基于MIPS的流媒体流量均衡设备的设计

针对中小型园区网视频点皤VOD(Video on Demand)应用中因网络带宽及点播服务器I/O带宽不足导致的系统阻塞问题,提出一种新型网络设备--流媒体流量均衡设备.该设备基于MIPS处理器,操作系统采用Linux,具有成本低廉、功耗低、易于安装部署等优点,基于该设备的VOD方案实用性强、性价比高、可伸缩性好.试运行证明该设备的使用效果良好,具有较高的经济可行性,可广泛应用于中小型园区网VOD系统的建设与改造.     

一种视频服务器的负载均衡方法

针对VOD系统中视频服务器的瓶颈问题，提出了一种视频服务器负载均衡的方法。该方法解决了多个服务器协同工作及对外服务的问题，使得每个点播中心可以有效地服务大量用户。     

分布存储VOD系统的负载均衡设计及其仿真

为了满足日益增长的VOD用户数量,设计了一种多服务器组成的分布式VOD系统,系统中服务器的负载均衡问题成为亟待解决的问题.为了解决负载均衡问题.从用户行为分析入手,建立了用户行为的数学模型.根据用户的节目选择分布以及点播时长分布,制定了媒体文件按时间分块并分布存储在多服务器上的策略,并根据热度信息的变化,每隔一段时间对各服务器上存储的内容进行重发布,以此达到各服务器的负载均衡.实现了仿真系统,并设计出用户行为仿真程序进行用户点播行为仿真.仿真实验结果显示,使用该重发布策略后,各服务器达到很好的负载均衡,说明策略是有效的.     

基于模糊聚类的VOD代理服务调度算法

提出一种面向园区网VOD代理系统的服务调度算法.基于模糊聚类思想将VOD点播热门节目及系统中的代理服务器进行混合聚类,再根据聚类结果从资源利用的角度为每个热门节目调度最恰当的代理服务器以进行服务,从而实现了单个代理服务器内部各项资源的均衡利用以及全局范围内所有代理服务器的负载均衡.实验结果表明,该算法具有良好性能,可在不升级应用框架及硬件基础设施的基础上提高系统的运行效率.     

视频监控系统中的VOD负载均衡改进算法

在传统轻负载优先调度负载均衡算法中,存在用户点播响应时延长且负载均衡度不高的问题。为解决上速问题,提出一种静态负载调度和动态负载迁移相结合的负载均衡算法。静态调度算法采用基于视频点播(VOD)相似度的轻负载优先算法,将相似的点播请求调度至相同的视频点播上,提高VOD的缓存命中率,以缩短点播响应时延。动态负载迁移算法采用基于缓存考虑的REM负载迁移算法,将负载由高载VOD迁移到低载VOD上,以提高负载均衡程度。实验结果表明,在典型城域视频监控系统500路点播规模下,提出的负载均衡算法与传统轻负载优先算法相比,能够缩短17.5%的点播延迟时间,降低53.4%的集群负载方差,提高了系统的负载均衡度。     

基于并行遗传算法的分布式VOD系统负载均衡研究

在大型VOD(Video On Demand)系统中,服务器需要响应来自用户的大量并发请求,不仅视频服务器成为系统的瓶颈,负责负载均衡的应用服务器也日益成为系统的瓶颈.基于分布式VOD系统的结构,采用并行遗传算法对大型分布式VOD系统的负载均衡进行了研究,提出并实现了一种基于并行遗传算法的分布式VOD系统负载均衡调度算法.实验结果表明,与不采用负载均衡和采用串行遗传算法的负载均衡调度算法相比,该算法能够显著提高VOD系统的处理性能和响应能力.     

基于统计学模型的VOD负载均衡设计

现有的负载均衡算法大都基于Web服务,并不适用于视频点播(VOD)。而基于VOD的负载均衡算法没有考虑用户操作以及一个节目不同片段的访问概率差异等因素对系统负载的影响。因此,设计一种基于统计学模型的负载均衡设计方法,从用户的随机性操作出发,统计分析其规律,根据不同节目不同片段的不同点播率对片源进行分块备份存储。通过仿真实验证实了算法较其他算法的优越性,提高了各节点服务器资源利用率,达到了负载均衡的目的。     

