Load balancing optimization of telecommunication networks with two differentiated services

The Differentiated Services architecture is a scalable solution to provide differentiated Quality of Service. In this paper, we address the network load balancing optimization of such networks based on bandwidth differentiation between two services. We define the optimization problem as an Integer Programming model and propose a heuristic algorithm based on GRASP with Path Relinking. We present computational results showing that (i) good quality solutions can be computed and (ii) proper load balancing can efficiently obtain service differentiation.     

基于MAS协作机制的动态计算资源调度模型

针对网格环境的自治性、动态性、分布性和异构性等特征.提出基于多智能体系统(Mutil Agent System,MAS)博弈协作的资源动态分配和任务调度模型,建立了能够反映供求关系的网格资源调度模型和任务求解算法,证明了资源分配博弈中Nash均衡点的存在性、唯一性和Nash均衡解,该方法能够利用消费者agent的学习和协商能力,考虑和引入消费者的心理行为,使得消费者的资源申请和任务调度具有较高的合理性和有效性.实验结果表明,资源调度算法不但可以有效减少不必要的延迟,而且在响应时间的平滑性、吞吐率及资源利用率方面比传统算法要好,从而使得整个资源的供需合理、负载均衡.     

Periodic load balancing

Multiprocessor load balancing aims to improve performance by moving jobs from highly loaded processors to more lightly loaded processors. Some schemes allow only migration of new jobs upon arrival, while other schemes allow migration of jobs in progress. A difficulty with all these schemes, however, is that they require continuously maintaining detailed state information. In this paper we consider the alternative of periodic load balancing, in which the loads are balanced only at each T time units for some appropriate T. With periodic load balancing, state information is only needed at the balancing times. Moreover, it is often possible to use slightly stale information collected during the interval between balancing times. In this paper we study the performance of periodic load balancing. We consider multiple queues in parallel with unlimited waiting space to which jobs come either in separate independent streams or by assignment (either random or cyclic) from a single stream. Resource sharing is achieved by periodically redistributing the jobs or the work in the system among the queues. The performance of these systems of queues coupled by periodic load balancing depends on the transient behavior of a single queue. We focus on useful approximations obtained by considering a large number of homogeneous queues and a heavy load. When the number of queues is sufficiently large, the number of jobs or quantity of work at each queue immediately after redistribution tends to evolve deterministically, by the law of large numbers. The steady-state (limiting) value of this deterministic sequence is obtained as the solution of a fixed point equation, where the initial value is equal to the expected transient value over the interval between successive redistributions conditional on the initial value. A refined approximation based on the central limit theorem is a normal distribution, where the mean and variance are obtained by solving a pair of fixed-point equations. With higher loads, which is natural to consider when load balancing is performed, a heavy-traffic limit theorem shows that one-dimensional reflected Brownian motion can be used to approximately describe system performance, even with general arrival and service processes. With these approximations, we show how performance depends on the assumed arrival pattern of jobs and the model parameters. We do numerical calculations and conduct simulation experiments to show the accuracy of the approximations. This revised version was published online in June 2006 with corrections to the Cover Date.     

基于移动Agent技术的网格负载均衡调度方法

为了实现网格计算资源的动态自适应性管理和负载均衡调度,将移动Agent技术引入网格资源管理,并提出了一种基于Agent的网格资源调度模型.在该模型基础上,采用遗传克隆算法解决网格计算中的任务调度问题.仿真实验表明该算法不仅充分发挥了Agent的智能性、自主性,还具有良好的扩展性,提高了网格资源调度的效率.     

并行间断有限元算法求解Navier-Stokes方程

间断Galerkin有限元方法非常适合在非结构网格上高精度求解Navier-Stokes方程,然而其十分耗费计算资源.为了提高计算效率,提出了高效的MIMD并行算法.采用隐式时间离散GMRES+LU SGS格式,结合多重网格方法,当地时间步长加速算法收敛.为了保证各处理器间负载平衡,采用区域分解二级图方法划分网格,实现内存合理分配,数据只在相邻处理器间传递.数值模拟了RAE2822翼型和M6黏性绕流,加速比基本呈线性变化且接近理想值.结果表明了该算法能有效减少计算时间、合理分配内存,具有较高的加速比和并行效率,适合于MIMD粗粒度科学计算.     

