异构分布系统的实时任务轮转式容错调度算法

建立了一个异构分布式系统实时调度模型,对异构分布式系统中的任务及不同处理机资源进行了形式化描述.结合基版本/副版本技术,给出了用于异构分布式系统的实时任务轮转式容错调度算法.实例分析表明,该算法有效提高了异构处理机环境下的资源利用率以及整体计算性能.     

异构集群系统的可分负载多轮调度算法

针对更实际的异构集群计算环境,充分考虑处理机具有不同的计算速度、通信能力和存储容量的特性,通过允许计算和通信操作重叠执行,采取多次并行分配计算任务的方法,设计一种可分负载多轮调度算法。实验结果表明,该算法不但能获得与均匀多轮调度(UMR)算法相当的渐近最优调度时间长度,并且能够处理更大规模的应用负载,实用性更强。     

Distributed adaptive formation tracking for heterogeneous multiagent systems with multiple nonidentical leaders and without well‐informed follower

This paper studies the time‐varying output formation tracking (OFT) problems for linear heterogeneous multiagent systems with multiple leaders, where both the followers and the leaders can have nonidentical dynamics and dimensions. The existing results on formation tracking with multiple leaders depend on the assumption that each follower is well‐informed or uninformed, where the well‐informed follower has all the leaders as its neighbor. To remove this assumption, a novel OFT approach is presented using a distributed observer scheme. Firstly, based on the local estimation and the interaction with neighboring followers, a fully distributed observer is designed for each follower to estimate the dynamical matrices and the states of multiple leaders without requiring the well‐informed follower assumption. The convergence of the distributed observer is proved by using Lyapunov theory. Then, an adaptive algorithm is proposed to solve the regulator equations in finite time based on the estimation of the leaders' dynamical matrices. Furthermore, the desired time‐varying output formation of each follower is generated by a local active exosystem. A time‐varying OFT protocol is presented using the estimated states of multiple leaders, the online solutions of the regulator equations, and the desired formation vector generated by the local exosystem. It is proved that the outputs of the followers can not only realize the expected formation shape but also track the predefined convex combination of multiple leaders. Finally, a simulation example is given to verify the theoretical results.     

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。     

Distributed iterative command governor schemes for interconnected linear systems

A novel distributed command governor (CG) supervision strategy relying on iterative optimization procedure is presented for multi‐agent interconnected linear systems subject to pointwise‐in‐time set‐membership coordination constraints. Unlike non‐iterative distributed CG schemes, here all agents undertake several optimization iterations and data exchange before arriving to the optimal solution. As a result, these methods are able to achieve Pareto‐optimal solutions not only in steady‐state conditions as the ones based on non‐iterative optimization procedures but also during transients and are not hampered by the presence of undesirable Nash‐equilibria or deadlock situations. The main properties of the method are fully investigated and in particular its optimality, stability, and feasibility properties rigorously proved. A final example is presented where the proposed distributed solution is contrasted with existing centralized and distributed non‐iterative CG solutions. Copyright © 2017 John Wiley & Sons, Ltd.     

Design and implementation of an adaptive job allocation strategy for heterogeneous multi‐cluster computing systems

Cluster computing is an attractive approach to provide high‐performance computing for solving large‐scale applications. Owing to the advances in processor and networking technology, expanding clusters have resulted in the system heterogeneity; thus, it is crucial to dispatch jobs to heterogeneous computing resources for better resource utilization. In this paper, we propose a new job allocation system for heterogeneous multi‐cluster environments named the Adaptive Job Allocation Strategy (AJAS), in which a self‐scheduling scheme is applied in the scheduler to dispatch jobs to the most appropriate computing resources. Our strategy focuses on increasing resource utility by dispatching jobs to computing nodes with similar performance capacities. By doing so, execution times among all nodes can be equalized. The experimental results show that AJAS can improve the system performance. Copyright © 2011 John Wiley & Sons, Ltd.     

List scheduling with duplication for heterogeneous computing systems

Effective task scheduling is essential for obtaining high performance in heterogeneous computing systems (HCS). However, finding an effective task schedule in HCS, requires the consideration of the heterogeneity of computation and communication. To solve this problem, we present a list scheduling algorithm, called Heterogeneous Earliest Finish with Duplicator (HEFD). As task priority is a key attribute for list scheduling algorithm, this paper presents a new approach for computing their priority which considers the performance difference in target HCS using variance. Another novel idea proposed in this paper is to try to duplicate all parent tasks and get an optimal scheduling solution. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithm HEFD significantly surpasses other three well-known algorithms.     

