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Proceedings Paper

Resource management in distributed SDN using reinforcement learning
Author(s): Liang Ma; Ziyao Zhang; Bongjun Ko; Mudhakar Srivatsa; Kin K Leung
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Paper Abstract

Distributed software-defined networking (SDN), which consists of multiple inter-connected network domains and each managed by one SDN controller, is an emerging networking architecture that offers balanced centralized/distributed control. Under such networking paradigm, resource management among various domains (e.g., optimal resource allocation) can be extremely challenging. This is because many tasks posted to the network require resources (e.g., CPU, memory, bandwidth, etc.) from different domains, where cross-domain resources are correlated, e.g., their feasibility depends on the existence of a reliable communication channel connecting them. To address this issue, we employ the reinforcement learning framework, targeting to automate this resource management and allocation process by proactive learning and interactions. Specifically, we model this issue as an MDP (Markov Decision Process) problem with different types of reward functions, where our objective is to minimize the average task completion time. Regarding this problem, we investigate the scenario where the resource status among controllers is fully synchronized. Under such scenario, the SDN controller has complete knowledge of the resource status of all domains, i.e., resource changes upon any policies are directly observable by controllers, for which Q-learning-based strategy is proposed to approach the optimal solution.

Paper Details

Date Published: 4 May 2018
PDF: 8 pages
Proc. SPIE 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, 106350M (4 May 2018); doi: 10.1117/12.2306087
Show Author Affiliations
Liang Ma, IBM Thomas J. Watson Research Ctr. (United States)
Ziyao Zhang, Imperial College London (United Kingdom)
Bongjun Ko, IBM Thomas J. Watson Research Ctr. (United States)
Mudhakar Srivatsa, IBM Thomas J. Watson Research Ctr. (United States)
Kin K Leung, Imperial College London (United Kingdom)

Published in SPIE Proceedings Vol. 10635:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX
Michael A. Kolodny; Dietrich M. Wiegmann; Tien Pham, Editor(s)

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