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.     

一种支持服务网格的动态负载平衡系统

为了实现服务网格系统内负载的均衡分布,提高资源利用率和系统的吞吐率,设计并实现了一种基于服务网格环境的动态负载平衡系统。提出了层次式负载平衡调度模式,给出了本系统结构形式,设计并实现了一种综合考虑各局部代理作业数和各个局部代理性能以及当前的负载情况的动态双阈值作业分配算法。实验结果表明,此算法能有效地基于负载分派作业,达到了提高网格内分布资源的利用率和减少作业调度时间的目的。     

上海网格中间件中基于负载平衡的调度框架

在网格环境中,实现动态的负载平衡在服务调度中起到了非常关键的作用。然而,网格环境中的资源的动态性决定了它难以被监控,自治性又决定了它难以被集中管理。为在网格中间件中实现服务调度的负载平衡,提供了一些具有参考价值的解决方案。介绍设计和实现调度框架的动机以及需要解决的问题,并深入探讨在上海网格中间件中设计和实现调度框架的思路,以及达到动态负载平衡的具体方案。     

网格环境下负载平衡研究

随着Internet上网络应用和网格技术的飞速发展,负载平衡的研究和发展面临着新的机遇和挑战。这些新的技术、概念和方法提供了更新的环境来研究和发展负载平衡系统;结合网格和负载平衡两种技术,依据开放网格服务体系、网格数据服务接口模型和网格服务资源框架,研究了在网格环境下的负载平衡实施,提出了一个负载平衡系统的体系结构模型。针对负载平衡中的两个关键问题——信息分发收集过程和有效的负载估计算法做了详细的论述。     

基于改进蚂蚁算法的网格任务调度策略研究

介绍了蚂蚁算法,并进一步将这种新型的生物优化思想进行扩展,应用于网格系统中的任务调度问题。通过增加负载平衡因子,将用户提交的任务合理地映射到相对空闲的资源上去,经仿真平台实验,可有效地实现任务的合理调度和网格系统的负载平衡。     

网格环境中调度优越性测评及其算法

任务分配与调度是网格计算中的核心问题之一,通过建立任务调度测评模型体系,结合调度的通信代价、处理器利用率和负载平衡提出了一种对调度策略优越性的测评算法。对算法进行了一些问题的定义,提出了联系概率、处理器满足率和负载压强差等概念,对算法的结构及步骤进行了说明,通过用数学计算对不同的分配方法进行了计算,并对计算结果进行了分析,给出了分析的结论。算法取消了一系列理想化的假设,有一定的现实意义。     

改进蚂蚁算法在网格容错调度中的研究

本文提出了一种改进的蚂蚁算法,并将其应用到网格容错调度模型中,通过选取合适的参数,并增加负载平衡因子,实现了即使在个别节点出现故障的情况下也能顺利完成任务,并能有效地提高资源的利用率.     

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。     

一个网格环境下基于分块的混合型自调度算法

针对当前已有自调度算法在网格环境中存在负载不平衡、参数值难以确定的问题,结合网格自身的特点提出一种新的基于分块的混合型自调度算法CHSS。该算法吸取了可以人为确定块大小及根据性能值分配的思想,提出在分配的每一阶段都考虑子节点性能的方法。实验证明,该算法比现有自调度算法在总计算时间上有了大大的缩短并且能取得更好的负载平衡。     

用户QOS及系统指标指导的计算网格任务调度

针对网格环境中的动态性特点,特别是用户服务质量（QoS）需求的动态变化性,通过定义任务的效益函数来评估任务的多维QoS需求。同时为了兼顾任务完成时间、负载平衡等系统指标,引入系统效益的概念,给出负载平衡度的定义用来指导调度及评价调度性能。针对一组具有QoS需求的相互独立的计算任务提出一种用户QoS及系统指标指导的计算网格任务调度算法——UQSI。模拟实验结果显示,该算法能较好地满足用户的多维QoS需求,更加适合开放复杂的网格环境。     

网格原子化作业的二分图调度方法

对于网格系统中计算力调度等问题,结合有向无环作业图DATG和无向节点图UNG,采用并行集APS建立了一种基于二分图的网格调度算法BGS,并在惩罚策略、负载均衡、复活机制的引导下,使系统的调度动态地逐步趋向优化.实验结果表明:该算法能够更加适应网格资源的变化,降低作业负载,提高作业的并行化程度,并能根据系统负载合理地利用节点资源.     

Task scheduling using NSGA II with fuzzy adaptive operators for computational grids

Scheduling algorithms have an essential role in computational grids for managing jobs, and assigning them to appropriate resources. An efficient task scheduling algorithm can achieve minimum execution time and maximum resource utilization by providing the load balance between resources in the grid. The superiority of genetic algorithm in the scheduling of tasks has been proven in the literature. In this paper, we improve the famous multi-objective genetic algorithm known as NSGA-II using fuzzy operators to improve quality and performance of task scheduling in the market-based grid environment. Load balancing, Makespan and Price are three important objectives for multi-objective optimization in the task scheduling problem in the grid. Grid users do not attend load balancing in making decision, so it is desirable that all solutions have good load balancing. Thus to decrease computation and ease decision making through the users, we should consider and improve the load balancing problem in the task scheduling indirectly using the fuzzy system without implementing the third objective function. We have used fuzzy operators for this purpose and more quality and variety in Pareto-optimal solutions. Three functions are defined to generate inputs for fuzzy systems. Variance of costs, variance of frequency of involved resources in scheduling and variance of genes values are used to determine probabilities of crossover and mutation intelligently. Variance of frequency of involved resources with cooperation of Makespan objective satisfies load balancing objective indirectly. Variance of genes values and variance of costs are used in the mutation fuzzy system to improve diversity and quality of Pareto optimal front. Our method conducts the algorithm towards best and most appropriate solutions with load balancing in less iteration. The obtained results have proved that our innovative algorithm converges to Pareto-optimal solutions faster and with more quality.     

