An improved approach for load balancing among heterogeneous resources in computational grids

In recent days, due to the rapid technological advancements, the grid computing has become an important area of research in distributed systems. The load balancing is a very important and complex problem in grid computing. In this paper, we propose a dynamic-distributed load-balancing technique called the improved load balancing on enhanced GridSim with deadline control (IEGDC) for computational grids. Here, we provide a new mechanism of scheduling to enhance the utilization of the resources and to prevent the resource overloading. A selection method for scheduling by considering the state of resource bandwidth and capacity of various resources is presented. We simulate the proposed load-balancing strategy on the GridSim platform. The proposed mechanism on comparison is found to outperform the existing schemes in terms of response time, resubmitted time, finished and unfinished Gridlets. The simulation results are presented.     

An enhanced load balancing mechanism based on deadline control on GridSim

A Grid is a network of computational resources that may potentially span many continents. Load balancing in a Grid is a hot research issue which affects every aspect of the Grid, including service selection and task execution. Thus, it is necessary and significant to solve the load balancing problem in a Grid. In this paper, we propose a dynamic, distributed load balancing scheme for a Grid which provides deadline control for tasks. In our scenario, first, resources check their state and make a request to the Grid Broker according to the change of load state. Then, the Grid Broker assigns Gridlets between resources and scheduling for load balancing under the deadline request. We apply our load balancing strategy into a popular Grid simulation platform GridSim. Experimental results prove that our proposed load balancing mechanism can (1) reduce the makespan, (2) improve the finished rate of the Gridlet, and (3) reduce the resubmitted time.     

Enhanced GridSim architecture with load balancing

Grid is a network of computational resources that may potentially span many continents. Maximization of the resource utilization hinges on the implementation of an efficient load balancing scheme, which provides (i) minimization of idle time, (ii) minimization of overloading, and (iii) minimization of control overhead. In this paper, we propose a dynamic and distributed load balancing scheme for grid networks. The distributed nature of the proposed scheme not only reduces the communication overhead of grid resources but also cuts down the idle time of the resources during the process of load balancing. We apply the proposed load balancing approach on Enhanced GridSim in order to gauge the effectiveness in terms of communication overhead and response time reduction. We show that significant savings are delivered by the proposed technique compared to other approaches such as centralized load balancing and no load balancing.     

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.     

Biased random walks on resource network graphs for load balancing

The currently emerging large-scale complex networks and networks of networks are becoming apparent in the pervasive supply of seamless and transparent access to heterogeneous resources and services such as network domains, applications, services and storage owned by multiple organizations. The dynamics and heterogeneous environments involved, however, pose many challenges for controlling and balancing resource access, composition and deployment across complex grid and network infrastructures. In this paper, a scheme is proposed that gives a distributed load-balancing scheme by generating almost regular resource allocation networks. This network system is self-organized and depends only on local information for load distribution and resource discovery. The in-degree of each node refers to its free resources, and the job assignment and resource discovery processes required for load-balancing are accomplished by using fitted random sampling. Simulation results show that the generated network system provides an effective, scalable, and reliable load-balancing scheme for the distributed resources in grids and networks. The proposed solution is tested with real world data and the performance is tested against a recently reported distributed algorithm for load balancing.     

Swarm Intelligence Approaches for Grid Load Balancing

With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. The huge amount of computations a Grid can fulfill in a specific amount of time cannot be performed by the best supercomputers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized optimally using a good load balancing algorithm. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One algorithm is based on ant colony optimization and the other algorithm is based on particle swarm optimization. A simulation of the proposed approaches using a Grid simulation toolkit (GridSim) is conducted. The performance of the algorithms are evaluated using performance criteria such as makespan and load balancing level. A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches is provided. Experimental results show the proposed algorithms perform very well in a Grid environment. Especially the application of particle swarm optimization, can yield better performance results in many scenarios than the ant colony approach.     

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.     

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.     

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 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.     

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．NET Remoting的动态负载平衡模型

在对.NET Remoting技术和传统的分布式负载平衡算法深入研究的基础上,提出一种排序队列和哈希映射矩阵相结合的负载平衡策略,给出一个分布式环境下的动态负载平衡模型。一方面可提高系统吞吐量,另一方面可缩短任务请求的响应时间。模型采用模块化设计方法,使其具有部署灵活性和容错性,并应用滑动窗口机制提高模型的负载平衡指标可信度。     

Reputation-based dependable scheduling of workflow applications in Peer-to-Peer Grids

Grids facilitate creation of wide-area collaborative environment for sharing computing or storage resources and various applications. Inter-connecting distributed Grid sites through peer-to-peer routing and information dissemination structure (also known as Peer-to-Peer Grids) is essential to avoid the problems of scheduling efficiency bottleneck and single point of failure in the centralized or hierarchical scheduling approaches. On the other hand, uncertainty and unreliability are facts in distributed infrastructures such as Peer-to-Peer Grids, which are triggered by multiple factors including scale, dynamism, failures, and incomplete global knowledge.In this paper, a reputation-based Grid workflow scheduling technique is proposed to counter the effect of inherent unreliability and temporal characteristics of computing resources in large scale, decentralized Peer-to-Peer Grid environments. The proposed approach builds upon structured peer-to-peer indexing and networking techniques to create a scalable wide-area overlay of Grid sites for supporting dependable scheduling of applications. The scheduling algorithm considers reliability of a Grid resource as a statistical property, which is globally computed in the decentralized Grid overlay based on dynamic feedbacks or reputation scores assigned by individual service consumers mediated via Grid resource brokers. The proposed algorithm dynamically adapts to changing resource conditions and offers significant performance gains as compared to traditional approaches in the event of unsuccessful job execution or resource failure. The results evaluated through an extensive trace driven simulation show that our scheduling technique can reduce the makespan up to 50% and successfully isolate the failure-prone resources from the system.     

