Classification of Optical Interconnects in Data Center Networks

2014 ◽  
Author(s):  
Mohsin Fayyaz ◽  
Khurram Aziz
Keyword(s):  
Data Center ◽  
Optical Interconnects ◽  
Data Center Networks

Performance analysis of optical interconnects’ architectures for data center networks

2016 ◽  
Vol 19 (3) ◽  
pp. 1139-1161 ◽  
Author(s):  
Mohsin Fayyaz ◽  
Khurram Aziz ◽  
Ghulam Mujtaba
Keyword(s):  
Performance Analysis ◽  
Data Center ◽  
Optical Interconnects ◽  
Data Center Networks

Blocking probability in optical interconnects in data center networks

2015 ◽  
Vol 30 (2) ◽  
pp. 204-222 ◽  
Author(s):  
Mohsin Fayyaz ◽  
Khurram Aziz ◽  
Ghulam Mujtaba
Keyword(s):  
Data Center ◽  
Optical Interconnects ◽  
Blocking Probability ◽  
Data Center Networks

Hierarchical optical interconnects saving spectrum resources in data center networks

2017 ◽  
Author(s):  
Pawel Wiatr ◽  
Robert Forchheimer ◽  
Marija Furdek ◽  
Jiajia Chen ◽  
Lena Wosinska ◽  
...  
Keyword(s):  
Data Center ◽  
Optical Interconnects ◽  
Data Center Networks

scholarly journals Scalability analysis methodology for passive optical interconnects in data center networks using PAM

2017 ◽  
Vol 403 ◽  
pp. 283-289 ◽  
Author(s):  
R. Lin ◽  
Krzysztof Szczerba ◽  
Erik Agrell ◽  
Lena Wosinska ◽  
M. Tang ◽  
...  
Keyword(s):  
Data Center ◽  
Optical Interconnects ◽  
Data Center Networks ◽  
Scalability Analysis ◽  
Analysis Methodology

Analysis on Buffer Occupancy of Quantized Congestion Notification in Data Center Networks

2016 ◽  
Vol E99.B (11) ◽  
pp. 2361-2372 ◽  
Author(s):  
Chang RUAN ◽  
Jianxin WANG ◽  
Jiawei HUANG ◽  
Wanchun JIANG
Keyword(s):  
Data Center ◽  
Data Center Networks ◽  
Buffer Occupancy

scholarly journals HTPC: heterogeneous traffic-aware partition coding for random packet spraying in data center networks

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jiawei Huang ◽  
Shiqi Wang ◽  
Shuping Li ◽  
Shaojun Zou ◽  
Jinbin Hu ◽  
...  
Keyword(s):  
Data Center ◽  
Large Scale ◽  
Network Performance ◽  
Rooted Tree ◽  
Heterogeneous Traffic ◽  
Data Center Networks ◽  
Packet Reordering ◽  
Traffic Characteristics ◽  
Network Utilization ◽  
The Impact

AbstractModern data center networks typically adopt multi-rooted tree topologies such leaf-spine and fat-tree to provide high bisection bandwidth. Load balancing is critical to achieve low latency and high throughput. Although the per-packet schemes such as Random Packet Spraying (RPS) can achieve high network utilization and near-optimal tail latency in symmetric topologies, they are prone to cause significant packet reordering and degrade the network performance. Moreover, some coding-based schemes are proposed to alleviate the problem of packet reordering and loss. Unfortunately, these schemes ignore the traffic characteristics of data center network and cannot achieve good network performance. In this paper, we propose a Heterogeneous Traffic-aware Partition Coding named HTPC to eliminate the impact of packet reordering and improve the performance of short and long flows. HTPC smoothly adjusts the number of redundant packets based on the multi-path congestion information and the traffic characteristics so that the tailing probability of short flows and the timeout probability of long flows can be reduced. Through a series of large-scale NS2 simulations, we demonstrate that HTPC reduces average flow completion time by up to 60% compared with the state-of-the-art mechanisms.

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