基于MAS协作机制的动态计算资源优化调度模型

计算网格资源调度的目标是提高网格资源的利用率、改善网格应用的性能,它是网格中需着力解决的问题之一.目前,围绕着网格中的资源调度方法,虽已提出了多种调度算法,但是都不能很好地适应网格环境下的自治性、动态性、分布性和异构性等特征.针对上述问题,文中运用MAS协同技术和市场演化博弈机制,建立了一个动态计算资源优化调度模型和演化博弈算法,构建了消费者效用函数,讨论了资源请求博弈中Nash均衡点的存在性和唯一性以及Nash均衡解,分析了模型的性质.实验结果表明,资源调度模型不但可以有效减少不必要的延迟,而且在响应时间的平滑性、吞吐率及资源利用率方面比传统方法要好,从而可以达到优化系统效率和提高用户满意度的目标.     

基于推荐机制的网格资源匹配算法研究

针对网格计算环境下,参与计算用户和计算资源规模日益庞大,用户申请资源过程中所需的资源匹配过程逐步复杂化和大规模化,提出了一种基于推荐机制的网格资源匹配算法.以往的网格计算资源的匹配和调度算法需要在调度计算时遍历所有网格资源,而改进的基于SVD(奇异值分解)的协同过滤算法考虑了用户行为相关性和资源使用频度的相关性,通过用户对资源项的使用历史记录建立用户对资源的满意度评分体系,利用推荐机制给出用户推荐资源集以到达资源匹配的效果.从一个新的角度给出了解决大量资源匹配的方法.     

云计算与网格计算的比较研究

网格计算是一种能够整合零散资源并实现资源共享和协同工作的计算模式;云计算是网格计算、并行计算、分布式计算的发展,是一种新兴的商业计算模式。它具有与网格计算不同的新的特点。该文在研究网格计算与云计算概念的基础上从体系结构、专注方向、资源管理、作业调度等多种角度对网格计算与云计算进行了分析和研究。云计算所采用的商业理念、成熟的资源虚拟化技术以及非标准化的规范,使其体系结构、资源管理、作业调度等方面呈现出了不同的特点,也更适宜于为用户提供按需服务的目标,但在安全方面仍需不断完善。     

网格的虚拟组织资源管理与工作流调度模型

资源调度是网格计算的重要内容。利用虚拟组织管理领域相关的网格资源,利用工作流技术组织网格任务,可以有效的降低网格调度问题的复杂性。本文提出一种调度模型,分别在工作流引擎和虚拟组织两个层次实施调度,以协调网格用户和服务提供者的不同利益,提高网格系统的性能、服务质量和易用性。     

基于遗传编程的网格资源调度算法

网格资源调度是一个非常重要的研究课题。由于因特网的开放、动态性,传统的资源调度和分配方法已经不再适用网格计算,基于经济模型的资源管理和调度成为研究热点。在计算市场模型中,构造有效的效益函数又是提高算法性能的关键。有关文献中采用的是线性效益函数,虽然降低了复杂度,但不能很好地反映用户的效益。文中提出了基于遗传编程来寻找和构造非线性效益函数的方法,并将其应用到网格调度算法中。实验结果表明该算法可以提高网格中的资源调度性能。     

基于遗传编程的网格资源调度算法

网格资源调度是一个非常重要的研究课题.由于因特网的开放、动态性,传统的资源调度和分配方法已经不再适用网格计算,基于经济模型的资源管理和调度成为研究热点.在计算市场模型中,构造有效的效益函数又是提高算法性能的关键.有关文献中采用的是线性效益函数,虽然降低了复杂度,但不能很好地反映用户的效益.文中提出了基于遗传编程来寻找和构造非线性效益函数的方法,并将其应用到网格调度算法中.实验结果表明该算法可以提高网格中的资源调度性能.     

