动态负载均衡算法在校园网格中的应用

校园网格能有效消除信息孤岛,实现我国高校的计算资源和信息资源的有效共享。一个亟待解决的问题是在校园网格环境下,服务器节点响应能力低下。目前已提出多种技术与方案以解决并提高校园网格的服务器节点的响应能力,负载均衡技术就是一种全新的技术。本文根据校园网格的特点和影响负载均衡的因素,对基于校园网格的负载均衡技术进行了分析和探讨,并提出一种动态负载均衡算法。     

国家级地质灾害气象预警服务模型设计与应用

国家级地质灾害气象预警服务模型是基于空间信息网格理念的六层结构模型,强调数据和计算统一抽象为资源,强调数据资源迁移和数据处理分析资源迁移的一致性。建立基于节点平均吞吐率、处理器利用率和内存占用率的节点负载指数概念,4节点负载指数样本独立性检验结果表明,节点负载差异性不显著,达到均衡目的。预警服务应用实例表明,预警服务比桌面预警系统时间效率提高40%。     

校园网格的负载均衡算法研究

ChinaGrid支撑平台的开发成功,将消除信息孤岛,实现中国高校的计算资源和信息资源的有效共享。一个亟待解决的问题是在ChinaGrid环境下,基于校园网格的服务器集群响应能力低下。目前已提出多种技术与方案以解决并提高校园网格的服务器集群的响应能力,负载均衡技术就是一种全新的技术。文中根据校园网格的特点和影响负载均衡的因素,对基于校园网格的负载均衡技术进行了分析和探讨,并提出一种网格自调度负载均衡算法。     

遗传算法在网格任务调度的应用研究

研究了网格任务调度问题.针对传统任务调度算法在网格环境下存在不能很好地平衡节点负载和满足用户服务质量需求等缺点,导致网格系统负载极不均衡,调度效果低.为了提高网格任务调度的效果,提出一种基于遗传算法的网格任务调度方法.将网格任务编码成种群中的个体,网络任务目标作为遗传算法的适应度函数,通过遗传算法的强全局搜索及交叉、变异操作,获得最优的任务调度方案.仿真结果表明,采用遗传算法进行网格任务调度可以减少系统总执行时间和任务完成时间,提高了资源调度效率,使网格系统负载均衡度更好,在网格任务调度具有广泛的应用前景.     

校园网格的负载均衡算法研究

ChinaGrid支撑平台的开发成功，将消除信息孤岛，实现中国高校的计算资源和信息资源的有效共享。一个亟待解决的问题是在ChinaGrid环境下，基于校园网格的服务器集群响应能力低下。目前已提出多种技术与方案以解决并提高校园网格的服务器集群的响应能力，负载均衡技术就是一种全新的技术。文中根据校园网格的特点和影响负载均衡的因素，对基于校园网格的负载均衡技术进行了分析和探讨，并提出一种网格自调度负载均衡算法。     

基于群智能的P2P计算网格负载均衡算法

对等网络(P2P)计算网格是采用非集中控制的动态网络环境,在P2P网络环境的各个对等节点间均匀分配任务是网格计算的重要研究内容。传统C/S模式的负载均衡算法无法适用于分布式且动态变化的P2P网络。文章提出了一种基于群智能和多代理技术的P2P网络负载均衡算法,设计并实现了基于蚁群优化算法的分布式作业调度策略。仿真结果表明该算法是合理而有效的。     

基于Gold的气象中心网格平台资源管理系统构建及应用

在介绍成都区域气象中心网格计算平台基础上,为了保证资源的合理分配与网格节点负载均衡,构建了基于Gold的网格资源管理系统,并将其应用到成都区域中心网格计算平台中,通过典型应用实例分析结果表明,该系统功能丰富。大大提高了网格平台资源管理的效率。     

校园网格中VOD系统动态负载均衡算法研究

校园网格中的视频点播系统表现形式的直观性,自主性一直受到校园内师生的广泛关注,但视频服务器节点响应能力低下,视频点播中的缓冲和延迟而导致点播不能适应实时、连续稳定的视频流。通过分析视频点播系统面临的诸多问题,根据校园网格的特点和影响负载均衡的因素,对基于校园网格的负载均衡技术进行了分析,提出一种动态负载均衡算法,实现校园网络中视频资源的共享、均衡校园视频资源的负载。测试表明该算法可行、有效。     

计算网格中动态负载平衡的分布调度模式

网格计算下对资源进行有效的管理和调度可以提高系统的利用率.在对现有若干调度方法的研究和分析基础上,针对计算网格中的负载平衡问题,提出了一种分布式网格作业调度模型,并给出相关算法.算法通过建立主从模式的负载信息收集机制,提供给节点全局负载信息,加速重负载节点的负载转移速度.通过有效的负载平衡模式,解决资源调度中负载平衡及其可靠性问题.     

