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#6094. (ITMP) – Intelligent Traffic Management Prototype using Reinforcement Learning approach for Software Defined Data Center (SDDC)
| October 2026 | publication date |
| Proposal available till | 10-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Computer Science (all); Electrical and Electronic Engineering; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №6094.1   | Contract №6094.2 | Contract №6094.3 | Contract №6094.4 |
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Software defined network architecture offers scalability and resilience as the significant advantages to data center networks. This increases the fault tolerance ability of traditional data center network architectures. Massive amounts of mobile network data as well as e-commerce application data requests are the key sources for data centers which recurrently desire attention. Researchers are yet to design a suitable prototype with functional intelligence to support traffic optimization techniques in SDDC. In this research work, we are proposing an intelligent traffic management prototype for software defined data center by means of reinforcement learning approach through the integration of the functionalities such as controller positioning, traffic load balancing, routing and energy efficiency. These are the key areas where traffic optimization becomes essential to improve network performance. The proposed prototype provides a complete framework for enterprises to deploy applications in an efficient manner. We model the prototype to handle dynamic network data applications such as information retrieval, communication and banking applications. We focus in this article on how communication happens among the data center nodes as an inter-data center communication process upon receiving requests from the applications considered. To further enhance the novelty and efficiency of our research work, we adopt multiple reinforcement learning agents to lever load balancing and routing functionalities. Moreover, to assess and ensure the optimized network performance, we evaluate the energy consumption of the network achieved through our proposed prototype.
Keywords:
Data center network (DCN); Intelligent traffic management prototype (ITMP); Reinforcement learning (RL); Software defined data center (SDDC)
Contacts :
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Software defined network architecture offers scalability and resilience as the significant advantages to data center networks. This increases the fault tolerance ability of traditional data center network architectures. Massive amounts of mobile network data as well as e-commerce application data requests are the key sources for data centers which recurrently desire attention. Researchers are yet to design a suitable prototype with functional intelligence to support traffic optimization techniques in SDDC. In this research work, we are proposing an intelligent traffic management prototype for software defined data center by means of reinforcement learning approach through the integration of the functionalities such as controller positioning, traffic load balancing, routing and energy efficiency. These are the key areas where traffic optimization becomes essential to improve network performance. The proposed prototype provides a complete framework for enterprises to deploy applications in an efficient manner. We model the prototype to handle dynamic network data applications such as information retrieval, communication and banking applications. We focus in this article on how communication happens among the data center nodes as an inter-data center communication process upon receiving requests from the applications considered. To further enhance the novelty and efficiency of our research work, we adopt multiple reinforcement learning agents to lever load balancing and routing functionalities. Moreover, to assess and ensure the optimized network performance, we evaluate the energy consumption of the network achieved through our proposed prototype.
Keywords:
Data center network (DCN); Intelligent traffic management prototype (ITMP); Reinforcement learning (RL); Software defined data center (SDDC)
Contacts :
