QoS-based Flow Classification and Forwarding in Hybrid Electrical/Optical Switched Data Center Networks

2021 ◽  
Author(s):  
Jie Zhang ◽  
Wei Wang ◽  
Yan Shen ◽  
Yajie Li ◽  
Yongli Zhao ◽  
...  
2020 ◽  
Vol 168 ◽  
pp. 102766
Author(s):  
Wai-Xi Liu ◽  
Jun Cai ◽  
Yu Wang ◽  
Qing Chun Chen ◽  
Jia-Qi Zeng

2016 ◽  
Vol 8 (10) ◽  
pp. 777 ◽  
Author(s):  
Houman Rastegarfar ◽  
Madeleine Glick ◽  
Nicolaas Viljoen ◽  
Mingwei Yang ◽  
John Wissinger ◽  
...  

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

Author(s):  
Jiawei Huang ◽  
Shiqi Wang ◽  
Shuping Li ◽  
Shaojun Zou ◽  
Jinbin Hu ◽  
...  

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.


Author(s):  
Jiawei Huang ◽  
Wenjun Lyu ◽  
Weihe Li ◽  
Jianxin Wang ◽  
Tian He

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