一种网格环境下的动态负载均衡机制

负载均衡机制有利于提高广域分布式环境中资源共享和协同工作的效率。根据网格系统的特点,采用灰色预测方法,设计了一种动态资源负载均衡机制,给出了预测模型和实时预测策略以及基于该机制的负载均衡算法。该资源负载均衡机制具有以下特点：可在较小的开销下取得满意的负载均衡性能,具有网格环境下的可扩展性,能够适应网格资源动态变化的特性,解决资源发现过程中的负载均衡 问题。     

基于分层与容错机制的云计算负载均衡策略

针对混合动态负载均衡算法应用在云计算中,出现的站点信息交换过于频繁导致处理效率低下以及缺乏容错机制等问题,提出了基于分层与容错机制的负载均衡算法。算法融合集中式和分布式的优点,通过组织邻站点,使站点信息交换控制在邻站点范围之内,在任务调度时携带站点实时负载信息以解决频繁广播负载消息导致网络繁忙与服务器效率低下的问题。算法实现云系统负载均衡,减小请求响应时间,引入容错备份机制,以增强系统鲁棒性。实验结果表明,基于分层与容错机制的云计算负载均衡策略在任务分配时间、任务响应时间方面比传统算法提高20%以上,且在稳定性方面所提算法优于传统算法。     

网格下的一种动态负载均衡算法理论模型

一个具有高可用性的网格平台,必然采取某种负载均衡策略和方法来解决提高资源的优化配置问题。影响负载均衡的因素有三个,分别是算法、网络拓扑以及负载均衡的粒度。本文在介绍网格技术的发展趋势和研究方向的基础上,主要针对负载均衡算法进行研究。提出了一种动态负载均衡算法的理论模型,并采用现有的算法对该理论模型进行了验证。实验结果表明,本文的理论模型是正确的、可行的和有效的。     

一种支持MMOG的MMOGGRID网格体系结构

基于C／S结构的MMOG不易扩展，服务器容易成为瓶颈．本文提出了一种应用网格技术实现MMOG的体系结构-MMOGGRID网格，并提出了适合此结构的负载均衡解决方案，该方案包括负载均衡算法和游戏地图动态划分算法．实验结果表明，该方案与其它同类的负载均衡机制相比减少了系统延迟，降低了计算资源使用率，更符合MMOG实时性和可扩展性的要求．     

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。     

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.     

基于软件定义网络的分层式控制器负载均衡机制

针对软件定义网络（SDN）多控制器负载均衡过程中控制器之间通信开销大以及控制器吞吐量低等问题,提出一种分层式控制器负载均衡机制。基于分层式架构,通过超级控制器与域控制器协作完成负载均衡,并采用预定义负载阈值以减少域控制器与超级控制器之间的消息交换开销;同时,该机制可以有效选择出过载最重的域控制器,并从该过载域控制器所控制的交换机中选取多个符合迁移标准的交换机,将其同时分别迁移到多个综合性能高的域控制器上,从而解决多控制器间负载不均衡问题。实验结果表明,与层次式SDN控制器协同负载均衡方案（COLBAS）以及用于控制器负载均衡的动态和自适应算法（DALB）相比,所提机制系统的消息数量降低了约79个百分点,且该系统的吞吐量分别比DALB、COLBAS分别提高了约8.57%、52.01%。所提机制能够有效降低通信开销,并提高系统吞吐量,有更好的负载均衡效果。     

一种多集群网格的负载均衡算法

网格环境由于其可扩展性、异构性以及大量的传输延迟,使得网格环境下的负载均衡不同于传统的分布式系统。提出了一种动态的分布式负载均衡算法,该算法综合考虑网格站点的处理能力和站点之间的传输延迟,采用即时分配策略来降低作业的执行成本,目标是使系统平均作业响应时间最小化。仿真结果显示该算法显著减少了作业的平均响应时间。     

视频网格动态负载加权平滑均衡算法

为实现网格中视频资源服务的动态负载均衡,对一种动态负载加权均衡算法进行改进。利用监测与发现系统收集每台视频服务器的CPU利用率等主要负载参数,运用上述参数加权得到综合负载,对相邻时刻的负载做平滑处理以避免调度抖动。通过比较平滑后的动态负载值与服务器综合负载阈值进行动态调度,改变相应节点的负载,避免视频服务器间的负载失衡。实验结果表明,该算法能有效降低系统平均服务延迟时间并提高吞吐量,从而提升视频资源网格服务的整体性能。     

气象应用网格中改进遗传负载均衡算法研究

根据气象应用网格的特点和影响负载均衡的因素,对基于气象应用网格的负载均衡技术进行分析和探讨,提出一种气象应用网格中的负载均衡算法,实验结果表明该算法提高网格的负载能力.     

