基于服务器实时性能状态的动态负载均衡算法

为实现Web服务器集群合理的作业任务分配,文章提出了一种新的负载均衡算法,综合考虑了负载均衡调度器后端的业务主机的实时性能,实现了负载均衡调度的动态调整.     

基于文化算法的负载均衡自适应机制

负载均衡是解决Web集群系统容量和伸缩能力的重要方法,但通常使用的单纯的加权轮叫调度算法依然会导致服务器间的负载不平衡。本文分析了影响Web服务器性能的主要因素,提出了一种负载均衡的自适应机制。该方法将文化算法(CultureAlgorithmsCA)应用到对服务器性能权值的进化计算中,通过评价服务器的负载状况,获得优化的性能权值,并自适应地转换到集群的分配器中,使事务在集群系统中得到合理分配。模拟实验证明,随着访问量的逐渐增多,每台服务器都趋近于最佳负载,系统达到好的使用效果。     

基于服务类型的动态反馈负载均衡算法*

针对现有静态和动态负载均衡算法往往存在计算服务节点负载过程中引用特征信息过少,或忽视不同类型服务对于节点负载的影响等问题,提出了一种基于服务类型的动态反馈算法。该算法统计各节点的多种负载信息,通过NECP协议实现动态反馈,并引入负载权重向量和负载能力向量计算节点的综合负载。算法在实际的仿真环境中得到了验证,说明了具有可用性和优越性。     

基于网格计算的混合负载均衡策略

根据分布式系统的静态和动态负载均衡策略的优缺点,提出了在网格计算环境下的混合负载均衡策略.为了让网络中节点在网格计算环境中有效地执行需要大量计算的复杂任务,提出了用来评估节点效率的函数,并结合模拟实验证实了在此函数下算法的优越性.     

基于空间填充曲线的动态负载均衡算法

针对Web集群系统中服务器的数量不断增加、负载指标动态变化的特点,为实现均衡的分配请求,提出一种使用空间填充曲线来实现动态负载均衡的算法。利用空间填充曲线可高效得将高维数据映射到一维索引的特点,使均衡器根据实时收集的各项负载指标快速定位到最优编码的服务器。实验结果表明,该算法能有效地缩短请求响应时间,提升了集群系统的整体性能,在大规模集群系统中均衡效果更好。     

基于网格计算的动态反馈负载均衡策略研究

基于分布式系统中静态负载均衡策略的优缺点,提出一种基于网格计算的动态反馈负载均衡策略,最终达到提高整个体系的网络吞吐率和服务平均响应时间等指标。     

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.     

基于时间序列预测的动态负载均衡算法

在分布式数据流计算系统中,负载均衡是必须解决的关键问题之一。为了追求高资源利用率同时又保持接近临界值的延迟时间,提出一种基于时间序列预测的动态负载均衡算法,算法根据时间序列预测算法预测的输入数据以及历史的延迟时间数据,动态确定计算节点个数。经性能测试表明,该算法在资源利用效率上具有明显的优势。     

基于网格计算的混合负载均衡的改进策略

近年来,随着科学研究对计算资源的要求不断增加,结合分布式计算环境和互联网的网格计算已经得到越来越多研究者的关注。网格计算就是利用网络中的空闲计算资源来协助那些要求大量计算的复杂任务的执行。根据分布式系统的静态和动态负载均衡策略的优缺点,本文提出了在网格计算环境下的混合负载均衡策略。为了让网络中的节点在网格计算环境中有效地执行需要大量计算的复杂任务,并根据大量的实验总结,提出了新的用来评估节点效率的函数,较以前的函数执行效率有了提高。     

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。     

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 new step toward load balancing based on competency rank and transitional phases in Grid networks

Grid computing is a network of software-hardware capabilities. It serves as a comprehensive and complete system for organizations by which the maximum utilization from resources is achieved. Resource distribution in a heterogeneous and unstable environment and also effective load distribution among these resources are the important and difficult problems in Grid networks. Using dynamic and static algorithms or searching tree and Branch and Bound algorithm are considered to be among the available methods to reach the load balancing in Grid networks. This paper presents a new method for dynamic load balancing. In this method, we use the subtraction of forward and backward ants as a competency rank to take the priority of the sites, and we use a control word to search the suitable resource as well. Our main purpose is to devote jobs to the existing resources based on their processing power. Simulation results show that the proposed method can reduce the total completion time and also total tardiness to get the load balancing. The cost of using resources as an effective factor in load balancing is also observed.     

Towards a hybrid load balancing policy in grid computing system

Grid computing has become conventional in distributed systems due to technological advancements and network popularity. Grid computing facilitates distributed applications by integrating available idle network computing resources into formidable computing power. As a result, by using efficient integration and sharing of resources, this enables abundant computing resources to solve complicated problems that a single machine cannot manage. However, grid computing mines resources from accessible idle nodes and node accessibility varies with time. A node that is currently idle, may become occupied within a second of time and then be unavailable to provide resources. Accordingly, node selection must provide effective and sufficient resources over a long period to allow load assignment. This study proposes a hybrid load balancing policy to integrate static and dynamic load balancing technologies. Essentially, a static load balancing policy is applied to select effective and suitable node sets. This will lower the unbalanced load probability caused by assigning tasks to ineffective nodes. When a node reveals the possible inability to continue providing resources, the dynamic load balancing policy will determine whether the node in question is ineffective to provide load assignment. The system will then obtain a new replacement node within a short time, to maintain system execution performance.     

