数据中心网络拓扑效率的算法研究

IF 3.6 3区 医学 Q2 NEUROSCIENCES Network Neuroscience Pub Date : 2023-06-26 DOI:10.3390/network3030015
P. Roig, S. Alcaraz, K. Gilly, Cristina Bernad, C. Juiz
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引用次数: 1

摘要

由于物联网部署的快速增长,数据中心受到越来越多的关注,这可能导致更靠近最终用户的小型设施的实施以及云中的大型设施的实施。本文进行了一项算法研究,以测量与节点之间的平均跳数和设备之间的平均链路数相关的系数,适用于数据中心的一系列典型网络拓扑结构。这样的拓扑要么是树状设计,要么是图状设计,其中该系数在性能和简单性之间提供了平衡,导致系数值较低,从而在冗余架构中更好地折衷这两个因素。这个贡献的动机是通过应用简单的算术运算来创建一个易于计算的系数。该系数可以看作是比较数据中心网络拓扑的另一种工具,可以作为决定性因素,以便在其他参数提供矛盾结果时选择给定的设计。
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Arithmetic Study about Efficiency in Network Topologies for Data Centers
Data centers are getting more and more attention due the rapid increase of IoT deployments, which may result in the implementation of smaller facilities being closer to the end users as well as larger facilities up in the cloud. In this paper, an arithmetic study has been carried out in order to measure a coefficient related to both the average number of hops among nodes and the average number of links among devices for a range of typical network topologies fit for data centers. Such topologies are either tree-like or graph-like designs, where this coefficient provides a balance between performance and simplicity, resulting in lower values in the coefficient accounting for a better compromise between both factors in redundant architectures. The motivation of this contribution is to craft a coefficient that is easy to calculate by applying simple arithmetic operations. This coefficient can be seen as another tool to compare network topologies in data centers that could act as a tie-breaker so as to select a given design when other parameters offer contradictory results.
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来源期刊
Network Neuroscience
Network Neuroscience NEUROSCIENCES-
CiteScore
6.40
自引率
6.40%
发文量
68
审稿时长
16 weeks
期刊最新文献
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