分布式数据流处理系统管理中负载均衡问题建模与求解

对分布式数据流处理系统管理中,处理节点负载均衡问题进行了研究。阐述了分布式数据流处理系统的运行机理以及节点负载不均衡的成因,并提出了对系统负载均衡调整的优化方案;对提出的优化方案建立模型,并对模型的适用条件进行理论分析;然后采用蚁群算法对模型进行求解,并针对分布式数据流处理系统实时性的需求对算法进行改进;最后用实验证明本文所建立的模型及其求解方法对于解决分布式数据流处理系统管理中节点负载均衡问题的有效性。     

Promoting cooperation in selfish computational grids

In distributed computing, the recent paradigm shift from centrally-owned clusters to organizationally distributed computational grids introduces a number of new challenges in resource management and scheduling. In this work, we study the problem of Selfish Load Balancing which extends the well-known load balancing (LB) problem to scenarios in which each processor is concerned only with the performance of its local jobs. We propose a simple mathematical model for such systems and a novel function for computing the cost of the execution of foreign jobs. Then, we use the game-theoretic framework to analyze the model in order to compute the expected result of LB performed in a grid formed by two clusters. We show that, firstly, LB is a socially-optimal strategy, and secondly, for similarly loaded clusters, it is sufficient to collaborate during longer time periods in order to make LB the dominant strategy for each cluster. However, we show that if we allow clusters to make decisions depending on their current queue length, LB will never be performed. Then, we propose a LB algorithm which balances the load more equitably, even in the presence of overloaded clusters. Our algorithms do not use any external forms of compensation (such as money). The load is balanced only by considering the parameters of execution of jobs. This analysis is assessed experimentally by simulation, involving scenarios with multiple clusters and heterogeneous load.     

中央空调系统节能设计

根据商场的冷负荷平衡公式,求得商场的平均客流量;利用数据拟合、插值等算法对商场的逐时客流量进行了优化,并建立了以人流量和外部环境温度为变量的商场冷负荷模型;采用模糊控制策略建立了保持商场内部温度稳定的数学模型,可以实现对中央空调系统供冷量的逐时控制;运用微积分原理建立了基准冷负荷的数学模型,为直接基于基准能耗的控制策略提供了可行性依据.     

An efficient algorithm for finding the largest chain graph according to a given chain graph

Chain graph (CG) is a general model of graphical Markov models. Some different chain graphs may describe the same conditional independence structure, then we say that these CGs are Markov equivalent. In 1990 Frydenberg showed that every class of Markov equivalent CGs has a CG which is called the largest chain graph with the greatest number of lines. This paper presents an efficient algorithm for finding the largest chain graph of the corresponding Markov equivalent class of a given CG. The computational complexity of the algorithm is O(n3). It is more efficient than the complexity O(n!) of the present algorithms. Also a more intuitive graphical characterization of the largest chain graph is provided based on the algorithm in this paper.     

On estimating workload in interval branch-and-bound global optimization algorithms

In general, solving Global Optimization (GO) problems by Branch-and-Bound (B&B) requires a huge computational capacity. Parallel execution is used to speed up the computing time. As in this type of algorithms, the foreseen computational workload (number of nodes in the B&B tree) changes dynamically during the execution, the load balancing and the decision on additional processors is complicated. We use the term left-over to represent the number of nodes that still have to be evaluated at a certain moment during execution. In this work, we study new methods to estimate the left-over value based on the observed amount of pruning. This provides information about the remaining running time of the algorithm and the required computational resources. We focus on their use for interval B&B GO algorithms.     

