特定条件,即分配消耗和时延等。将分配任务转化成寻找分配结果的最优解,构建最优解模型,利用粒子群算法对模型实施求解,经过不断迭代和更新,生成最优边缘计算任务的分配结果。实验结果表明,粒子群方法在分配任务数量为20~100之间时计算时间在1 s~3.3 s;当任务数量为100时,本文方法能耗仅为4107 J;粒子群方法在任务达到率达到100%时,其时延仅为12.5 ms;其任务分配计算时间短、能量消耗小和数据传输的时延短,能较好地满足实际应用需要。     

基于SPSO的机械流水线负载任务分配方法

在弱通信条件下,传统的机械流水线任务分配方法采用任务的随机分配,无法根据流水线的实际负载能力,将应有的任务量分配到相应的流水线上。提出一种基于简化粒子群优化算法(Simplified particle swarm optimization algorithm,SPSO)的高效机械流水线任务分配方法,首先对每个流水线的实际负载能力进行动态评估。然后采用粒子群优化算法对所有流水线负载分配相应的计算任务。由于每个负载的任务量是根据实际的流水线性能来分配的,所以可以使得全局的效率最优化。最后通过实验对算法的性能进行验证。结果显示,改进方法在基于粒子群优化的机械流水线任务分配下,任务根据流水线性能,呈现很好的聚类,算法收敛性好,分配任务速度快,具有很好的应用价值。     

基于能力及任务需求的多Agent任务分配方法

针对动态环境中的任务和Agent的动态变化问题,提出一种基于能力及任务需求的层次任务分配方法.利用全局分配方法为Agent分配合适的任务,当环境发生变换时,通过局部调整来解决任务和Agent之间的不适应问题,每个Agent能够根据局部信息选择理想的任务执行,提高了分配算法的鲁棒性.仿真实验表明该方法是可行及有效的,能够解决动态环境中的任务分配问题.     

一种智能的元任务调度算法

提出一种新的元任务调度算法.该算法根据网格中当前可用的计算资源、存储资源和元任务对这些资源的不同需求,选择一些任务预先分配到其中的一种资源上;再根据运行时另一种资源的可用情况,调整预分配任务运行顺序和给未预分配的任务分配资源,平衡计算资源和存储资源的负载并使元任务的完成时间趋向最短.     

基于分解优化的多星合成观测调度算法

某些卫星的侧摆性能较差, 必须进行合成观测以提高观测效率. 研究了多星联合对地观测中的任务合成观测调度问题. 提出了将原问题分解为任务分配与任务合成的分解优化思路. 任务分配为任务选择卫星资源及时间窗口; 任务合成则针对该分配方案,将分配到各卫星的任务按照轨道圈次分组, 分别进行最优合成. 采用蚁群优化算法(Ant colony optimization, ACO)求解任务分配问题, 通过自适应参数调整及信息素平滑策略, 实现全局搜索和快速收敛间的平衡.提出了基于动态规划的最优合成算法, 求解任务合成子问题,能够在多项式时间内求得最优合成方案. 依据分配方案的合成结果, 得到优化方案的特征信息, 反馈并引导蚁群优化算法对任务分配方案的搜索过程. 大规模测试算例验证了本文算法的效率.     

Locality-sensitive task allocation and load balancing in networked multiagent systems: Talent versus centrality

With the development of large scale multiagent systems, agents are always organized in network structures where each agent interacts only with its immediate neighbors in the network. Coordination among networked agents is a critical issue which mainly includes two aspects: task allocation and load balancing; in traditional approach, the resources of agents are crucial to their abilities to get tasks, which is called talent-based allocation. However, in networked multiagent systems, the tasks may spend so much communication costs among agents that are sensitive to the agent localities; thus this paper presents a novel idea for task allocation and load balancing in networked multiagent systems, which takes into account both the talents and centralities of agents. This paper first investigates the comparison between talent-based task allocation and centrality-based one; then, it explores the load balancing of such two approaches in task allocation. The experiment results show that the centrality-based method can reduce the communication costs for single task more effectively than the talent-based one, but the talent-based method can generally obtain better load balancing performance for parallel tasks than the centrality-based one.     