Job scheduling in multiprogrammed computer systems

The job-scheduling function in a multiprogramming computer system plays a key role in the achievement of the performance goals for the system. It is possible and convenient to partition this scheduling function into a priority assignment function and resource assignment function. Implementation of a general purpose resource assignment module permits a large family of scheduling strategies to be implemented via different priority calculation schemes. The implications of this partitioning of the scheduling function are studied by use of a simulation model. A system embodying this approach to job scheduling is discussed as an application of the approach to other types of systems.     

MFHS: A modular scheduling framework for heterogeneous system

Current innovative distributed architectures, proposing on-line services, involve more and more computing resources. From a provider point of view, the platform management leads to challenging problematic relating to resource allocation, which involve different kind of quality of service parameters, the provider has to focus on to keep his platform reliable and efficient. MFHS is a modular generic framework, which can be adapted to any distributed computing environment. Structured in modules, MFHS allows to discover the existing computing resources in terms of computing performance, network throughput and disk I/O speeds (Resources Discovery module) and to predict how the experiment should behave (P_i value). As the setting up of real experiments is often complex, MFHS allows: to make theoretical experimentation (based on models), to use any kind of distributed emulators, or to deploy experiments on real-experimental platforms. In this article, these three environments are used to highlight the reliability of MFHS (measured P_i=90% against 94% for the predicted P_i). Deployment and scheduling studies have also been achieved using an experimental Cloud based on OpenStack while Emulab test-bed has been used as emulator. During experiments, four QoS parameters are taken into account (Resources Monitoring module): energy consumption, cost, resource utilization, and makespan. These studies also includes a new heuristic called MMin, based on Max-Min and Min-Min algorithms. Experimentation section, proposes a detailed comparative analysis of these algorithms in terms of QoS results, while the abilities of the proposed heuristic MMin regarding the makespan metric is shown.     

一种新的异构网格任务调度算法

提出了一种基于免疫计算的异构网格任务调度算法。设计了异构网格独立任务调度问题的数学模型,给出了免疫调度算法的框架、基于实数编码的克隆变异算子和浓度抑制算子,并在仿真环境下进行了实验。实验结果表明,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。     

Efficient and scalable scheduling for performance heterogeneous multicore systems

Performance heterogeneous multicore processors (HMP for brevity) consisting of multiple cores with the same instruction set but different performance characteristics (e.g., clock speed, issue width), are of great concern since they are able to deliver higher performance per watt and area for programs with diverse architectural requirements than comparable homogeneous ones. However, such power and area efficiencies of performance heterogeneous multicore systems can only be achieved when workloads are matched with cores according to both the properties of the workload and the features of the cores.     

Energy‐efficient scheduling algorithms for batch‐of‐tasks (BoT) applications on heterogeneous computing systems

One of the major design constraints of a heterogeneous computing system is optimal scheduling, that is, mapping of tasks on the processing nodes in order to optimize the QoS parameters. Because of the huge energy consumption by computing resources, negative environmental effects and reduced system reliability, energy has unavoidably been added as a new parameter to the list of QoS parameters. Energy optimization in scheduling strategies along with makespan makes it an even more challenging combinatorial optimization problem. This work proposes two energy‐aware scheduling algorithms G1 and G2 to schedule a batch‐of‐tasks, made of a collection of independent tasks, on heterogeneous processors in order to minimize the makespan and the energy consumption. The proposed algorithms schedule tasks based on weighted aggregation cost function to the appropriate processors followed by task migration phase designed to further minimize the makespan and the energy consumption. The study evaluates the performance of the proposed algorithms with some of the peers, that is, MinMin, MINSuff on account of makespan, energy consumption, flowtime, and utilization. An experimental study reveals that the proposed algorithm (G2) consistently performs better under various test conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

A scalable approach to solving dense linear algebra problems on hybrid CPU‐GPU systems

Aiming to fully exploit the computing power of all CPUs and all graphics processing units (GPUs) on hybrid CPU‐GPU systems to solve dense linear algebra problems, we design a class of heterogeneous tile algorithms to maximize the degree of parallelism, to minimize the communication volume, and to accommodate the heterogeneity between CPUs and GPUs. The new heterogeneous tile algorithms are executed upon our decentralized dynamic scheduling runtime system, which schedules a task graph dynamically and transfers data between compute nodes automatically. The runtime system uses a new distributed task assignment protocol to solve data dependencies between tasks without any coordination between processing units. By overlapping computation and communication through dynamic scheduling, we are able to attain scalable performance for the double‐precision Cholesky factorization and QR factorization. Our approach demonstrates a performance comparable to Intel MKL on shared‐memory multicore systems and better performance than both vendor (e.g., Intel MKL) and open source libraries (e.g., StarPU) in the following three environments: heterogeneous clusters with GPUs, conventional clusters without GPUs, and shared‐memory systems with multiple GPUs. Copyright © 2014 John Wiley & Sons, Ltd.     