E-science infrastructures for molecular modeling and parametrization

E-science infrastructures are becoming the essential tools for computational scientific research. In this paper, we describe two e-science infrastructures: Science and Engineering Applications Grid (SEAGrid) and molecular modeling and parametrization (ParamChem). The SEAGrid is a virtual organization with a diverse set of hardware and software resources and provides services to access such resources in a routine and transparent manner. These essential services include allocations of computational resources, client-side application interfaces, computational job and data management tools, and consulting activities. ParamChem is another e-science project dedicated for molecular force-field parametrization based on both ab-initio and molecular mechanics calculations on high performance computers (HPCs) driven by scientific workflow middleware services. Both the projects share a similar three-tier computational infrastructure that consists of a front-end client, a middleware web services layer, and a remote HPC computational layer. The client is a Java Swing desktop application with components for pre- and post-data processing, communications with middleware server and local data management. The middleware service is based on Axis2 web service and MySQL relational database, which provides functionalities for user authentication and session control, HPC resource information collections, discovery and matching, job information logging and notification. It can also be integrated with scientific workflow to manage computations on HPC resources. The grid credentials for accessing HPCs are delegated through MyProxy infrastructure. Currently SEAGrid has integrated several popular application software suites such as Gaussian for quantum chemistry, NAMD for molecular dynamics and engineering software such as Abacus for mechanical engineering. ParamChem has integrated CGenFF (CHARMM General Force-Field) for molecular force-field parametrization of drug-like molecules. Long-term storage of user data is handled by tertiary data archival mechanisms. SEAGrid science gateway serves more than 500 users while more than 1000 users use ParamChem services such as atom typing and initial force-field parameter guess at present.     

A case study of an adaptive load balancing algorithm

In this paper we present an effective load balancing algorithm for a multi-processor architecture designed for the real time switching of telephone calls. By modifying an algorithm developed for an abstract queueing model, which is of independent interest by itself, we propose a hybrid load balancing algorithm and study its performance in a simulation test-bed. This case study demonstrates how simple abstractions and theoretically intractable but intuitively appealing ideas can be combined to effectively solve a real problem.     

Computational Strategies for Multivariate Linear Mixed-Effects Models With Missing Values

This article presents new computational techniques for multivariate longitudinal or clustered data with missing values. Current methodology for linear mixed-effects models can accommodate imbalance or missing data in a single response variable, but it cannot handle missing values in multiple responses or additional covariates. Applying a multivariate extension of a popular linear mixed-effects model, we create multiple imputations of missing values for subsequent analyses by a straightforward and effective Markov chain Monte Carlo procedure. We also derive and implement a new EM algorithm for parameter estimation which converges more rapidly than traditional EM algorithms because it does not treat the random effects as “missing data,” but integrates them out of the likelihood function analytically. These techniques are illustrated on models for adolescent alcohol use in a large school-based prevention trial.     

Strong stability of Nash equilibria in load balancing games

We study strong stability of Nash equilibria in load balancing games of m(m 2)identical servers,in which every job chooses one of the m servers and each job wishes to minimize its cost,given by the workload of the server it chooses.A Nash equilibrium(NE)is a strategy profile that is resilient to unilateral deviations.Finding an NE in such a game is simple.However,an NE assignment is not stable against coordinated deviations of several jobs,while a strong Nash equilibrium(SNE)is.We study how well an NE approximates an SNE.Given any job assignment in a load balancing game,the improvement ratio(IR)of a deviation of a job is defined as the ratio between the pre-and post-deviation costs.An NE is said to be aρ-approximate SNE(ρ1)if there is no coalition of jobs such that each job of the coalition will have an IR more thanρfrom coordinated deviations of the coalition.While it is already known that NEs are the same as SNEs in the 2-server load balancing game,we prove that,in the m-server load balancing game for any given m 3,any NE is a(5/4)-approximate SNE,which together with the lower bound already established in the literature yields a tight approximation bound.This closes the final gap in the literature on the study of approximation of general NEs to SNEs in load balancing games.To establish our upper bound,we make a novel use of a graph-theoretic tool.     