网格计算中工作流调度的非合作博弈优化*

在异构网络计算问题中,网格计算方法通过引入资源共享机制,可解决复杂的计算任务。然而在网格环境中,需要对网络可获得的资源进行合理调度和协调,才可以获得良好的网络工作流,以及合适的网络性能和网络响应时间。为了提高网格计算方法的任务调度和资源分配的能力和性能,提出了一种基于非合作博弈方式的博弈模型。该模型通过设定使用户的资源分配所需时间和代价降低的解来增加代理的利润,激励资源代理使用一种优化调度算法,使资源调度的时间和代价都最小。仿真结果表明了该模型的可行性和适用性,并且基于该模型的遗传算法是最好的资源调度算法。     

网格技术在半导体生产调度中的应用研究

网格技术是一种新型的分布计算技术,致力于解决复杂度很高的新应用问题.随着全球半导体生产规模的日益扩大,半导体生产线的优化调度问题成为学术界及工程界研究的热点.半导体生产线具有许多特殊的特点,诸如生产规模大、工件数量多、随机性大、加工成本高、高度的可重入性等,这些特点决定了原有的调度策略已不能满足半导体生产线的要求.鉴于网格技术在处理设备可扩展性和资源平衡性上的优势,主要研究将网格技术的思想用于半导体生产线的调度中.利用网格计算中的负载向量和失衡因子的概念,来控制半导体生产线上各加工机器处工件块的规模以及投料规模.通过优化算法的调度,使得半导体生产线的各加工设备负载得到平衡,设备的生产效率提高,缩短加工周期,从而达到优化生产线的目的.     

一个基于证券市场的计算网格环境下的资源分配模型

计算网格(Computational Grid)下对资源进行有效管理和调度是十分具有挑战性的问题.论文中提出了一个证券市场模型来实现计算网格环境下的资源分配.在此模式中,资源作为一种证券商品被自由买卖,用户可以方便快捷地实现对系统资源的存取,模拟实验表明证券市场模型是实现计算网格环境下资源分配的一个有效手段.     

基于经济模型的资源代理

网格计算是为解决大规模资源密集型问题而提出的新一代计算平台,是当前并行和分布处理技术的一个发展方向,而资源管理是计算网格的关键技术之一。对各种各样可利用资源的整合和管理是网格应用的基础,而资源的分布性、动态性、异构性、自治性和需要协调一致性使得网格资源的管理调度成为一个棘手的问题。目前基于市场的经济资源管理和调度算法非常适合计算网格中的资源管理问题,但有调度价格不能更改、负载平衡等问题。文中提出了“网格环境下基于经济模型的资源代理”,依靠多维QoS指导的调度策略和经济模型的启发式调节资源价格,改进和优化计算网格资源的分配。     

基于进化算法的网格计算资源管理调度系统

网格计算是下一代互联网的应用模式，资源管理是网格技术研究的核心任务之一，包含资源发现、任务调度和负载均衡。提出了种基于Agent的网格资源管理调度层次模型，并且采用了进化算法作为调度策略，满足了网格对调度系统可扩展性和全局最优调度的需求。     

A novel approach to resource scheduling for parallel query processing on computational grids

Advances in network technologies and the emergence of Grid computing have both increased the need and provided the infrastructure for computation and data intensive applications to run over collections of heterogeneous and autonomous nodes. In the context of database query processing, existing parallelisation techniques cannot operate well in Grid environments because the way they select machines and allocate tasks compromises partitioned parallelism. The main contribution of this paper is the proposal of a low-complexity, practical resource selection and scheduling algorithm that enables queries to employ partitioned parallelism, in order to achieve better performance in a Grid setting. The evaluation results show that the scheduler proposed outperforms current techniques without sacrificing the efficiency of resource utilisation. Recommended by: Ioannis Vlahavas     

Multi-criteria and satisfaction oriented scheduling for hybrid distributed computing infrastructures

