DeepRLB: A deep reinforcement learning‐based load balancing in data center networks

Author(s):  
Negar Rikhtegar ◽  
Omid Bushehrian ◽  
Manijeh Keshtgari
Author(s):  
Tao Zhang ◽  
Qianqiang Zhang ◽  
Yasi Lei ◽  
Shaojun Zou ◽  
Juan Huang ◽  
...  

Author(s):  
Tariq Emad Ali ◽  
Ameer Hussein Morad ◽  
Mohammed A. Abdala

<span>In the last two decades, networks had been changed according to the rapid changing in its requirements.  The current Data Center Networks have large number of hosts (tens or thousands) with special needs of bandwidth as the cloud network and the multimedia content computing is increased. The conventional Data Center Networks (DCNs) are highlighted by the increased number of users and bandwidth requirements which in turn have many implementation limitations.  The current networking devices with its control and forwarding planes coupling result in network architectures are not suitable for dynamic computing and storage needs.  Software Defined networking (SDN) is introduced to change this notion of traditional networks by decoupling control and forwarding planes. So, due to the rapid increase in the number of applications, websites, storage space, and some of the network resources are being underutilized due to static routing mechanisms. To overcome these limitations, a Software Defined Network based Openflow Data Center network architecture is used to obtain better performance parameters and implementing traffic load balancing function. The load balancing distributes the traffic requests over the connected servers, to diminish network congestions, and reduce underutilization problem of servers. As a result, SDN is developed to afford more effective configuration, enhanced performance, and more flexibility to deal with huge network designs</span>


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