Abstract

Network interconnection critically impacts the performance of data centers (DCs) and high-performance computing (HPC) systems, with scalability becoming vital as computing demands grow. This necessitates interconnection architectures that meet stringent latency, bandwidth, cost, and power consumption requirements. Optical interconnections provide cost-efficiency, reduced power consumption, and scalability to fulfill bandwidth needs. However, optical switches lack optical buffers, complicating the operation of all optical networks. To this end, we propose HiveNet, a novel hybrid interconnect architecture based on dual-port nodes and arrayed waveguide grating routers (AWGRs). HiveNet integrates low-radix electrical switches at lower layers to reduce cable complexity and construction costs, while AWGR-based optical connections at upper layers ensure fast switching and high bandwidth. The dual-port capability enables robust fault tolerance and supports all communication types (node-switch and node-node). Furthermore, a customized routing algorithm significantly enhances performance. Simulations conducted under various traffic patterns demonstrate that HiveNet achieves controlled delay and superior aggregate throughput. For large-scale networks (with 104,976 nodes at 10 Gb/s), HiveNet reduces construction costs by 49.3%, 26.4%, 32.7%, 54.1%, and 59.3% compared to Fat-Tree, H-LION, Leaf-Spine, BCube, and Lotus, respectively. Additionally, HiveNet decreases power consumption by 34.8%, 48.2%, 29.8%, and 23.1% compared to Fat-Tree, BCube, Leaf-Spine, and Lotus, respectively.

Details

Title
HiveNet: a high-performance, hybrid, and cost-effective architecture for data center networks
Author
AL-Makhlafi, Moeen 1   VIAFID ORCID Logo  ; Alsmadi, Mutasem K. 2 ; Almekhlafi, Eiad 3 ; Smadi, Ahmad AL 4 ; ALKannad, Abdulrahman A. 5 ; Wu, Fuhui 1 

 Wuhan College, School of Information Engineering, Wuhan, China (GRID:grid.502386.a) 
 Imam Abdulrahman Bin Faisal University, Department of MIS, College of Applied Studies and Community Service, Dammam, Saudi Arabia (GRID:grid.411975.f) (ISNI:0000 0004 0607 035X) 
 IBB University, Department of Computer Science and Information Technology, IBB, Yemen (GRID:grid.444909.4) 
 Al-Zaytoonah University, Department of Computer Science, Faculty of Science and Information Technology, Amman, Jordan (GRID:grid.443348.c) (ISNI:0000 0001 0244 5415) 
 Xidian University, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xi’an, China (GRID:grid.440736.2) (ISNI:0000 0001 0707 115X) 
Pages
360
Publication year
2025
Publication date
Dec 2025
Publisher
Springer Nature B.V.
e-ISSN
13191578
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3281923625
Copyright
© The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.