A hybrid policy for fault tolerant load balancing in grid computing environments

Due to the emergence of grid computing over the Internet, there is a need for a hybrid load balancing algorithm which takes into account the various characteristics of the grid computing environment. Hence, this research proposes a fault tolerant hybrid load balancing strategy namely AlgHybrid_LB, which takes into account grid architecture, computer heterogeneity, communication delay, network bandwidth, resource availability, resource unpredictability and job characteristics. AlgHybrid_LB juxtaposes the strong points of neighbor-based and cluster based load balancing algorithms. Our main objective is to arrive at job assignments that could achieve minimum response time and optimal computing node utilization. Major achievements include low complexity of proposed approach and drastic reduction of number of additional communications induced due to load balancing. A simulation of the proposed approach using Grid Simulation Toolkit (GridSim) is conducted. Experimental results show that the proposed algorithm performs very well in a large grid environment.     

A Heuristic Algorithm for Task Scheduling Based on Mean Load on Grid

Efficient task scheduling is critical to achieving high performance on grid computing environment. The task scheduling on grid is studied as optimization problem in this paper. A heuristic task scheduling algorithm satisfying resources load balancing on grid environment is presented. The algorithm schedules tasks by employing mean load based on task predictive execution time as heuristic information to obtain an initial scheduling strategy. Then an optimal scheduling strategy is achieved by selecting two machines satisfying condition to change their loads via reassigning their tasks under the heuristic of their mean load. Methods of selecting machines and tasks are given in this paper to increase the throughput of the system and reduce the total waiting time. The efficiency of the algorithm is analyzed and the performance of the proposed algorithm is evaluated via extensive simulation experiments. Experimental results show that the heuristic algorithm performs significantly to ensure high load balancing and achieve an optimal scheduling strategy almost all the time. Furthermore, results show that our algorithm is high efficient in terms of time complexity.     

Dynamic workload balancing of parallel applications with user-level scheduling on the Grid

This paper suggests a hybrid resource management approach for efficient parallel distributed computing on the Grid. It operates on both application and system levels, combining user-level job scheduling with dynamic workload balancing algorithm that automatically adapts a parallel application to the heterogeneous resources, based on the actual resource parameters and estimated requirements of the application. The hybrid environment and the algorithm for automated load balancing are described, the influence of resource heterogeneity level is measured, and the speedup achieved with this technique is demonstrated for different types of applications and resources.     

基于Red5服务器集群负载均衡调度算法研究

基于动态负反馈的机制,参考现有的加权最小连接数调度和轮转调度算法,设计出了改进后的负载均衡调度算法。通过负反馈机制计算每台服务器的综合负载权重值,而综合负载权重值直接体现着服务器的当前处理能力。调度服务器根据综合负载权重值分配工作负载,实现负载的均衡分布。     

离散微粒群优化算法在网格任务调度中的应用

网格任务调度算法是影响网格成功与否的关键技术之一.在研究现有任务调度策略的基础上,指出Min-Min算法的负载不均衡性.借鉴遗传算法中的交叉操作过程,提出了一种新的任务调度算法.该算法对传统的连续型微粒群优化算法进行改进,使其适用于网格任务调度问题的优化处理,实现网格资源的优化分配.仿真研究表明该算法更符合网格调度的复杂环境,能得到较短的任务执行时间和较好的负载均衡性.对比分析表明,离散微粒群优化算法所得结果优于常用的Min-Min调度方案,是一种高效的调度方法.     

基于目标约束的分层动态负载均衡算法*

针对网格环境下的负载不均问题,提出了一种分层动态负载均衡机制,该机制采用随机服务模型描述网格任务流特性及其资源上的动态负载状态,将站点内负载平衡问题归结为目标约束规划问题。理论分析了分层负载均衡机制的有效性证明并设计了优化方案的求解算法,仿真实验结果显示,该分层负载均衡算法在平均响应时间、系统吞吐量方面优于以往的RBA算法和DBA算法。     

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。     

On scheduling transactions in a grid processing system considering load through Ant Colony Optimization

Load balanced transaction scheduling problem is an important issue in distributed computing environments including grid system. This problem is known to be NP-hard and can be solved by using heuristic as well as any meta-heuristic method. We ponder over the problem of the load balanced transaction scheduling in a grid processing system by using an Ant Colony Optimization for load balancing. The problem that we consider is to achieve good execution characteristics for a given set of transactions that has to be completed within their given deadline. We propose a transaction processing algorithm based on Ant Colony Optimization (ACO) for load balanced transaction scheduling. We modify two meta-heuristic along with ACO and three heuristic scheduling algorithms for the purpose of comparison with our proposed algorithm. The results of the comparison show that the proposed algorithm provides better results for the load balanced transaction scheduling in the grid processing system.