Cooperative distributed dynamic load balancing

Summary This paper explores and applies the concept of cooperation to the load balancing problem in a computer network. We discuss an analytical model and propose a scheme which can be classified as distributed, dynamic, and stochastic. In the case of a homogeneous network, we guarantee that the load is balanced and no communication cost or information exchange is necessary.     

A hierarchical adaptive distributed algorithm for load balancing

Load balancing/sharing exploits the communication facility between the servers of a distributed system, by using the exchanging of status information and jobs between any two servers of the system in order to improve the overall performance.We propose an adaptive distributed hierarchical scheme, the Virtual Tree Algorithm (VTA), which creates a virtual binary tree structure over the actual network topology. It uses the difference-initiated (DI) technique for load balancing, which needs remote information for the transfer policy and no additional information for the location policy. We demonstrate that the introduced virtual construction keeps the exchanged messages to relatively low levels. To evaluate the performance of our policy, we present both theoretical and simulation results. By using simulation, we compare our results with one of the most representative, adaptive, symmetrical and efficient algorithms, the Variable Threshold (V_THR) algorithm.     

Strategy and Simulation of Adaptive RID for Distributed Dynamic Load Balancing in Parallel Systems

Dynamic load balancing schemes are significant for efficiently executing nonuniform problems in highly parallel multicomputer systems.The objective is to minimize the total exectuion time of single applications.This paper has proposed an ARID strategy for distributed dynamic load balancing.Its principle and control protocol are described,and te communication overhead,the effect on system stability and the performance efficiency are analyzed.Finally,simulation experiments are carried out to compare the adaptive strategy with other dynamic load balancing schemes.     

A Dynamic Load Balancing Mechanism for Distributed Systems

It is desirable in a distributed system to have the system load balanced evenly among the nodes so that the mean job response time is minimized.In this paper,we present a dynamic load balancing mechanism(DLB).It adopts a cntralized approach and is network topology independent.The DLB mechanism employs a set of threscholds which are automatically adjusted as the system load changes.It also provides a simple mechanism for the system to switch between periodic and instantaneous load balancing policies with ease.The performance of the proposed algorithm is evaluated by intensive simulations for various parameters.Te simulation results show that the mean job response time in a system implementing DLB algorithm is significantly lower than the same system without load balancings.Furthermore,compared with a previously proposed algorithm,DLB algorithm demonstrates improved performance,especially when the system is heavily loaded and the load is unevenly distributed.     

Synchronous distributed load balancing on dynamic networks

In this paper, three distributed load-balancing algorithms for dynamic networks are investigated. Dynamic networks are networks in which the topology may change dynamically. The definition of a dynamic network is introduced and its graph model is presented. The main result of this study consists in proving the convergence toward the uniform load distribution of the diffusion algorithm on an arbitrary dynamic network despite communication link failures. We also give two adaptations of this algorithm (the GAE and the relaxed diffusion). Note that the hypotheses of our result are realistic and that for example the network does not have to be maintained connected. To study the behavior of these algorithms, we compare the load evolution by several simulations.     

A fast adaptive load balancing method for parallel particle-based simulations

Balancing the work load can improve the performance of distributed simulation systems. In this paper we propose a fast adaptive balancing method, in which a binary tree structure is used to partition the simulation region into sub-domains. From a global view to local views, we balance the loads between sub-domains recursively by compressing and stretching sub-domains in group. This method can adjust the sub-domains with heavy loads and decompose their loads very fast. Then we compare the algorithm with two previously proposed algorithms by an artificial case and a real distributed case respectively. In both cases, our method can get a faster convergence speed and a lower communication overhead.     

Resource scheduling and load balancing in distributed robotic control systems

This paper describes the latest advances made to a software architecture designed to control multiple miniature robots. As the robots themselves have very limited computational capabilities, a distributed control system is needed to coordinate tasks among a large number of robots. Two of the major challenges facing such a system are the scheduling of access to system resources and the distribution of work across multiple workstations. This paper discusses solutions to these problems in the context of a distributed surveillance task.     