基于集群的VOD系统视频服务器的负载均衡算法改进

文章针对VOD视频服务中不同的点播方式和视频文件存储调度的特点等问题,改进了三种负载均衡算法-加权最少连接算法、视频服务节点负载排名算法以及视濒文件播放信息算法。     

基于静态路由的ISP负载均衡解决方案

传统的ISP负载均衡方法主要有2种,一是使用开启BGP协议的高端防火墙;二是使用专业负载均衡设备.本论文建立了1套新型的基于静态路由的ISP负载均衡解决方案.本文首先通过Whois协议获得ISP的AS号码,在公网BGP路由器数据库中根据这个AS号码查询属于不同ISP的全部BGP路由条目;之后将不同ISP(例如中国电信和中国联通)的路由条目导入园区网边界防火墙,使得用户访问实现负载分担和路由备份.本方案适用于大中型企业和大型居民小区园区网,相比传统的负载均衡方法,本方案能够节省大量设备投资.     

基于关系矩阵编码的粒子群负载均衡算法研究

针对网络流量负载均衡与优化问题,提出了一种基于关系矩阵编码的粒子群负载均衡算法.给出一种采用关系矩阵作为编码方法的粒子群算法来处理网络负载均衡问题,能够使网络流量能较好的分担到不同链路上.仿真结果表明,该算法取得了较好的流量负载分担效果及较小的时间花费.     

Hydrodynamic load balancing

This paper presents a hydrodynamic framework to solving the dynamic load balancing problem in heterogeneous distributed systems. In this approach, each processor is viewed as a liquid cylinder where the cross-sectional area corresponds to the capacity of the processor, the communication links are modeled as liquid channels between the cylinders, the workload is represented by liquid, and the load balancing algorithm manages the flow of the liquid. It is proven that all algorithms under this framework converge geometrically to the state of equilibrium, in which the heights of the liquid columns are the same in all the cylinders. In this way, each processor obtains an amount of workload proportional to its capacity. A hydrodynamic algorithm is presented and its performance is evaluated. The algorithm is applied to solve several practical applications to demonstrate the applicability of the framework     

Optimal aircraft load balancing

The Canadian Forces employ cargo aircraft for countless missions to deliver equipment, supplies and passengers. Maximizing the payload while maintaining a safe load balance is of high importance. This paper details a mathematical formulation, a Mixed Integer Linear Program model, to solve the problem of determining the arrangement of a set of items in a cargo hold that optimizes the load balance. Items are modelled as rectangles with specified dimensions, mass and centre of gravity offsets. The main decision variables determine the orientation and placement of a given set of items. The objective function can be chosen to minimize deviation of the centre of gravity from the target position or to maximize a function of the items loaded (cardinality, priority, etc.). The formulation models item rotation, spacing requirements, load ordering, macro items, obstacles and constrained placement. Furthermore, specialized cut and transitivity constraints are developed that limit the solution search space.     

Value driven load balancing

To date, the study of dispatching or load balancing in server farms has primarily focused on the minimization of response time. Server farms are typically modeled by a front-end router that employs a dispatching policy to route jobs to one of several servers, with each server scheduling all the jobs in its queue via Processor-Sharing. However, the common assumption has been that all jobs are equally important or valuable, in that they are equally sensitive to delay. Our work departs from this assumption: we model each arrival as having a randomly distributed value parameter, independent of the arrival’s service requirement (job size). Given such value heterogeneity, the correct metric is no longer the minimization or response time, but rather, the minimization of value-weighted response time. In this context, we ask “what is a good dispatching policy to minimize the value-weighted response time metric?” We propose a number of new dispatching policies that are motivated by the goal of minimizing the value-weighted response time. Via a combination of exact analysis, asymptotic analysis, and simulation, we are able to deduce many unexpected results regarding dispatching.     

