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Balanced overlay networks (BON): Decentralized load balancing and analysis of large-scale systems.

机译:平衡覆盖网络(BON):分散的负载平衡和大型系统的分析。

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We present a self-organizing distributed computing architecture that encodes the information about each node's available computational resources in the structure of the links connecting the nodes in the network. Each node's degree is kept proportional to its free resources by adding or deleting edges when node resources are freed or consumed. The result of this local edge rewiring algorithm is that short, O(logN), random walks preferentially sample nodes with high degree and thus high node resources. Different variations of this algorithm produce Erdos-Renyi random graphs, nearly d-regular random graphs and random power-law graphs. What each variation has in common is that each node bears a load that is proportional to its resources, which is a useful load-balancing metric for any resource distribution. There have been prior studies on using random walks to distribute jobs, but BON distinguishes itself by continually reshaping the network structure to permit efficient node sampling using short random walks.; Some of the most recent distributed computing systems have been based on distributed hash tables (DHTs). We have developed a tool called the reachable component method (RCM) to evaluate the scalability and performance of these types of systems. RCM is a generally-applicable technique for predicting structured DHT routing performance in the presence of random failures. Here we apply this method to seven popular DHT architectures and find that some systems are scalable under random failures and some are not. DHT routing algorithms that continue to function under a non-zero rate of random failures as N → infinity are scalable. Unscalable DHT algorithms are unable to route at all for any non-zero node failure rate as N → infinity. These predictions enable the effective planning and use of DHT systems to ensure a desired quality of service.
机译:我们提出了一种自组织的分布式计算体系结构,该体系结构在连接网络中各节点的链接结构中编码了有关每个节点的可用计算资源的信息。当节点资源被释放或消耗时,通过添加或删除边来使每个节点的度数与其自由资源成比例。这种局部边缘重布线算法的结果是,短的O(logN)随机游走优先选择具有高度并因此具有高节点资源的节点。该算法的不同变体产生Erdos-Renyi随机图,近d-正则随机图和随机幂律图。每个变体的共同点在于,每个节点承受的负载与其资源成正比,这对于任何资源分配而言都是有用的负载平衡指标。先前已有关于使用随机游走来分配工作的研究,但是BON通过不断地重塑网络结构以允许使用短随机游走来进行有效的节点采样来区分自己。一些最新的分布式计算系统已经基于分布式哈希表(DHT)。我们已经开发了一种称为可达组件方法(RCM)的工具,用于评估这些类型的系统的可伸缩性和性能。 RCM是在出现随机故障时预测结构化DHT路由性能的通用技术。在这里,我们将这种方法应用于七种流行的DHT架构,发现有些系统在随机故障下可以扩展,而有些则不能。当N→无穷大时,在非零随机故障率下继续起作用的DHT路由算法是可扩展的。对于N→无穷大,不可扩展的DHT算法根本无法为任何非零节点故障率进行路由。这些预测可以有效地规划和使用DHT系统,以确保所需的服务质量。

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