基于网格计算的自适应负载均衡策略研究

为了融合大量网络资源并有效地计算,解决网格计算中的负载均衡问题成为关键性的技术.论文提出了一种自适应负载均衡策略,采用了以静态为辅,动态自适应负载均衡算法为主的服务,可根据具体计算任务的情况,对任务重定向分配,提高了系统的伸缩性和响应时间,并采用基于CORBA体系机构的设计,在网格中间件层服务,灵活选择负载均衡算法,达到系统透明性.通过仿真模拟,证实了此策略的实用性和有效性.     

Static strategy and dynamic adjustment: An effective method for Grid task scheduling

Task scheduling is the key technology in Grid computing. Hierarchical organization is suitable for the computational Grid because of the dynamic, heterogeneous and autonomous nature of the Grid. Although a number of Grid systems adopt this organization, few of them has dealt with task scheduling for the hierarchical architecture. In this paper, we present an effective method, fully taking into account both historical Grid trade data and dynamic variation of the Grid market to improve the task scheduling for a hierarchical Grid market. The main idea of the proposed method is a combination of an off-line static strategy using time series prediction and an on-line dynamic adjustment using reinforcement learning. The superiority of this new scheduling algorithm, in improving the inquiry efficiency for resource consumers, getting better load balancing of the whole hierarchical Grid market, and achieving higher success rate of the Grid service request, is demonstrated by simulation experiments.     

基于网络断层扫描的网格网络性能测量分析

网格计算通过网络连接来获得一个高性能和高效的计算平台。网格网络的监测和性能测量为网格性能分析、负载平衡、任务调度等提供了重要的科学依据,而成为大规模网格服务的关键组件。现有的几种网格监测方法因缺乏对监测数据的推断分析而无法对网格网络的性能进行测量。通过对网格网络性能测量的特点、GloPerf及传统网络测量技术的分析,提出了基于网络断层扫描的网格网络性能测量方法。研究结果为网格网络性能的测量提供了新的途径。     

A hybrid load balancing policy underlying grid computing environment

In recent years, network bandwidth and quality has been drastically improved, even much faster than the enhancement of computer performance. The various communication and computing tasks in the fields such as telecommunication, multimedia, information technology, and construction simulation, can be integrated and applied in a distributed computing environment nowadays. However, as the demands of many researches for computing resources gradually grow, Grid Computing integrated with a distributed computing environment and the Internet (network) has gained more attention. The so-called Grid Computing is to utilize the idle computing resources (nodes) on the network to facilitate the execution of complicated tasks that require large-scale computing. In other words, the composition of Grid resources is dynamic and varies with time. Thus, when selecting nodes for executing a task, the dynamic of the nodes in the Grid must be considered, and to exploit the effectiveness of the resources, they have to be properly selected according to the properties of the task. This study proposed a hybrid load balancing policy which integrated static and dynamic load balancing technologies to assist in the selection for effective nodes. In addition, if any selected node can no longer provide resources, it can be promptly identified and replaced with a substitutive node to maintain the execution performance and the load balancing of the system.     

An optimization approach for decentralized QoS‐based scheduling based on utility and pricing in Grid computing

This paper presents an optimization approach for decentralized Quality of Service (QoS)‐based scheduling based on utility and pricing in Grid computing. The paper assumes that the quality dimensions can be easily formulated as utility functions to express quality preferences for each task agent. The utility values are calculated by the user‐supplied utility function that can be formulated with the task parameters. The QoS constraint Grid resource scheduling problem is formulated into a utility optimization problem. The QoS‐based Grid resource scheduling optimization is decomposed into two subproblems by applying the Lagrangian method. In the Grid, a Grid task agent acts as a consumer paying for the Grid resource and the resource providers receive profits from task agents. A pricing‐based QoS scheduling algorithm is used to perform optimally decentralized QoS‐based resource scheduling. The experiments investigate the effect of the QoS metrics on the global utility and compare the performance of the proposed algorithm with other economical Grid resource scheduling algorithms. Copyright © 2006 John Wiley & Sons, Ltd.     