A topology-aware load balancing algorithm for clustered hierarchical multi-core machines

In this paper, we present a topology-aware load balancing algorithm for parallel multi-core machines and its proof of asymptotic convergence to an optimal solution. The algorithm, named HwTopoLB, aims to improve the application performance by reducing core idleness and communication delays. HwTopoLB was designed taking into account the properties of current parallel systems composed of multi-core compute nodes, namely their network interconnection, and their complex and hierarchical core topology. The latter comprises multiple levels of cache, and a memory subsystem with NUMA design. These systems provide high processing power at the expense of asymmetric communication costs, which can hamper the performance of parallel applications depending on their communication patterns if ignored. Our load balancing algorithm models asymmetries in terms of latencies and bandwidths, representing the distances and communication costs among hardware components. We have implemented HwTopoLB using the Charm++ Parallel Runtime System and evaluated its performance with two different benchmarks and one application. Our experimental results with HwTopoLB exhibit scalability over clustered multi-core compute nodes, and average performance improvements of 23% over execution without load balancers and 19% over the existing load balancing strategies on different multi-core systems.     

Resource scheduling algorithm with load balancing for cloud service provisioning

Cloud computing uses scheduling and load balancing for virtualized file sharing in cloud infrastructure. These two have to be performed in an optimized manner in cloud computing environment to achieve optimal file sharing. Recently, Scalable traffic management has been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, latency reducing during multidimensional resource allocation still remains a challenge. Hence, there necessitates efficient resource scheduling for ensuring load optimization in cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The method constructs a Fuzzy-based Multidimensional Resource Scheduling model to obtain resource scheduling efficiency in cloud infrastructure. Increasing utilization of Virtual Machines through effective and fair load balancing is then achieved by dynamically selecting a request from a class using Multidimensional Queuing Load Optimization algorithm. A load balancing algorithm is then implemented to avoid underutilization and overutilization of resources, improving latency time for each class of request. Simulations were conducted to evaluate the effectiveness using Cloudsim simulator in cloud data centers and results shows that the proposed method achieves better performance in terms of average success rate, resource scheduling efficiency and response time. Simulation analysis shows that the method improves the resource scheduling efficiency by 7% and also reduces the response time by 35.5 % when compared to the state-of-the-art works.     

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.     

Join-Idle-Queue: A novel load balancing algorithm for dynamically scalable web services

The prevalence of dynamic-content web services, exemplified by search and online social networking, has motivated an increasingly wide web-facing front end. Horizontal scaling in the Cloud is favored for its elasticity, and distributed design of load balancers is highly desirable. Existing algorithms with a centralized design, such as Join-the-Shortest-Queue (JSQ), incur high communication overhead for distributed dispatchers.We propose a novel class of algorithms called Join-Idle-Queue (JIQ) for distributed load balancing in large systems. Unlike algorithms such as Power-of-Two, the JIQ algorithm incurs no communication overhead between the dispatchers and processors at job arrivals. We analyze the JIQ algorithm in the large system limit and find that it effectively results in a reduced system load, which produces 30-fold reduction in queueing overhead compared to Power-of-Two at medium to high load. An extension of the basic JIQ algorithm deals with very high loads using only local information of server load.     

Dynamic Load Balancing and Pricing in Grid Computing with Communication Delay

Due to the emergence of Grid computing over the Internet, there is presently a need for dynamic load balancing algorithms which take into account the characteristics of Grid computing environments. In this paper, we consider a Grid architecture where computers belong to dispersed administrative domains or groups which are connected with heterogeneous communication bandwidths. We address the problem of determining which group an arriving job should be allocated to and how its load can be distributed among computers in the group to optimize the performance. We propose algorithms which guarantee finding a load distribution over computers in a group that leads to the minimum response time or computational cost. We then study the effect of pricing on load distribution by considering a simple pricing function. We develop three fully distributed algorithms to decide which group the load should be allocated to, taking into account the communication cost among groups. These algorithms use different information exchange methods and a resource estimation technique to improve the accuracy of load balancing. We conducted extensive simulations to evaluate the performance of the proposed algorithms and strategies.     

Parallel extremal optimization in processor load balancing for distributed applications

The paper concerns parallel methods for extremal optimization (EO) applied in processor load balancing in execution of distributed programs. In these methods EO algorithms detect an optimized strategy of tasks migration leading to reduction of program execution time. We use an improved EO algorithm with guided state changes (EO-GS) that provides parallel search for next solution state during solution improvement based on some knowledge of the problem. The search is based on two-step stochastic selection using two fitness functions which account for computation and communication assessment of migration targets. Based on the improved EO-GS approach we propose and evaluate several versions of the parallelization methods of EO algorithms in the context of processor load balancing. Some of them use the crossover operation known in genetic algorithms. The quality of the proposed algorithms is evaluated by experiments with simulated load balancing in execution of distributed programs represented as macro data flow graphs. Load balancing based on so parallelized improved EO provides better convergence of the algorithm, smaller number of task migrations to be done and reduced execution time of applications.