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Conference Papers Year : 2020

RHODA Topology Configuration Using Bayesian Optimization

Maotong Xu
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  • PersonId : 1096174
Min Tian
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  • PersonId : 1096175
Eytan Modiano
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Suresh Subramaniam
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  • PersonId : 1096176


The rapid growth of data center traffic requires data center networks (DCNs) to be scalable, energy-efficient, and provide low latencies. Optical Wavelength Division Multiplexing (WDM) is a promising technique to build data centers comprising millions of servers. In [24], a WDM-based Reconfigurable Hierarchical Optical DCN Architecture (RHODA) was presented, which can accommodate up to 10+ million of servers and a variety of traffic patterns. RHODA also saves tremendous amounts of power and cost through its extensive use of passive optical devices, and minimal use of power-hungry and costly devices. RHODA achieves high throughput through reconfigurable clustering of racks of servers. In this paper, we focus on the design of the cluster topology (also called inter-cluster network). Given the pair-wise cluster traffic, our objective for the cluster topology is to minimize the average hop length. In [24], a simple variant of the Hungarian algorithm that maximizes the one-hop or direct traffic among clusters was used. In this paper, we leverage the Bayesian Optimization (BO) framework and propose a fast algorithm to minimize the average number of hops in the inter-cluster network of RHODA. To the best of our knowledge, this is the first paper that employs BO to optimize optical DCN performance. We present our design decisions and modifications to BO based on the network constraints. Results show that BO can achieve optimal or near-optimal results, and outperforms a well-known regular topology (Gemnet) and the Hungarian-based method by up to $$13\%$$ and $$58\%$$, respectively.
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Dates and versions

hal-03200639 , version 1 (16-04-2021)





Maotong Xu, Min Tian, Eytan Modiano, Suresh Subramaniam. RHODA Topology Configuration Using Bayesian Optimization. 23th International IFIP Conference on Optical Network Design and Modeling (ONDM), May 2019, Athens, Greece. pp.130-141, ⟨10.1007/978-3-030-38085-4_12⟩. ⟨hal-03200639⟩
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