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Title: Deep Q-Learning for Load Balancing Traffic in SDN Networks
Authors: Tosounidis, Vasileios
Pavlidis, Georgios
Sakellariou, Ilias
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Deep Q-Learning
Convolutional Neural Network
Network Analytics
Knowledge-Defined Networking
Load Balancing
Issue Date: Sep-2020
Publisher: ACM
First Page: 135
Last Page: 143
Volume Title: 11th Hellenic Conference on Artificial Intelligence
Abstract: Load balancing is a widely used technique that aims to enable large network topologies, most commonly found in large data centers, to handle the constantly varying load of service requests. Traditional networks built on multi-vendor hardware and software present significant difficulties in the efficient and flexible application of load balancing techniques. Usually, solutions rely on high cost dedicated hardware and thus are used only for a subset of the tasks, resulting to limited flexibility for network administrators. Software Defined Networking (SDN) is a relatively new approach that enables flexible network management solutions to a number of problems, including that of efficient load-balancing. The key characteristics of decoupled centralized network control combined with programmability, allows the seamless integration of AI techniques to network management. Toward this direction, this paper employs deep reinforcement learning to effectively load balance requests to services in a data center network, resulting to an approach that is able to dynamically adapt to varying request loads, including changes in the infrastructure’s capabilities. The proposed approach is experimentally evaluated in order to support its feasibility, with very promising results.
ISBN: 9781450388788
Other Identifiers: 10.1145/3411408.3411423
Appears in Collections:Department of Applied Informatics

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