Smoothed analysis of balancing networks

In a balancing network each processor has an initial collection of unit‐size jobs (tokens) and in each round, pairs of processors connected by balancers split their load as evenly as possible. An excess token (if any) is placed according to some predefined rule. As it turns out, this rule crucially affects the performance of the network. In this work we propose a model that studies this effect. We suggest a model bridging the uniformly‐random assignment rule, and the arbitrary one (in the spirit of smoothed‐analysis). We start with an arbitrary assignment of balancer directions and then flip each assignment with probability α independently. For a large class of balancing networks our result implies that after $\mathcal{O}(\log n)$ rounds the discrepancy is $\mathcal{O}( (1/2-\alpha) \log n + \log \log n)$ with high probability. This matches and generalizes known upper bounds for α = 0 and α = 1/2. We also show that a natural network matches the upper bound for any α. © 2010 Wiley Periodicals, Inc. Random Struct. Alg., 39, 115–138, 2011     

Efficient MCMC estimation of inflated beta regression models

This paper introduces a new and computationally efficient Markov chain Monte Carlo (MCMC) estimation algorithm for the Bayesian analysis of zero, one, and zero and one inflated beta regression models. The algorithm is computationally efficient in the sense that it has low MCMC autocorrelations and computational time. A simulation study shows that the proposed algorithm outperforms the slice sampling and random walk Metropolis–Hastings algorithms in both small and large sample settings. An empirical illustration on a loss given default banking model demonstrates the usefulness of the proposed algorithm.     

A comparison of two algorithms for the assignment problem

State-of-the-art computational results have shown that the shortest augmenting path (SAP) methods are more efficient than other primal-dual and primal-simplex based methods for solving the linear assignment problem on uniprocessor computers. There is, however, some controversy concerning their merits when compared with Bertsekas' auction algorithm on multiprocessor computers. In this study we investigate the performance of these competing methods on the Alliant FX/8. For each method, theoretical motivation, sources of parallelism and computational results are presented.     

Transient and Asymptotic Analysis of General Markov Fluid Models

In this paper, transient and asymptotic behaviors of general Markov fluid models are studied and analyzed. The input and output rates are assumed to be modulated by a finite state irreducible Markov process, which can admit states with zero effective input rate. The main advantage of the proposed methods is their accuracy and their numerical stability. For the transient solution, properties of stationary detection lead to reduce considerably the computational complexity of the algorithm. As for the asymptotic solution, it is derived from the transient one's. We apply these methods to a general Markov fluid model and we interpret the numerical results.     

Parallel anisotropic mesh adaptivity with dynamic load balancing for cardiac electrophysiology

Simulations in cardiac electrophysiology generally use very fine meshes and small time steps to resolve highly localized wavefronts. This expense motivates the use of mesh adaptivity, which has been demonstrated to reduce the overall computational load. However, even with mesh adaptivity performing such simulations on a single processor is infeasible. Therefore, the adaptivity algorithm must be parallelised. Rather than modifying the sequential adaptive algorithm, the parallel mesh adaptivity method introduced in this paper focuses on dynamic load balancing in response to the local refinement and coarsening of the mesh. In essence, the mesh partition boundary is perturbed away from mesh regions of high relative error, while also balancing the computational load across processes. The parallel scaling of the method when applied to physiologically realistic heart meshes is shown to be good as long as there are enough mesh nodes to distribute over the available parallel processes. It is shown that the new method is dominated by the cost of the sequential adaptive mesh procedure and that the parallel overhead of inter-process data migration represents only a small fraction of the overall cost.     

An organization of sparse gauss elimination for solving partial differntial equations on distributed memory machines

A computational arrangement of Gauss elimination is presented for solving sparse, nonsymmetric linear systems arising from partial differential equation problems. It is particularly targeted for use on distributed memory message passing multiprocessor computers and it is presented and analyzed in this context. The objective of the algorithm is to exploit the sparsity (i.e., reducing computation, communication, and memory requirements) and to optimize the data structure manipulation overhead. The algorithm is based on the nested dissection approach, which starts with a large set of very sparse, completely independent subsystems and progresses in stages to a single, nearly dense system at the last stage. The computational efforts of each stage are roughly equal (almost exactly equal for model problems), yet the data structures appropriate for the first and last stages are quite different. Thus we use different types of data structures and algorithm components at different stages of the solution. The new organization is a combination of previous techniques including nested dissection, implicit block factorization, domain decomposition, fan-in, fan-out, up-looking, down-looking, and dynamic data structures. © 1993 John Wiley & Sons, Inc.