网格资源超图划分聚类任务调度

网格计算是当前一个活跃的研究领域,其中任务调度是实现网格计算目标的一个重要部分.为获得良好的网格任务调度性能,提出了一种基于资源超图划分聚类的网格任务调度算法RHPC.该算法根据网格环境下资源数量庞大、异构、多样的特点,在构建的网格资源超图模型基础上,预先对资源进行性能划分聚类,将任务与聚类资源相匹配并实施调度.模拟实验结果证明算法缩短了任务资源相匹配的时间,提高了任务调度的性能,是一种有效的网格任务调度算法.     

QoS guided Min-Min heuristic for grid task scheduling

Task scheduling is an integrated component of computing.With the emergence of Grid and ubiquitous computing,new challenges appear in task scheduling based on properties such as security,quality of service,and lack of central control within distributed administrative domains.A Grid task scheduling framework must be able to deal with these issues.One of the goals of Grid task scheduling is to achivev high system throughput while matching applications with the available computing resources.This matching of resources in a non-deterministically shared heterogeneous environment leads to concerns over Quality of Service (QoS).In this paper a novel QoS guided task scheduling algorithm for Grid computing is introduced.The proposed novel algorithm is based on a general adaptive scheduling heuristics that includes QoS guidance.The algorithm is evaluated within a simulated Grid environment.The experimental results show that the nwe QoS guided Min-Min heuristic can lead to significant performance gain for a variety of applications.The approach is compared with others based on the quality of the prediction formulated by inaccurate information.     

一种基于深度强化学习与概率性能感知的边缘计算环境多工作流卸载方法

移动边缘计算是一种新兴的分布式和泛在计算模式,其将计算密集型和时延敏感型任务转移到附近的边缘服务器,有效缓解了移动终端资源不足的问题,显著减小了用户与计算处理节点之间的通信传输开销.然而,如果多个用户同时提出计算密集型任务请求,特别是流程化的工作流任务请求,边缘计算环境往往难以有效地进行响应,并会造成任务拥塞.另外,受...     

Efficient task replication and management for adaptive fault tolerance in Mobile Grid environments

Fault tolerant Grid computing is of vital importance as the Grid and Mobile computing worlds converge to the Mobile Grid computing paradigm. We present an efficient scheme based on task replication, which utilizes the Weibull reliability function for the Grid resources so as to estimate the number of replicas that are going to be scheduled in order to guarantee a specific fault tolerance level for the Grid environment. The additional workload that is produced by the replication is handled by a resource management scheme which is based on the knapsack formulation and which aims to maximize the utilization and profit of the Grid infrastructure. The proposed model has been evaluated through simulation and has shown its efficiency for being used in a middleware approach in future mobile Grid environments.     

面向Flink的负载均衡任务调度算法的研究与实现

Apache Flink是现在主流的大数据分布式计算引擎之一,其中任务调度问题是分布式计算系统中的关键问题。由于集群的异构性以及不同算子复杂度不同,大数据计算系统Flink中不可避免地会出现负载不均的情况,针对这种问题,提出了基于资源反馈的负载均衡任务调度算法RFTS。通过实时资源监控、区域划分和基于人工萤火虫优化的任务调度算法3个模块,把负载过重的机器中处于等待状态的任务分配给负载较轻的机器,来实现集群的负载均衡,提高系统集群利用率和执行效率。最后通过基于TPC-C和TPC-H数据集的实验结果表明,RFTS算法从执行时间和吞吐量2个方面有效提升了Apache Flink计算系统的性能。     

EMPEROR: An OGSA Grid Meta-Scheduler Based on Dynamic Resource Predictions

Scheduling constitutes an integral feature of Grid computing infrastructures, being also a key to realizing several of the Grid promises. In particular, scheduling can maximize the resources available to end users, accelerate the execution of jobs, while also supporting scalable and autonomic management of the resources comprising a Grid. Grid scheduling functionality hinges on middleware components called meta-schedulers, which undertake to automatically distribute jobs across the dispersed heterogeneous resources of a Grid. In this paper we present the design and implementation of a Grid meta-scheduler, which we call EMPEROR. EMPEROR provides a framework for implementing scheduling algorithms based on performance criteria. In implementing a particular instantiation of this framework, we have devised models for predicting host load and memory resources, and accordingly for estimating the running time of a task. These models hinge on time series analysis techniques and take into account results of the cluster computing literature. Apart from incorporating these models, EMPEROR provides fully fledged Grid scheduling functionality, which complies with OGSA standards as the later are reflected in the Globus toolkit. Specifically, EMPEROR interfaces to Globus middleware services (i.e., GSI, MDS, GRAM) towards discovering resources, implementing the scheduling algorithm and ultimately submitting jobs to local scheduling systems. By and large, EMPEROR is one of the few standards based meta-schedulers making use of dynamic scheduling information.