混合复杂网络拓扑结构是大规模数据中心组件订购的首选
Hybrid complex network topologies are preferred for component-subscription in large-scale data-centres
摘要：
我们报告的实验探索在一个大规模的数据中心的依赖组件的复杂网络的拓扑结构之间的相互作用，该数据中心的鲁棒性和缩放特性。在之前的论文[1]中，我们使用SPECI大规模数据中心模拟器[2]来比较数据中心的鲁棒性和缩放特性，这些数据中心的相关组件通过Strogatz-Watts小世界（SW）网络[3]连接，而那些组织为Barabasi-Albert无标度（SF）网络[4]，并发现显着差异。在本文中，我们提出了使用Klemm-Eguiliz（KE）构造方法[5]生成数据中心组件依赖的复杂网络拓扑的结果。KE模型具有控制参数μ \in [0，1] \inR，其确定所生成的网络是SW（0<μ<<1）还是SF（μ=1）还是SW和SF之间的“混合”网络拓扑（0<μ<1）。我们发现，模拟数据中心的系统级性能指标的最佳分数是由μ的“混合”值显着不同于纯SW或纯SF。
英文摘要：
We report on experiments exploring the interplay between the topology of the complex network of dependent components in a large-scale data-centre, and the robustness and scaling properties of that data-centre. In a previous paper [1] we used the SPECI large-scale data-centre simulator [2] to compare the robustness and scaling characteristics of data-centres whose dependent components are connected via Strogatz-Watts small-world (SW) networks [3], versus those organized as Barabasi-Albert scale-free (SF) networks [4], and found significant differences. In this paper, we present results from using the Klemm-Eguiliz (KE) construction method [5] to generate complex network topologies for data-centre component dependencies. The KE model has a control parameter μ\in[0,1]\inR that determines whether the networks generated are SW (0<μ<<1) or SF (μ=1) or a "hybrid" network topology part-way between SW and SF (0<μ<1). We find that the best scores for system-level performance metrics of the simulated data-centres are given by "hybrid" values of μ significantly different from pure-SW or pure-SF.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
