Abstract
In this paper, we explore the issue of static routing and spectrum/IT resource assignment (RSIA) of elastic all-optical switched intra-datacenter networks (intra-DCNs) by proposing anycast- and manycast-based integer linear programming (ILP) models. The objective is to jointly optimize the DCN resources, i.e., network transmission bandwidth and IT resources, under different situations. First, for given service-request matrices with unknown network transmission bandwidth and IT resources, we propose anycast and manycast ILP models to minimize the maximum numbers of required network and IT resources to accommodate all the service requests. For anycast RSIA issue, we proposed two different ILP models that are based on node-arc and link-path methods, respectively. Node-arc based manycast ILP model is also proposed for the first time to our knowledge. Second, for given network transmission bandwidth and IT resources and known service-request matrices, we propose node-arc based anycast ILP models to maximize the total number of successfully served service requests. To evaluate the efficiency of anycast and manycast models, all proposed ILP models are evaluated and compared with unicast ILP models. Simulation results show that anycast and manycast ILP models perform much better in efficiently using DCN resources and successfully accommodating more service requests when compared to unicast ILP models under the same network conditions.
创新点
此文通过提出多种基于Anycast和Manycast的整数线性规划(ILP)模型,探讨基于弹性全光交换技术的数据中心内网的静态路由和资源分配。目标是在不同的网络环境下,联合优化数据中心网络资源,例如传输资源和IT资源。首先,在服务请求矩阵已知但数据中心网络资源未知的情况下,提出Anycast和Manycast的ILP模型以最小化需要的联合网络和IT资源。对于Anycast的RSIA问题,分别提出基于node-arc和link-path的两种ILP模型。其次,在服务请求矩阵和数据中心网络资源均已知的情况下,提出基于node-arc的Anycast整数线性规划模型以最大化数据中心内网能成功服务的请求。为了验证所提Anycast和Manycast整数线性规划模型的有效性,所有提出的模型都和传统的unicast整数线性规划模型做了比较。仿真结果显示在同样的数据中心网络环境下,基于Anycast和Manycast的整数线性规划模型要比基于unicast的整数线性规划模型更高效的利用数据中心网络资源和服务数据中心请求。
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Peng, L., Park, K. & Youn, CH. Investigation on static routing and resource assignment of elastic all-optical switched intra-datacenter networks. Sci. China Inf. Sci. 59, 102304 (2016). https://doi.org/10.1007/s11432-016-0312-7
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DOI: https://doi.org/10.1007/s11432-016-0312-7