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.     

一种基于负载平衡的网络接入选择方法

针对异构无线环境,提出了一种基于负载平衡的网络选择算法。采用"终端辅助,网络决策"的策略,首先利用修正权重后的层次分析法得到网络参考向量,然后终端用户根据向量间的几何关系挑选出可接入网络集合,最后网络侧基于负载平衡的前提下为终端用户选择最终接入网络。通过仿真对比,验证了该算法的有效性。     

A bipartite model for load balancing in grid computing environments

In this paper, a bipartite model for load balancing (LB) in grid computing environments, called Transverse viewpoint-based Bi-Tier model (TBT), is proposed. TBT can efficiently eliminate topology mismatching between overlay- and physical-networks during the load transfer process. As an implementation of TBT, a novel LB policy called M²ON (Min-cost and Max-flow Channel based Overlay Network) is presented. In M²ON, the communication capability is denoted as M²C (Min-cost and Max-flow Channel) which is obtained using a Labeled Tree Probing (LTP) method. The computing capacity is denoted as the Idle Factor (IF) which is obtained from the semantic overlay. The higher- and lower-level characteristics are combined into an Integrated Impacting Factor (IIF) using a Double Linear Inserting (DLI) function. Based on IIF, optimal topology matching can be achieved in the LB process. Extensive experiments and simulations have been performed and will be discussed. The results show that M²ON achieves more accurate topology matching with a minimum increment in the overall locating time yet achieving higher system performance as a whole.     

A decomposition algorithm for optimal static load balancing in treehierarchy network configurations

We study the static load balancing problem in a distributed computer system with the tree hierarchy configuration. It is formulated as a nonlinear optimization problem. After studying the conditions that the solution to the optimization problem of the tree hierarchy network satisfies, we demonstrate that the special structure of the optimization problem leads to an interesting decomposition technique. A new effective decomposition algorithm to solve the optimization problem is presented. The proposed algorithm Is compared with two other well known algorithms: the Flow Deviation (FD) algorithm and the Dafermos-Sparrow (D-S) algorithm. It is shown that the amounts of the storage required for the proposed algorithm and the FD algorithm are O(n) for load balancing of an n-node system. However, the amount of the storage required for the D-S algorithm is O(n log(n)). By using numerical experiments, we show that both the proposed algorithm and the D-S algorithm have much faster convergence in terms of central processing unit (CPU) time than the FD algorithm     

基于OpenFlow的数据中心网络负载均衡算法

现代数据中心网络(Date Center Network,DCN)经常会使用多路径(Multi Path,MP)拓扑结构,这样可以避免两节点间某条链路失效而导致的网络拥塞问题,而且增加了网络的带宽和容错率。传统的OSPF(Open Shortest Path First)路由算法会选择单条最短路径作为最终路径,这样可能会导致大部分数据流集中在单一路径上而出现网络拥塞,而其他可用路径处于闲置状态,不能充分地利用DCN中的链路资源,基于SDN(Software Defined Network)的集中化的调度方式能够提高网络利用效率。设计了一套基于Open Flow协议的链路负载均衡模型,详细阐述了它的总体框架和算法实现过程,并通过实验仿真验证了算法的可行性和有效性。     

Autonomic load balancing of flow monitors

In monitoring flows at routers for flow analysis or deep packet inspection, the monitor calculates hash values from the flow ID of each packet arriving at the input port of the router. Therefore, the monitors must update the flow table at the transmission line rate, so high-speed and high-cost memory, such as SRAM, is used for the flow table. This requires the monitors to limit the monitoring target to just some of the flows. However, if the monitors randomly select the monitoring targets, multiple routers on the route will sometimes monitor the same flow, or no monitors will monitor a flow. To maximize the number of monitored flows in the entire network, the monitors must select the monitoring targets while maintaining a balanced load among them. We propose an autonomous load-balancing method where monitors exchange information on monitor load only with adjacent monitors. Numerical evaluations using the actual traffic matrix of Internet2 show that the proposed method improves the total monitored flow count by about 50% compared with that of independent sampling. Moreover, we evaluate the load-balancing effect on 36 backbone networks of commercial ISPs.     

多蚁群算法的网络负载动态均衡方法

针对网络资源管理中的负载均衡与优化问题,提出一种多蚁群网络负载动态均衡方法,采用网络流量工程理论中拥塞控制机制实现信息素随网络流量动态释放与更新。算法通过蚁群间信息素的动态相互作用（蚁群内信息素相互增强,蚁群间信息素相互削弱）,将代表网络负载的蚂蚁合理分配到可用路径,避免蚂蚁集中到特定路径而造成网络拥塞。实验结果表明,通过路径信息素控制能够实现网络负载均衡,有效提高网络在路径延时、平均带宽利用率和平均丢包率方面的性能。     

异构无线网络负载均衡研究*

针对异构网络中的带宽分配及网络的负载均衡问题,提出了一种异构无线网络负载均衡的联合优化模型,该模型在一定条件下利用用户的带宽进行负载成本和网络效用的建立,并通过求解网络与用户的关联矩阵来得到带宽在网络中的优化分配方案,使异构无线网络达到负载均衡。仿真结果表明,联合优化模型的结果在最小化负载成本以及优化网络总效用性能之间,它让用户通过在不同网络间的切换使网络带宽优化分配、网络负载达到均衡。     

A case for on-machine load balancing

This paper diverges from the traditional load balancing, and introduces a new principle called the on-machine load balance rule. The on-machine load balance rule leads to resource allocations that are better in tolerating uncertainties in the processing times of the tasks allocated to the resources when compared to other resource allocations that are derived using the conventional “across-the-machines” load balancing rule. The on-machine load balance rule calls for the resource allocation algorithms to allocate similarly sized tasks on a machine (in addition to optimizing some primary performance measures such as estimated makespan and average response time). The on-machine load balance rule is very different from the usual across-the-machines load balance rule that strives to balance load across resources so that all resources have similar finishing times.We give a mathematical justification for the on-machine load balance rule requiring only liberal assumptions about task processing times. Then we validate with extensive simulations that the resource allocations derived using on-machine load balance rule are indeed more tolerant of uncertain task processing times.     

