Autonomous Network Management Using Cooperative Learning for Network-Wide Load Balancing in Heterogeneous Networks

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Executive Summary

Traditional hop-by-hop dynamic routing makes inefficient use of network resources as it forwards packets along already congested shortest paths while uncongested longer paths may be underutilized. To maintain network-wide load balancing, the paper proposes Autonomous Network management with Team learning based Self-configuration (ANTS) which attempts to manage a feasible route for traffic flow with QoS constraints in heterogeneous networks. To enable cognitive intelligence for network-wide load balancing, the paper implements a cross-layer mechanism in which learning agents in middleware layer can monitor the queue sizes of MAC layer, thereby allowing for the discovery of optimal routes.

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