Software‐defined networking security for private data center networks and clouds: Vulnerabilities, attacks, countermeasures, and solutions

2020 ◽  
Author(s):  
Abdallah Mustafa Abdelrahman ◽  
Joel J. P. C. Rodrigues ◽  
Mukhtar M. E. Mahmoud ◽  
Kashif Saleem ◽  
Ashok Kumar Das ◽  
...  
Keyword(s):  
Data Center ◽  
Software Defined Networking ◽  
Data Center Networks ◽  
Private Data ◽  
Networking Security

scholarly journals An Efficient Wireless Control Plane for Software Defined Networking in Data Center Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 58158-58167
Author(s):  
Zuneera Umair ◽  
Umair Mujtaba Qureshi ◽  
Yingying Cheng ◽  
Xiaohua Jia
Keyword(s):  
Data Center ◽  
Software Defined Networking ◽  
Control Plane ◽  
Data Center Networks ◽  
Wireless Control

scholarly journals Multipath Routing of Elephant Flows in Data Centers Based on Software Defined Networking

2019 ◽  
Vol 9 (2) ◽  
pp. 2714-2717
Keyword(s):  
Data Center ◽  
Software Defined Networking ◽  
Cloud Services ◽  
Data Center Networks ◽  
Load Imbalance ◽  
Current State ◽  
Network Load ◽  
Network Utilization ◽  
The Status ◽  
Multiple Paths

The data center networks encompass various cloud services. Network congestion and network load imbalance may occur in data center networks due to elephant flows. In order to improve the throughput and overall utilization of the network, a dynamic load balancing mechanism has to be in place. Software Defined Networking (SDN) is used to perform the balancing of the network load. SDN can obtain the global view of the network and hence contain the status and topology of the entire data center network. The elephant flows can be split and send to multiple paths based on the current state of the network. The described idea is implemented in the OpenFlow environment and tested for improvement. The result shows the enhancement in throughput and network utilization.

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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