Assembling and simultaneously using different types of distributed computing infrastructures (DCI) like Grids and Clouds is an increasingly common situation. Because infrastructures are characterized by different attributes such as price, performance, trust, and greenness, the task scheduling problem becomes more complex and challenging. In this paper we present the design for a fault-tolerant and trust-aware scheduler, which allows to execute Bag-of-Tasks applications on elastic and hybrid DCI, following user-defined scheduling strategies. Our approach, named Promethee scheduler, combines a pull-based scheduler with multi-criteria Promethee decision making algorithm. Because multi-criteria scheduling leads to the multiplication of the possible scheduling strategies, we propose SOFT, a methodology that allows to find the optimal scheduling strategies given a set of application requirements. The validation of this method is performed with a simulator that fully implements the Promethee scheduler and recreates an hybrid DCI environment including Internet Desktop Grid, Cloud and Best Effort Grid based on real failure traces. A set of experiments shows that the Promethee scheduler is able to maximize user satisfaction expressed accordingly to three distinct criteria: price, expected completion time and trust, while maximizing the infrastructure useful employment from the resources owner point of view. Finally, we present an optimization which bounds the computation time of the Promethee algorithm, making realistic the possible integration of the scheduler to a wide range of resource management software.     

Implementing and evaluating scheduling policies in gLite middleware

Grid scheduling algorithms are usually implemented in a simulation environment using tools that hide the complexity of the Grid and assumptions that are not always realistic. In our work, we describe the steps followed, the difficulties encountered and the solutions provided to develop and evaluate a scheduling policy, initially implemented in a simulation environment, in the gLite Grid middleware. Our focus is on a scheduling algorithm that allocates in a fair way the available resources among the requested users or jobs. During the actual implementation of this algorithm in gLite, we observed that the validity of the information used by the scheduler for its decisions affects greatly its performance. To improve the accuracy of this information, we developed an internal feedback mechanism that operates along with the scheduling algorithm. Also, a Grid computation resource cannot be shared concurrently between different users or jobs, making it difficult to provide actual fairness. For this reason we investigated the use of virtualization technology in the gLite middleware. We did a proof‐of‐concept implementation and performed an experimental evaluation of our scheduling algorithm in a small gLite testbed that proves the validity and applicability of our solutions. Copyright © 2012 John Wiley & Sons, Ltd.     

Data Intensive and Network Aware (DIANA) Grid Scheduling

In Grids scheduling decisions are often made on the basis of jobs being either data or computation intensive: in data intensive situations jobs may be pushed to the data and in computation intensive situations data may be pulled to the jobs. This kind of scheduling, in which there is no consideration of network characteristics, can lead to performance degradation in a Grid environment and may result in large processing queues and job execution delays due to site overloads. In this paper we describe a Data Intensive and Network Aware (DIANA) meta-scheduling approach, which takes into account data, processing power and network characteristics when making scheduling decisions across multiple sites. Through a practical implementation on a Grid testbed, we demonstrate that queue and execution times of data-intensive jobs can be significantly improved when we introduce our proposed DIANA scheduler. The basic scheduling decisions are dictated by a weighting factor for each potential target location which is a calculated function of network characteristics, processing cycles and data location and size. The job scheduler provides a global ranking of the computing resources and then selects an optimal one on the basis of this overall access and execution cost. The DIANA approach considers the Grid as a combination of active network elements and takes network characteristics as a first class criterion in the scheduling decision matrix along with computations and data. The scheduler can then make informed decisions by taking into account the changing state of the network, locality and size of the data and the pool of available processing cycles.     

Integration of task abortion and security requirements in GA-based meta-heuristics for independent batch grid scheduling

Scheduling and resource allocation in large scale distributed environments, such as Computational Grids (CGs), arise new requirements and challenges not considered in traditional distributed computing environments. Among these new requirements, task abortion and security become needful criteria for Grid schedulers. The former arises due to the dynamics of the Grid systems, in which resources are expected to enter and leave the system in an unpredictable way. The latter requirement appears crucial in Grid systems mainly due to a multi-domain nature of CGs. The main aim of this paper is to develop a scheduling model that enables the aggregation of task abortion and security requirements as additional, together with makespan and flowtime, scheduling criteria into a cumulative objective function. We demonstrate the high effectiveness of genetic-based schedulers in finding near-optimal solutions for multi-objective scheduling problem, where all criteria (objectives) are simultaneously optimized. The proposed meta-heuristics are experimentally evaluated in static and dynamic Grid scenarios by using a Grid simulator. The obtained results show the fast reduction of the values of basic scheduler performance metrics, especially in the dynamic case, that confirms the usefulness of the proposed approach in real-life scenarios.     