负载平衡调度问题的一般模型研究

分布式系统提供了巨大的处理能力,为了实现和充分利用这种能力,需要优良的负载平衡调度技术。因此,负载平衡问题是影响分布式系统性能的重要因素。在深入研究分布式系统中负载平衡调度问题的基础上,归纳总结了负载平衡调度的一般模型,对影响负载平衡的各个因素进行了详细的分析。此模型已在一个实际模型中得到了有效地验证。     

On delay adjustment for dynamic load balancing in distributed virtual environments

Distributed virtual environments (DVEs) are becoming very popular in recent years, due to the rapid growing of applications, such as massive multiplayer online games (MMOGs). As the number of concurrent users increases, scalability becomes one of the major challenges in designing an interactive DVE system. One solution to address this scalability problem is to adopt a multi-server architecture. While some methods focus on the quality of partitioning the load among the servers, others focus on the efficiency of the partitioning process itself. However, all these methods neglect the effect of network delay among the servers on the accuracy of the load balancing solutions. As we show in this paper, the change in the load of the servers due to network delay would affect the performance of the load balancing algorithm. In this work, we conduct a formal analysis of this problem and discuss two efficient delay adjustment schemes to address the problem. Our experimental results show that our proposed schemes can significantly improve the performance of the load balancing algorithm with neglectable computation overhead.     

A repartitioning hypergraph model for dynamic load balancing

In parallel adaptive applications, the computational structure of the applications changes over time, leading to load imbalances even though the initial load distributions were balanced. To restore balance and to keep communication volume low in further iterations of the applications, dynamic load balancing (repartitioning) of the changed computational structure is required. Repartitioning differs from static load balancing (partitioning) due to the additional requirement of minimizing migration cost to move data from an existing partition to a new partition. In this paper, we present a novel repartitioning hypergraph model for dynamic load balancing that accounts for both communication volume in the application and migration cost to move data, in order to minimize the overall cost. The use of a hypergraph-based model allows us to accurately model communication costs rather than approximate them with graph-based models. We show that the new model can be realized using hypergraph partitioning with fixed vertices and describe our parallel multilevel implementation within the Zoltan load balancing toolkit. To the best of our knowledge, this is the first implementation for dynamic load balancing based on hypergraph partitioning. To demonstrate the effectiveness of our approach, we conducted experiments on a Linux cluster with 1024 processors. The results show that, in terms of reducing total cost, our new model compares favorably to the graph-based dynamic load balancing approaches, and multilevel approaches improve the repartitioning quality significantly.     

分布式数据库系统的动态负载平衡策略及算法设计

随着网络中数据库应用的发展,使得分布式数据库系统的负载平衡问题突显出来。目前大多数分布式数据库管理系统没有负责平衡功能,其负载平衡依赖于操作系统的负载平衡机制来解决,这样带来的问题是系统负载的评价粒度细小和负载转移的开销增加。讨论了动态负载平衡策略的要素,针对分布式数据库系统的负载平衡问题,提出了以事务队列长度作为负载评价标准,并给出了一个动态负载平衡策略及算法。     

Framework of agent-based intelligence system with two-stage decision-making process for distributed dynamic scheduling

The advent of multiagent systems, a branch of distributed artificial intelligence, introduced a new approach to problem solving through agents interacting in the problem solving process. In this paper, a collaborative framework of a distributed agent-based intelligence system is addressed to control and resolve dynamic scheduling problem of distributed projects for practical purposes. If any delay event occurs, the self-interested activity agent, the major agent for the problem solving of dynamic scheduling in the framework, can automatically cooperate with other agents in real time to solve the problem through a two-stage decision-making process: the fuzzy decision-making process and the compensatory negotiation process. The first stage determines which behavior strategy will be taken by agents while delay event occurs, and prepares to next negotiation process; then the compensatory negotiations among agents are opened related with determination of compensations for respective decisions and strategies, to solve dynamic scheduling problem in the second stage. A prototype system is also developed and simulated with a case to validate the problem solving of distributed dynamic scheduling in the framework.     

基于动态负载均衡策略的网格任务调度优化模型和算法

任务调度是一个NP-hard问题,而且是并行与分布式计算中一个必不可少的组成部分,特别是在网格计算环境中任务调度更加复杂。结合免疫克隆算法和模拟退火算法的优点,提出了一种网格任务调度优化模型和算法。仿真实验结果表明,这种调度算法有效地实现了资源的负载均衡,克服了遗传算法容易陷入局部最优的缺点,可以成功地应用于网格任务调度中。     

Dynamic balancing of communication and computation load for HLA-based simulations on large-scale distributed systems

Dynamic balancing of computation and communication load is vital for the execution stability and performance of distributed, parallel simulations deployed on the shared, unreliable resources of large-scale environments. High Level Architecture (HLA) based simulations can experience a decrease in performance due to imbalances that are produced initially and/or during run time. These imbalances are generated by the dynamic load changes of distributed simulations or by unknown, non-managed background processes resulting from the non-dedication of shared resources. Due to the dynamic execution characteristics of elements that compose distributed applications, the computational load and interaction dependencies of each simulation entity change during run time. These dynamic changes lead to an irregular load and communication distribution, which increases overhead of resources and latencies. A static partitioning of load is limited to deterministic applications and is incapable of predicting the dynamic changes caused by distributed applications or by external background processes. Therefore, a scheme for balancing the communication and computational load during the execution of distributed simulations is devised in a scalable hierarchical architecture. The proposed balancing system employs local and cluster monitoring mechanisms in order to observe the distributed load changes and identify imbalances, repartitioning policies to determine a distribution of load and minimize imbalances. A migration technique is also employed by this proposed balancing system to perform reliable and low-latency load transfers. Such a system successfully improves the use of shared resources and increases distributed simulations’ performance by minimizing communication latencies and partitioning the load evenly. Experiments and comparative analyses were conducted in order to identify the gains that the proposed balancing scheme provides to large-scale distributed simulations.     