Fair online load balancing

We revisit from a fairness point of view the problem of online load balancing in the restricted assignment model and the 1-∞ model. We consider both a job-centric and a machine-centric view of fairness, as proposed by Goel et al. (In: Symposium on discrete algorithms, pp. 384–390, 2005). These notions are equivalent to the approximate notion of prefix competitiveness proposed by Kleinberg et al. (In: Proceedings of the 40th annual symposium on foundations of computer science, p. 568, 2001), as well as to the notion of approximate majorization, and they generalize the well studied notion of max-min fairness. We resolve a question posed by Goel et al. (In: Symposium on discrete algorithms, pp. 384–390, 2005) proving that the greedy strategy is globally O(log?m)-fair, where m denotes the number of machines. This result improves upon the analysis of Goel et al. (In: Symposium on discrete algorithms, pp. 384–390, 2005) who showed that the greedy strategy is globally O(log?n)-fair, where n is the number of jobs. Typically, n?m, and therefore our improvement is significant. Our proof matches the known lower bound for the problem with respect to the measure of global fairness. The improved bound is obtained by analyzing, in a more accurate way, the more general restricted assignment model studied previously in Azar et al. (J. Algorithms 18:221–237, 1995). We provide an alternative bound which is not worse than the bounds of Azar et al. (J. Algorithms 18:221–237, 1995), and it is strictly better in many cases. The bound we prove is, in fact, much more general and it bounds the load on any prefix of most loaded machines. As a corollary from this more general bound we find that the greedy algorithm results in an assignment that is globally O(log?m)-balanced. The last result generalizes the previous result of Goel et al. (In: Symposium on discrete algorithms, pp. 384–390, 2005) who proved that the greedy algorithm yields an assignment that is globally O(log?m)-balanced for the 1-∞ model.     

Self-stabilizing smoothing and balancing networks

A smoothing network is a distributed data structure that accepts tokens on input wires and routes them to output wires. It ensures that however imbalanced the traffic on input wires, the numbers of tokens emitted on output wires are approximately balanced. Prior work on smoothing networks always assumed that such networks were properly initialized. In a real distributed system, however, network switches may be rebooted or replaced dynamically, and it may not be practical to determine the correct initial state for the new switch. Prior analyses do not work under these new assumptions. This paper makes the following contributions. First, we show that some well-known 1-smoothing networks, known as counting networks, when started in an arbitrary initial state (perhaps chosen by an adversary), remain remarkably smooth, degrading from 1-smooth to (log n)-smooth, where n is the number of input/output wires. For the networks that we consider, we show that the above (log n) bound for the smoothness is tight. Our second contribution is to show how any balancing network can be made self-stabilizing with the addition of local stabilization actions and state, which restore the network back to a “legal state” even if it starts out in an illegal state. A preliminary version of this work appeared in the Proceedings of The 23rd International Conference on Distributed Computing Systems, Providence, Rhode Island, USA.     

The ALPACA line balancing system

The ALPACA line balancing system is an interactive computer-based system for the planning and control of assembly lines upon which mixed models with a large option mix are to be assembled. The ALPACA system facilities the assignment of work to operators along the assembly line and provides immediate evaluations of the effects of the assignment of work. Work is assigned and initially evaluated on the basis of forecasted model mix and option mix percentages. The operator's work assignment is further evaluated by passing an actual vehicle build sequence of models complete with all specified options past the operation (via a simulation) to highlight any extreme overcycle or idle conditions which are not apparent from average values based on the forecasted model mix and option mix percentages. ALPACA gives the industrial engineer and the line supervisor, working together as a team, the ability to balance an assembly line quickly and react rapidly to product engineering changes and/or schedule changes.     

Load balancing in project assignment

In this paper, we study a project assignment problem. Specifically, a set of projects, each of which needs to be finished over a project development cycle, are to be assigned to a group of identical engineers over a discrete planning horizon. The workload of the projects is different and fluctuates over their development cycles. In each period, engineers have a maximum allowed workload. The objective of the problem is to assign the projects to engineers with the objective of balancing the total workload among the engineers; the load balance is measured by the difference between the maximum and the minimum total workload. Such a problem is new to the literature. After proving the problem is strongly NP-hard, we propose a two-stage heuristic approach to solve it. Extensive numerical experiments show that the proposed approach can achieve optimal or nearly optimal solutions for all test cases; such performance is much better than what can be obtained from an IP model solved with ILOG CPLEX. An analysis of the algorithm has also been provided to explain how the superior performance has been achieved.     

Duple-EDA and sample density balancing

In this paper,a new method is proposed to overcome the problem of local optima traps in a class of evolutionary algorithms,called estimation of distribution algorithms(EDAs) ,in real-valued function optimization. The Duple-EDA framework is proposed in which not only the current best solutions but also the search history are modeled,so that long-term feedback can be taken into account. Sample Density Balancing(SDB) is proposed under the framework to alleviate the drift phenomenon in EDA. A selection scheme b...