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。     

Dynamic load balancing and efficient load estimators for asynchronous iterative algorithms

In a previous paper, we have shown the very high power of asynchronism for parallel iterative algorithms in a global context of grid computing. In this article, we study the interest of coupling load balancing with asynchronism in such algorithms. After proposing a noncentralized version of dynamic load balancing which is best suited to asynchronism, we verify its efficiency by some experiments on a general partial differential equation (PDE) problem. Finally, we give some general conditions for the use of load balancing to obtain good results with this kind of algorithm and discuss the choice of the residual as an efficient load estimator.     

Artificial life techniques for load balancing in computational grids

Load balancing is a very important and complex problem in computational grids. A computational grid differs from traditional high performance computing systems in the heterogeneity of the computing nodes and communication links, as well as background workloads that may be present in the computing nodes. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of load balancing scenarios. Artificial life techniques have been used to solve a wide range of complex problems in recent times. The power of these techniques stems from their capability in searching large search spaces, which arise in many combinatorial optimization problems, very efficiently. This paper studies several well-known artificial life techniques to gauge their suitability for solving grid load balancing problems. Due to their popularity and robustness, a genetic algorithm (GA) and tabu search (TS) are used to solve the grid load balancing problem. The effectiveness of each algorithm is shown for a number of test problems, especially when prediction information is not fully accurate. Performance comparisons with Min-min, Max-min, and Sufferage are also discussed.     

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.     

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.     

On the Design of Adaptive and Decentralized Load Balancing Algorithms with Load Estimation for Computational Grid Environments

In this paper, we address several issues that are imperative to grid environments such as handling resource heterogeneity and sharing, communication latency, job migration from one site to other, and load balancing. We address these issues by proposing two job migration algorithms, which are MELISA (modified ELISA) and LBA (load balancing on arrival). The algorithms differ in the way load balancing is carried out and is shown to be efficient in minimizing the response time on large and small-scale heterogeneous grid environments, respectively. MELISA, which is applicable to large-scale systems (that is, interGrid), is a modified version of ELISA in which we consider the job migration cost, resource heterogeneity, and network heterogeneity when load balancing is considered. The LBA algorithm, which is applicable to small-scale systems (that is, intraGrid), performs load balancing by estimating the expected finish time of a job on buddy processors on each job arrival. Both algorithms estimate system parameters such as the job arrival rate, CPU processing rate, and load on the processor and balance the load by migrating jobs to buddy processors by taking into account the job transfer cost, resource heterogeneity, and network heterogeneity. We quantify the performance of our algorithms using several influencing parameters such as the job size, data transfer rate, status exchange period, and migration limit, and we discuss the implications of the performance and choice of our approaches.     

Elastic Routing Table with Provable Performance for Congestion Control in DHT Networks

Consistent hashing-based DHT networks have an inherent load balancing problem. The problem becomes more severe in heterogeneous networks with nonuniform and time-varying popular files. Existing DHT load balancing algorithms are mainly focused on the issues caused by node heterogeneity. To deal with skewed lookups, this paper presents an elastic routing table (ERT) mechanism for query load balancing, based on the observation that high-degree nodes tend to receive more traffic load. The mechanism allows each node to have a routing table of variable size corresponding to node capacities. The indegree and outdegree of the routing table can also be adjusted dynamically in response to the change of file popularity and network churn. Theoretical analysis proves that the routing table degree is bounded. The ERT mechanism facilitates locality-aware randomized query forwarding to further improve lookup efficiency. By relating query forwarding to a supermarket customer service model, we prove that a two-way randomized query forwarding policy should lead to an exponential improvement in query processing time over random walking. Simulation results demonstrate the effectiveness of the ERT mechanism and its related query forwarding policy for congestion and query load balancing. In comparison with existing "virtual-server”-based load balancing algorithms and other routing table control approaches, the ERT-based congestion control protocol yields significant improvement in query lookup efficiency.     

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.     

一种基于Cache的网格任务反馈调度方法

网格调度的目标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,围绕着网格中的任务调度算法,国内外已做了大量的研究工作,先后提出了各种调度算法.但是,这些调度算法不能很好地适应网格环境下的自治性、动态性、分布性等特征.针对目前网格调度机制存在的问题,提出了一种动态的网格调度技术--基于Cache的反馈调度方法(cache based feedback scheduling,简称CBFS).该调度方法依据Cache中所存放的最近访问过的资源信息,如最近一次请求提交时间、任务完成时间等信息进行反馈调度,将任务提交给负载较小或性能较优的资源来完成.实验结果表明,CBFS方法不但可以有效减少不必要的延迟,而且在任务响应时间的平滑性、任务的吞吐率及任务在调度器等待调度的时间方面比随机调度等传统算法要好.