实验云虚拟资源动态自适应负载均衡研究

针对云平台的虚拟资源的负载均衡问题,为了实现虚拟资源的合理分配和保持用户桌面的稳定性,提出一种基于动态改变算法权值的自适应粒子群算法,并利用Eucalyptus平台进行实验。实验证明,该方法比贪婪算法和基本粒子群算法具有更好的调节效果,能够有效地控制负载均衡和保持系统的稳定性。     

Noncooperative load balancing in distributed systems

In this paper, we present a game theoretic framework for obtaining a user-optimal load balancing scheme in heterogeneous distributed systems. We formulate the static load balancing problem in heterogeneous distributed systems as a noncooperative game among users. For the proposed noncooperative load balancing game, we present the structure of the Nash equilibrium. Based on this structure we derive a new distributed load balancing algorithm. Finally, the performance of our noncooperative load balancing scheme is compared with that of other existing schemes. The main advantages of our load balancing scheme are the distributed structure, low complexity and optimality of allocation for each user.     

Cooperative load balancing in distributed systems

A serious difficulty in concurrent programming of a distributed system is how to deal with scheduling and load balancing of such a system which may consist of heterogeneous computers. In this paper, we formulate the static load‐balancing problem in single class job distributed systems as a cooperative game among computers. The computers comprising the distributed system are modeled as M/M/1 queueing systems. It is shown that the Nash bargaining solution (NBS) provides an optimal solution (operation point) for the distributed system and it is also a fair solution. We propose a cooperative load‐balancing game and present the structure of NBS. For this game an algorithm for computing NBS is derived. We show that the fairness index is always equal to 1 using NBS, which means that the solution is fair to all jobs. Finally, the performance of our cooperative load‐balancing scheme is compared with that of other existing schemes. Copyright © 2008 John Wiley & Sons, Ltd.     

Game-theoretic static load balancing for distributed systems

In this paper, we present a game theoretic approach to solve the static load balancing problem for single-class and multi-class (multi-user) jobs in a distributed system where the computers are connected by a communication network. The objective of our approach is to provide fairness to all the jobs (in a single-class system) and the users of the jobs (in a multi-user system). To provide fairness to all the jobs in the system, we use a cooperative game to model the load balancing problem. Our solution is based on the Nash Bargaining Solution (NBS) which provides a Pareto optimal solution for the distributed system and is also a fair solution. An algorithm for computing the NBS is derived for the proposed cooperative load balancing game. To provide fairness to all the users in the system, the load balancing problem is formulated as a non-cooperative game among the users who try to minimize the expected response time of their own jobs. We use the concept of Nash equilibrium as the solution of our non-cooperative game and derive a distributed algorithm for computing it. Our schemes are compared with other existing schemes using simulations with various system loads and configurations. We show that our schemes perform near the system optimal schemes and are superior to the other schemes in terms of fairness.     

On load balancing for distributed multiagent computing

Multiagent computing on a cluster of workstations is widely envisioned to be a powerful paradigm for building useful distributed applications. The agents of the system span across all the machines of a cluster. Just like the case of traditional distributed systems, load balancing becomes an area of concern. With different characteristics between ordinary processes and agents, it is both interesting and useful to investigate whether conventional load-balancing strategies are also applicable and sufficient to cope with the newly emerging needs, such as coping with temporally continuous agents, devising a performance metric for multiagent systems, and taking into account the vast amount of communication and interaction among agent. This paper discusses the above issues with reference to agent properties and load balancing techniques and outlines the space of load-balancing design choices in the arena of multiagent computing. In view of the special agent characteristics, a novel communication-based load-balancing algorithm is proposed, implemented, and evaluated. The proposed algorithm works by associating a credit value with each agent. The credit of an agent depends on its affinity to a machine, its current workload, its communication behavior, and mobility, etc. When a load imbalance occurs, the credits of all agents are examined and an agent with a lower credit value is migrated to relatively lightly loaded machine in the system. Quasi-simulated experiments of this algorithm show load-balancing improvement compared with conventional workload-oriented load-balancing schemes.