Cloud-aware data intensive workflow scheduling on volunteer computing systems

Volunteer computing systems offer high computing power to the scientific communities to run large data intensive scientific workflows. However, these computing environments provide the best effort infrastructure to execute high performance jobs. This work aims to schedule scientific and data intensive workflows on hybrid of the volunteer computing system and Cloud resources to enhance the utilization of these environments and increase the percentage of workflow that meets the deadline. The proposed workflow scheduling system partitions a workflow into sub-workflows to minimize data dependencies among the sub-workflows. Then these sub-workflows are scheduled to distribute on volunteer resources according to the proximity of resources and the load balancing policy. The execution time of each sub-workflow on the selected volunteer resources is estimated in this phase. If any of the sub-workflows misses the sub-deadline due to the large waiting time, we consider re-scheduling of this sub-workflow into the public Cloud resources. This re-scheduling improves the system performance by increasing the percentage of workflows that meet the deadline. The proposed Cloud-aware data intensive scheduling algorithm increases the percentage of workflow that meet the deadline with a factor of 75% in average with respect to the execution of workflows on the volunteer resources.     

结合预测机制和QoS约束的网格资源调度算法的研究

资源调度是网格计算领域中的研究热点之一.以达到最优的资源利用率和提高用户对服务的满意程度为目标,定义了资源QoS约束和形式化描述;在任务完成期限和网络带宽的双重属性约束下结合预测机制,提出了网格资源调度算法Senior;应用GridSim工具包实现了相关的调度算法,并对调度算法仿真结果中的数据进行了分析和比较,验证了Senior调度算法在解决类似问题的优势.     

A model for communication between resource discovery and load balancing units in computing environments

Resource overloading causes one of the main challenges in computing environments. In this case, a new resource should be discovered to transfer the extra load. However, this results in drastic performance degradation. Thus, it is of high importance to discover the appropriate resource at first. So far, several resource discovery mechanisms have been introduced to overcome this challenge, a majority of which neglect the fact that this important decision should be made in cooperation with other units existing in a computing environment. One of the units is load balancing. In this paper, we propose a model for communication between resource discovery and load balancing units in a computing environment. Based on the model, resource discovery and load balancing decisions are made cooperatively considering the behavior of running processes and resources capacities. These considerations make decisions more precise. In addition, the model presents the loosest type of coupling between resource discovery and load balancing units, i.e., message coupling. This feature provides a better scalability in size for the model. Comparative results show that the proposed model increases scalability in size by 7 to 15 %, cuts message transmission rate by 15 % and improves hit rate by 51 %.     

DistDLB: Improving cosmology SAMR simulations on distributed computing systems through hierarchical load balancing

Cosmology SAMR simulations have played a prominent role in the field of astrophysics. The emerging distributed computing systems provide an economic alternative to the traditional parallel machines, and enable scientists to conduct cosmological simulations that require vast computing power. An important issue of conducting distributed cosmological simulations is about performance and efficiency. In this paper, we present a dynamic load balancing scheme called DistDLB that is designed to improve the performance of distributed cosmology simulations. Distributed systems, e.g. the Computation Grid, usually consist of heterogeneous resources connected with shared networks. By considering these features of distributed systems and unique characteristics of cosmology SAMR simulations, DistDLB focuses on reducing the redistribution cost through a hierarchical load balancing approach and a run-time decision making mechanism. Heuristic methods have been proposed to adaptively adjust load balancing strategies based on the observation of the current system and application state. Our experiments with real-world cosmology simulations on production systems indicate that the proposed DistDLB scheme can effectively improve the performance of cosmology simulations by 2.56–79.14% as compared to the scheme that does not consider the heterogeneous and dynamic features of distributed systems.     

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.     

Libra: a computational economy‐based job scheduling system for clusters

Clusters of computers have emerged as mainstream parallel and distributed platforms for high‐performance, high‐throughput and high‐availability computing. To enable effective resource management on clusters, numerous cluster management systems and schedulers have been designed. However, their focus has essentially been on maximizing CPU performance, but not on improving the value of utility delivered to the user and quality of services. This paper presents a new computational economy driven scheduling system called Libra, which has been designed to support allocation of resources based on the users' quality of service requirements. It is intended to work as an add‐on to the existing queuing and resource management system. The first version has been implemented as a plugin scheduler to the Portable Batch System. The scheduler offers market‐based economy driven service for managing batch jobs on clusters by scheduling CPU time according to user‐perceived value (utility), determined by their budget and deadline rather than system performance considerations. The Libra scheduler has been simulated using the GridSim toolkit to carry out a detailed performance analysis. Results show that the deadline and budget based proportional resource allocation strategy improves the utility of the system and user satisfaction as compared with system‐centric scheduling strategies. Copyright © 2004 John Wiley & Sons, Ltd.     

CORBA负载平衡的研究

一、引言作为分布式计算的一个重要规范—CORBA,其主要目标是解决面向对象的异构应用之间的互操作问题,并提供了分布式计算所需的多项服务。ORB是CORBA平台的核心,它用于屏蔽与底层平台有关的细节,使开发者可以集中精力去解决与应用相关的问