A massively parallel fault-tolerant architecture for time-critical computing

Building large-scale parallel computer systems for time-critical applications is a challenging task since the designers of such systems need to consider a number of related factors such as proper support for fault tolerance, efficient task allocation and reallocation strategies, and scalability. In this paper we propose a massively parallel fault-tolerant architecture using hundreds or thousands of processors for critical applications with timing constraints. The proposed architecture is based on an interconnection network called thebisectional network. A bisectional network is isomorphic to a hypercube in that a binary hypercube network can be easily extended as a bisectional network by adding additional links. These additional links add to the network some rich topological properties such as node symmetry, small diameter, small internode distance, and partitionability. The important property of partitioning is exploited to propose a redundant task allocation and a task redistribution strategy under realtime constraints. The system is partitioned into symmetric regions (spheres) such that each sphere has a central control point. The central points, calledfault control points (FCPs), are distributed throughout the entire system in an optimal fashion and provide two-level task redundancy and efficiently redistribute the loads of failed nodes. FCPs are assigned to the processing nodes such that each node is assigned two types of FCPs for storing two redundant copies of every task present at the node. Similarly, the number of nodes assigned to each FCP is the same. For a failure-repair system environment the performance of the proposed system has been evaluated and compared with a hypercube-based system. Simulation results indicate that the proposed system can yield improved performance in the presence of a high number of node failures.     

大规模CFD多区结构网格任务负载平衡算法

针对现有负载平衡算法的适应度低、可扩展性差、通信开销度量不准确的缺陷, 提出一种大规模CFD多区结构网格任务负载平衡算法。通过对网格块的分割、网格块之间的组合映射、进程上网格计算量的调整来实现并行CFD任务负载平衡。实验结果表明, 该算法既适应同构平台也适应异构平台, 既适应网格块数多于进程数的情况也适应网格块数少于进程数的情况, 该算法可使得整个计算空间分配到各进程上的计算量负载平衡, 同时使得各进程间的最大通信开销最小。     

基于势博弈的异构多智能体系统任务分配和重分配EI北大核心CSCD

针对异构多智能体系统,基于势博弈理论提出一种新的任务分配和重分配算法.考虑任务执行同步性和任务时效性的多重约束,导致异构多智能体系统中各个体任务执行时间受到多种限制,建立一个基于势博弈的算法结构,使系统以分布式方式工作.在此基础上,基于势博弈理论设计任务分配算法,保证在较低复杂度的同时,可以得到近似最大化期望全局效用的良好分配方案,并且随后将所提出的方法推广到任务重分配方案实现故障下的容错.最后,针对攻击任务场景对所提算法进行仿真验证,结果表明,在期望全局效用、容错能力和算法复杂度方面具有全面的性能.     

An efficient grid scheduling strategy for data parallel applications

Scheduling large-scale application in heterogeneous grid systems is a fundamental NP-complete problem that is critical to obtain good performance and execution cost. To achieve high performance in a grid system it requires effective task partitioning, resource management and load balancing. The heterogeneous and dynamic nature of a grid, as well as the diverse demands of applications running on the grid, makes grid scheduling a major task. Existing schedulers in wide-area heterogeneous systems require a large amount of information about the application and the grid environment to produce reasonable schedules. However, this required information may not be available, may be too expensive to collect, or may increase the runtime overhead of the scheduler such that the scheduler is rendered ineffective. We believe that no one scheduler is appropriate for all grid systems and applications. This is because while data parallel applications in which further data partitioning is possible can be further improved by efficient management of resources, smart selection of resources and load balancing can be possible, in functional/not-dividable-task parallel applications such partitioning is either not possible or difficult or expensive in term of performance. In this paper, we propose a scheduler for data parallel applications (SDPA) which offers an efficient task partitioning and load balancing strategy for data parallel applications in grid environment. The proposed SDPA offers two major features: maintaining job priority even if insufficient number of free resources is available and pre-task assignment to cut the idle time of nodes. The SDPA selects nodes smartly according to the nature of task and the nodes’ resources availability. Simulation results conducted reveal that SDPA achieves performance improvement over reported strategies in the reviewed literature in terms of execution time, throughput and waiting time.     

Parallel Objects Migration: A Fine Grained Approach to Load Distribution

Migration is a fundamental mechanism for achieving load balancing and locality of references in parallel and distributed applications. This paper presents the migration mechanisms implemented in the Parallel Objects (PO) programming environment, which assumes a fine granularity in allocation and reallocation of objects. In fact, a PO object can dynamically distribute its components onto several nodes depending on its dynamic need for resources, and the migration mechanisms implemented in PO allow object components to migrate independently of each other. This paper describes how the PO environment can exploit the migration mechanisms via an embedded load-balancing policy, possibly driven by user-defined allocation hints, and evaluates the effectiveness of the approach in several application examples.     

