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    MADRina, a novel Multiagent Deep Reinforcement Learning approach, optimizes request dispatching in software-defined networking (SDN) data centers. This adaptive policy significantly reduces response times by intelligently distributing tasks across elastic controllers.

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    Area of Science:

    • Computer Science
    • Networking
    • Artificial Intelligence

    Background:

    • Software-defined networking (SDN) enables flexible control in cloud data centers.
    • Elastic, distributed SDN controllers are necessary for scalable processing capacity.
    • Efficient request dispatching among controllers is a critical challenge.

    Purpose of the Study:

    • To develop an adaptive request dispatching policy for elastic SDN environments.
    • To overcome limitations of existing policies that assume centralized control and global knowledge.
    • To improve dispatching performance and adaptability in dynamic data centers.

    Main Methods:

    • Proposed MADRina (Multiagent Deep Reinforcement Learning) for request dispatching.
    • Designed a multiagent system to avoid reliance on a single centralized agent.
    • Developed a Deep Neural Network-based adaptive policy for elastic controller sets.
    • Created a new algorithm for training adaptive policies in a multiagent context.

    Main Results:

    • MADRina demonstrates high dispatching adaptability and performance.
    • Evaluated using a prototype and simulation tool with real-world network data.
    • Significantly reduced response time by up to 30% compared to existing methods.

    Conclusions:

    • MADRina offers a robust solution for request dispatching in elastic SDN data centers.
    • The multiagent deep reinforcement learning approach enhances system efficiency.
    • This method addresses practical limitations of traditional dispatching policies.