Design and implementation of SDN-base QoS traffic control method for Electric power data center network

2016 ◽  
Author(s):  
Jian Di ◽  
Quanquan Ma
Keyword(s):  
Electric Power ◽  
Traffic Control ◽  
Data Center ◽  
Control Method ◽  
Data Center Network ◽  
Design And Implementation

OPMDC: Architecture Design and Implementation of a New Optical Pyramid Data Center Network

2015 ◽  
Vol 33 (10) ◽  
pp. 2019-2031 ◽  
Author(s):  
Maria C. Yuang ◽  
Po-Lung Tien ◽  
Hsing-Yu Chen ◽  
Wei-Zhang Ruan ◽  
Tzu-Kai Hsu ◽  
...  
Keyword(s):  
Data Center ◽  
Architecture Design ◽  
Data Center Network ◽  
Design And Implementation

Design and Implementation of Data Center Network Security

2013 ◽  
Vol 694-697 ◽  
pp. 2308-2312
Author(s):  
Wei Du
Keyword(s):  
Network Security ◽  
Data Center ◽  
Data Centers ◽  
Data Center Network ◽  
Security Issues ◽  
Design And Implementation ◽  
The Core ◽  
Operation And Maintenance ◽  
Secure Data ◽  
Security Infrastructure

In this paper, starting with the current security issues of the Ethernet data center, the author put forward the solutions for the secure data centers. The solutions are based on a security infrastructure, framed according to border protection, and The depth detection as the core of the secure data center solutions. It is apparent to penetrate the concept of security to the entire data center network design, deployment, and operation and maintenance.

scholarly journals Intelligent Network Traffic Control Based on Deep Reinforcement Learning

2022 ◽  
Vol 16 ◽  
pp. 585-594
Author(s):  
Fei Wu ◽  
Ting Li ◽  
Fucai Luo ◽  
Shulin Wu ◽  
Chuanqi Xiao
Keyword(s):  
Reinforcement Learning ◽  
Flow Control ◽  
Network Traffic ◽  
Traffic Control ◽  
Data Center ◽  
Control Method ◽  
Learning Algorithm ◽  
Traffic Density ◽  
Intelligent Network ◽  
Application Process

This paper studies the problems of load balancing and flow control in data center network, and analyzes several common flow control schemes in data center intelligent network and their existing problems. On this basis, the network traffic control problem is modeled with the goal of deep reinforcement learning strategy optimization, and an intelligent network traffic control method based on deep reinforcement learning is proposed. At the same time, for the flow control order problem in deep reinforcement learning algorithm, a flow scheduling priority algorithm is proposed innovatively. According to the decision output, the corresponding flow control and control are carried out, so as to realize the load balance of the network. Finally, experiments show, the network traffic bandwidth loss rate of the proposed intelligent network traffic control method is low. Under the condition of random 60 traffic density, the average bisection bandwidth obtained by the proposed intelligent network traffic control method is 4.0mbps and the control error rate is 2.25%. The intelligent network traffic control method based on deep reinforcement learning has high practicability in the practical application process, and fully meets the research requirements.

Architecture of the Data Center Network

2013 ◽  
Vol 24 (2) ◽  
pp. 295-316 ◽  
Author(s):  
Xiang-Lin WEI ◽  
Ming CHEN ◽  
Jian-Hua FAN ◽  
Guo-Min ZHANG ◽  
Zi-Yi LU
Keyword(s):  
Data Center ◽  
Data Center Network

A Universal Approach to Data Center Network Design

2015 ◽  
Author(s):  
Aditya Akella ◽  
Theophilus Benson ◽  
Bala Chandrasekaran ◽  
Cheng Huang ◽  
Bruce Maggs ◽  
...  
Keyword(s):  
Network Design ◽  
Data Center ◽  
Data Center Network ◽  
Universal Approach

scholarly journals Survey on Traffic Management in Data Center Network: From Link Layer to Application Layer

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 38427-38456
Author(s):  
Weihe Li ◽  
Jingling Liu ◽  
Shiqi Wang ◽  
Tao Zhang ◽  
Shaojun Zou ◽  
...  
Keyword(s):  
Data Center ◽  
Traffic Management ◽  
Application Layer ◽  
Data Center Network ◽  
Link Layer
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