遥感影像并行处理中基于优先级的任务分配策略

对集群环境下大规模遥感影像并行计算中任务分配效率低、负载不均衡的问题进行分析讨论,在此基础上建立多机任务分配模型,提出一种基于计算节点优先级的任务分配算法。该算法综合考虑计算节点的负载和性能,在任务分配时实时地收集各个节点的信息,计算出各个计算节点的优先级,按照优先级的高低分配任务,保证在满足集群间负载均衡的前提下能合理地将任务分配到计算节点。实验结果表明,该算法能快速实时地进行任务分配,任务的分布更加合理和均匀,并且当任务个数增多时,算法的执行效率要比轮转调度算法高出约2倍。     

Adaptive function allocation stabilization and a comparison of trigger types and adaptation strategies

Two experiments were conducted to (1) test and quantify the effect of an adaptive function allocation system on human performance under different combinations of trigger type (heart rate vs. performance-based) and function allocation adaptation strategy (complete reallocation, partial reallocation, partial transformation) and (2) to determine if the adaptive function allocation system continues to actively change the level of automation over relatively long periods of time (30 min). It was found that the adaptive function allocation system improves primary task performance by, on average, 6% and does not improve secondary task performance. In addition, the level of automation did not stabilize over the 30 min, suggesting the adaptive function a system continues to be relevant even over longer periods of time. Lastly, the study found that the use of heart rate as a trigger mechanism resulted in many more reallocations of function than a performance-based measure.     

Parallel Implementation of a Class of Adaptive Signal Processing Applications

Recently, High Performance Computing (HPC) platforms have been employed to realize many computationally demanding applications in signal and image processing. These applications require real-time performance constraints to be met. These constraints include latency as well as throughput. In order to meet these performance requirements, efficient parallel algorithms are needed. These algorithms must be engineered to exploit the computational characteristics of such applications. In this paper we present a methodology for mapping a class of adaptive signal processing applications onto HPC platforms such that the throughput performance is optimized. We first define a new task model using the salient computational characteristics of a class of adaptive signal processing applications. Based on this task model, we propose a new execution model. In the earlier linear pipelined execution model, the task mapping choices were restricted. The new model permits flexible task mapping choices, leading to improved throughput performance compared with the previous model. Using the new model, a three-step task mapping methodology is developed. It consists of (1) a data remapping step, (2) a coarse resource allocation step, and (3) a fine performance tuning step. The methodology is demonstrated by designing parallel algorithms for modern radar and sonar signal processing applications. These are implemented on IBM SP2 and Cray T3E, state-of-the-art HPC platforms, to show the effectiveness of our approach. Experimental results show significant performance improvement over those obtained by previous approaches. Our code is written using C and the Message Passing Interface (MPI). Thus, it is portable across various HPC platforms. Received April 8, 1998; revised February 2, 1999.     

Models and Scheduling Algorithms for Mixed Data and Task Parallel Programs

An increasing number of scientific programs exhibit two forms of parallelism, often in a nested fashion. At the outer level, the application comprises coarse-grained task parallelism, with dependencies between tasks reflected by an acyclic graph. At the inner level, each node of the graph is a data-parallel operation on arrays. Designers of languages, compilers, and runtime systems are building mechanisms to support such applications by providing processor groups and array remapping capabilities. In this paper we explore how to supplement these mechanisms with policy. What properties of an application, its data size, and the parallel machine determine the maximum potential gains from using both kinds of parallelism? It turns out that large gains can be expected only for specific task graph structures. For such applications, what are practical and effective ways to allocate processors to the nodes of the task graph? In principle one could solve the NP-complete problem of finding the best possible allocation of arbitrary processor subsets to nodes in the task graph. Instead of this, our analysis and simulations show that a simpleswitchedscheduling paradigm, which alternates between pure task and pure data parallelism, provides nearly optimal performance for the task graphs considered here. Furthermore, our scheme is much simpler to implement, has less overhead than the optimal allocation, and would be attractive even if the optimal allocation was free to compute. To evaluate switching in real applications, we implemented a switching task scheduler in the parallel numerical library ScaLAPACK and used it in a nonsymmetric eigenvalue program. Even for fairly large input sizes, the efficiency improves by factors of 1.5 on the Intel Paragon and 2.5 on the IBM SP-2. The remapping and scheduling overhead is negligible, between 0.5 and 5%.