A hierarchical reliability-driven scheduling algorithm in grid systems

In a Grid computing system, many distributed scientific and engineering applications often require multi-institutional collaboration, large-scale resource sharing, wide-area communication, etc. Applications executing in such systems inevitably encounter different types of failures such as hardware failure, program failure, and storage failure. One way of taking failures into account is to employ a reliable scheduling algorithm. However, most existing Grid scheduling algorithms do not adequately consider the reliability requirements of an application. In recognition of this problem, we design a hierarchical reliability-driven scheduling architecture that includes both a local scheduler and a global scheduler. The local scheduler aims to effectively measure task reliability of an application in a Grid virtual node and incorporate the precedence constrained tasks’ reliability overhead into a heuristic scheduling algorithm. In the global scheduler, we propose a hierarchical reliability-driven scheduling algorithm based on quantitative evaluation of independent application reliability. Our experiments, based on both randomly generated graphs and the graphs of some real applications, show that our hierarchical scheduling algorithm performs much better than the existing scheduling algorithms in terms of system reliability, schedule length, and speedup.     

Integrating Computing Resources on Multiple Grid-Enabled Job Scheduling Systems Through a Grid RPC System

We present a framework for a parallel programming model by remote procedure calls, which bridge large-scale computing resource pools managed by multiple Grid-enabled job scheduling systems. With this system, the user can exploit not only remote servers and clusters, but also the computing resources provided by Grid-enabled job scheduling systems located on different sites. This framework requires a Grid remote procedure call (RPC) system to decouple the computation in a remote node from the Grid RPC mechanism and uses document-based communication rather than connection-based communication. We implemented the proposed framework as an extension of the OmniRPC system, which is a Grid RPC system for parallel programming. We designed a general interface to easily adapt the OmniRPC system to various Grid-enabled job scheduling systems, including XtremWeb, CyberGRIP, Condor and Grid Engine. We show the preliminary performance of these implementations using a phylogenetic application. We found that the proposed system can achieve approximately the same performance as OmniRPC and can handle interruptions in worker programs on remote nodes. Yoshihiro Nakajima is a Research Fellow of the Japan Society for the Promotion of Science     

适用于应用模式的网格结点选择方法研究

网格环境中的任务调度面临着海量的计算和通信资源环境,所以调度者需要考虑资源的选择问题.传统的资源选择方法一般只考虑计算能力或通信能力的最大化,没有考虑资源的通信模式与应用的匹配问题.本文在Remos研究的基础上,提出了一个完整的基于应用通信模式的网格结点选择算法.算法使用子图同构的辨识方法来判断网格结点的连通关系是否与应用模式相匹配,在满足通信模式约束的前提下,再用贪婪算法来选择计算和通信均较优化的结点.在通用算法的基础上,特别考虑了基于Master-Slave和All-to-All应用模式简化结点选择算法.最后,利用仿真方法,将本文的算法和随机选择法进行了比较,结果表明本文算法选择的网格结点不但满足应用的通信约束,而且性能较优.     

A dynamic rescheduling algorithm for resource management in large scale dependable distributed systems

Scheduling is a key component for performance guarantees in the case of distributed applications running in large scale heterogeneous environments. Another function of the scheduler in such system is the implementation of resilience mechanisms to cope with possible faults. In this case resilience is best approached using dedicated rescheduling mechanisms. The performance of rescheduling is very important in the context of large scale distributed systems and dynamic behavior. The paper proposes a generic rescheduling algorithm. The algorithm can use a wide variety of scheduling heuristics that can be selected by users in advance, depending on the system’s structure. The rescheduling component is designed as a middleware service that aims to increase the dependability of large scale distributed systems. The system was evaluated in a real-world implementation for a Grid system. The proposed approach supports fault tolerance and offers an improved mechanism for resource management. The evaluation of the proposed rescheduling algorithm was performed using modeling and simulation. We present experimental results confirming the performance and capabilities of the proposed rescheduling algorithm.