Fault-Tolerant Communication Mechanisms in Data Center Network of HS-DCell

In the era of rapid technological advancements and widespread big data applications, data center networks serve as critical enablers for seamless communication and efficient data processing in large-scale computing ecosystems. Network reliability is fundamental to o ensure stable, high-performance operations. Traditional connectivity metrics assume simultaneous failures of all neighboring servers, which is unrealistic in complex network topologies and leads to inaccurate fault-tolerance assessments. To address this, [Formula: see text]-extra connectivity was proposed to better evaluate network reliability. [Formula: see text]-[Formula: see text], a novel server-centric network built upon DCell and BCCC, exhibits remarkable scalability with a linearly growing diameter as network levels increase. This paper investigates the [Formula: see text]-[Formula: see text] network, which leverages 3-port servers to reduce port counts, alleviate traffic congestion, and minimize latency, thereby offering robust scalability and fault tolerance. We derive the formula for the [Formula: see text]-extra connectivity of [Formula: see text]-[Formula: see text]. Unlike traditional metrics, which only consider disconnecting the network with the minimum number of nodes h-extra connectivity depends on the network’s hierarchical structure and server interconnections, ensuring each connected component contains at least [Formula: see text] nodes. This provides a more accurate measure of network robustness. The findings offer insights for designing resilient DCNs with enhanced fault-tolerance capabilities.

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Publication Details

Journal
International Journal of Foundations of Computer Science
Published
2025-12-06
DOI
https://doi.org/10.1142/s0129054125500455
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Fault-Tolerant Communication Mechanisms in Data Center Network of HS-DCell

Mengjie Lv, Xueli Sun, Fu Xiao, Feiyu Yang et al.
International Journal of Foundations of Computer Science
Cloud Computing and Resource Management
article

Fault-Tolerant Communication Mechanisms in Data Center Network of HS-DCell

Mengjie Lv, Xueli Sun, Fu Xiao, Feiyu Yang, Weibei Fan
article en

Abstract

In the era of rapid technological advancements and widespread big data applications, data center networks serve as critical enablers for seamless communication and efficient data processing in large-scale computing ecosystems. Network reliability is fundamental to o ensure stable, high-performance operations. Traditional connectivity metrics assume simultaneous failures of all neighboring servers, which is unrealistic in complex network topologies and leads to inaccurate fault-tolerance assessments. To address this, [Formula: see text]-extra connectivity was proposed to better evaluate network reliability. [Formula: see text]-[Formula: see text], a novel server-centric network built upon DCell and BCCC, exhibits remarkable scalability with a linearly growing diameter as network levels increase. This paper investigates the [Formula: see text]-[Formula: see text] network, which leverages 3-port servers to reduce port counts, alleviate traffic congestion, and minimize latency, thereby offering robust scalability and fault tolerance. We derive the formula for the [Formula: see text]-extra connectivity of [Formula: see text]-[Formula: see text]. Unlike traditional metrics, which only consider disconnecting the network with the minimum number of nodes h-extra connectivity depends on the network’s hierarchical structure and server interconnections, ensuring each connected component contains at least [Formula: see text] nodes. This provides a more accurate measure of network robustness. The findings offer insights for designing resilient DCNs with enhanced fault-tolerance capabilities.

International Journal of Foundations of Computer Science
Nanjing University of Posts and Telecommunications (CN)
Openalex Percentile: Top 48%
Cloud Computing and Resource Management
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