An asynchronous and iterative load balancing algorithm for discrete load model

Diffusion algorithms are some of the most popular algorithms for dynamic load balancing in which loads move from heavily loaded processors to lightly loaded neighbor processors. To achieve a global load balance in a parallel computer, the algorithm is iterated until the load difference between any two processors is smaller than a specified value. Therefore, one fundamental property to be studied is algorithm convergence. Several analytical works on the convergence of different diffusion load balancing algorithms have been carried out, but they treat loads as non-negative real quantities. In this paper, we describe the Diffusion Algorithm Searching Unbalanced Domains (DASUD) algorithm, which uses loads as non-negative integer values and, unlike existing algorithms, reaches a local balance situation where the maximum load difference between any two processor in the set of neighbor processors for each processor is one load unit. The convergence property of an asynchronous implementation of DASUD using integer loads is proven theoretically.     

网络计算机集群负载均衡机制的研究

目前使用单台网络计算机应用服务器难以满足大量用户的并发访问需求,在网络计算机系统中引入集群/负载均衡技术是解决这一问题的理想途径.研究网络计算机集群的负载均衡机制.设计和实现了适合于网络计算机应用模式的负载评估方法及负载状态更新机制,并在此基础上提出和实现了一种基于分布式协商的动态负载均衡算法.     

Perfect load balancing on the star interconnection network

In this paper, we use the regular distribution method to design a perfect load balancing algorithm for an n-star with a maximum error of 1 and a time complexity of 3n(n+1). This algorithm is based on the novel notion of leader trees. A second algorithm proposed in this paper as an enhancement to our first algorithm and uses an arbitrary spanning tree as the leader tree and has a worst time complexity of 2.25n ²−3n+0.75. We also discuss the issue of dynamically selecting the leader tree and hybrid load balancing algorithms in general. Furthermore, we present a hybrid algorithm for load balancing on the star interconnection network which benefits from a diffusion load balancing preprocessing phase and shows a smaller mean time complexity than our two first algorithms.     

A load index and load balancing algorithm for heterogeneous clusters

This paper presents a load balancing algorithm specifically designed for heterogeneous clusters, composed of nodes with different computational capabilities. The method is based on a new index, which takes into consideration two levels of processors heterogeneity: the number of cores per node and the computational power of each core. The experimental results show that this index allows achieving balanced workload distributions even on those clusters where heterogeneity can not be neglected.     

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。     

The probability of load balancing success in a homogeneous network

The problem of load balancing in distributed systems composed of several homogeneous sites connected by a subnet is examined. The author determines a general formula for the probability that any one site in the system is underloaded while some other site in the system is overloaded. This probability can be used to define the likelihood of load balancing success in a distributed operating system. This probability gives insight into the utilization of the system and is an aid in determining a measure of effectiveness of the system. From this formula one can determine this probability when the workload is composed of processes typical to distributed systems. The influence of variants in the load balancing algorithm on this probability is demonstrated     

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 %.     

数据中心网络四层负载均衡技术综述

针对数据中心难以适应流量增长进行横向扩展并保证连接一致性的问题,阐述了四层负载均衡技术在应对高并发访问和提高资源利用率方面的重要作用,梳理了国内外四层负载均衡模块的设计与算法,总结了负载均衡器以不同方式进行部署分别存在的优缺点,同时分析了网络可编程转发技术在四层负载均衡领域中的应用与最新进展.最后,对网络新形势下负载均...     

智能网络磁盘集群负载平衡研究

智能网络磁盘（IND）是一种存储体系结构的新构思,IND集群是一种海量存储的新途径,为维护系统的自动负载平衡,用基于访问频数的动态调整和适时迁移策略相结合,精心设计算法,合理布局数据,使系统高效稳定运行,长期实践表明,这种负载平衡的灵活调度策略,对IND集群存储系统的实现是必要而有利的,对高性能计算的海量存储尤为重要。     

A hybrid column generation with GRASP and path relinking for the network load balancing problem

In this paper, a hybrid meta-heuristic is proposed which combines the GRASP with path relinking method and Column Generation. The key idea of this method is to run a GRASP with path relinking search on a restricted search space, defined by Column Generation, instead of running the search on the complete search space of the problem. Moreover, column generation is used not only to compute the initial restricted search space but also to modify it during the whole algorithm. The proposed heuristic is used to solve the network load balancing problem: given a capacitated telecommunications network with single path routing and an estimated traffic demand matrix, the network load balancing problem is the determination of a routing path for each traffic commodity such that the network load balancing is optimized, i.e., the worst link load is minimized, among all such solutions, the second worst link load is minimized, and continuing in this way until all link loads are minimized. The computational results presented in this paper show that, for the network load balancing problem, the proposed heuristic is effective in obtaining better quality solutions in shorter running times.