An efficient weighted bi-objective scheduling algorithm for heterogeneous systems

This paper proposes the Makespan and Reliability Cost Driven (MRCD) heuristic, a static scheduling strategy for heterogeneous distributed systems that not only minimizes the makespan, but also maximizes the reliability of the application. The MRCD scheduling decisions are guided by a weighted function that considers both objectives simultaneously, instead of prioritizing one of them. This work also introduces a classification of the solutions produced by weighted bi-objective schedulers to aid users to tune the weighting function such that an appropriate solution can be selected in accordance with their needs. In comparison with the related work, MRCD produced schedules with makespans that were significantly better then those produced by the other strategies at expense of an insignificant deterioration in reliability.     

一种基于负载均衡异构分布式系统的改进容错调度算法*

基于基/副版本技术提出了一种具有容错功能的静态进程调度算法。给出了一个新的设计模型,并在该模型上提出HDAL算法。此前类似负载均衡容错调度算法都是通过排序来解决故障发生前后的负载均衡调度问题。该算法与以往算法不同之处就是在不依赖排序情况下,通过引进控制进程来解决负载均衡调度问题,并且该算法的负载均衡性在一定程度上具有了可控性。最后通过模拟实验得到以下有意义的结论：在业务繁忙的异构系统中,HDAL算法比以往算法资源利用率高,负载均衡性更好,并且在调度速度上优势明显。     

Reliability-aware scheduling strategy for heterogeneous distributed computing systems

Heterogeneous computing systems are promising computing platforms, since single parallel architecture based systems may not be sufficient to exploit the available parallelism with the running applications. In some cases, heterogeneous distributed computing (HDC) systems can achieve higher performance with lower cost than single-machine supersystems. However, in HDC systems, processors and networks are not failure free and any kind of failure may be critical to the running applications. One way of dealing with such failures is to employ a reliable scheduling algorithm. Unfortunately, most existing scheduling algorithms for precedence constrained tasks in HDC systems do not adequately consider reliability requirements of inter-dependent tasks. In this paper, we design a reliability-driven scheduling architecture that can effectively measure system reliability, based on an optimal reliability communication path search algorithm, and then we introduce reliability priority rank (RRank) to estimate the task’s priority by considering reliability overheads. Furthermore, based on directed acyclic graph (DAG) we propose a reliability-aware scheduling algorithm for precedence constrained tasks, which can achieve high quality of reliability for applications. The comparison studies, based on both randomly generated graphs and the graphs of some real applications, show that our scheduling algorithm outperforms the existing scheduling algorithms in terms of makespan, scheduling length ratio, and reliability. At the same time, the improvement gained by our algorithm increases as the data communication among tasks increases.     

HARTEX—a safe real‐time kernel for distributed computer control systems

A hard real‐time kernel is presented for distributed computer control systems (DCCS), highlighting a number of novel features, such as integrated scheduling of hard and soft real‐time tasks as well as tasks and resources; high‐performance time management supporting safe DCCS operation in a hard real‐time environment; synchronization and communication featuring event notification via vector semaphores and transparent communication through implicit (content‐oriented) message addressing. Conventional queues have been substituted by Boolean vectors and vector processing techniques throughout the kernel, resulting in efficient and highly deterministic behaviour, which is characterized by very low overhead and constant execution time of kernel operations, independent of the number of tasks involved. Copyright © 2001 John Wiley & Sons, Ltd.     

A hybrid heuristic–genetic algorithm for task scheduling in heterogeneous processor networks

Efficient task scheduling on heterogeneous distributed computing systems (HeDCSs) requires the consideration of the heterogeneity of processors and the inter-processor communication. This paper presents a two-phase algorithm, called H2GS, for task scheduling on HeDCSs. The first phase implements a heuristic list-based algorithm, called LDCP, to generate a high quality schedule. In the second phase, the LDCP-generated schedule is injected into the initial population of a customized genetic algorithm, called GAS, which proceeds to evolve shorter schedules. GAS employs a simple genome composed of a two-dimensional chromosome. A mapping procedure is developed which maps every possible genome to a valid schedule. Moreover, GAS uses customized operators that are designed for the scheduling problem to enable an efficient stochastic search. The performance of each phase of H2GS is compared to two leading scheduling algorithms, and H2GS outperforms both algorithms. The improvement in performance obtained by H2GS increases as the inter-task communication cost increases.