Abstract
Software defined network (SDN) is a promising technology which can reduce network management complexity through the decoupling of the control plane and data plane. Due to large number of switches in the data plane, distributed and multiple controllers are necessary in the control plane for managing the switches. The switch controller mapping strategy for identifying the mapping relationships between the switch and controller is crucial in order to optimize the network performance. Considering the dynamics of the network behavior, it is quite important and challenging to develop models to reflect the network topology dynamics and to propose method for solving the long-term network performance optimization. Inspired by the recent advances in Artificial Intelligence (AI), in this paper, we propose a Deep Reinforcement Learning (DRL) based strategy for solving the switch controller mapping problem. A DRL based mapping strategy is proposed, in which Markov Decision Process (MDP) formulation is devised and Deep $Q$ -network (DQN) is proposed to achieve the maximization of long-term system performance by leveraging network latency, load balancing and system stability. Extensive simulations show that the DQN based algorithm can achieve the best system stability results while maintaining moderate switch controller latency and system equilibrium performance comparing with the optimization which only considers current system performance for switch controller mapping decision, and the optimization approaches which generate mapping decisions purely based on latency or load balancing separately.
Highlights
Software Defined Network (SDN) is an emerging technology, in which the control plane and forwarding plane are decoupled
In [13], the SDN controller assignment problem is solved for minimizing the long-term operating, maintenance and switching cost considering the dynamic changing of work load
In this paper, a Deep Reinforcement Learning (DRL) based approach has been proposed for solving the switch controller mapping problem in order to optimize the long-term network performance
Summary
Software Defined Network (SDN) is an emerging technology, in which the control plane and forwarding plane are decoupled. The above existing research works solve the switch controller mapping problem based on the instantaneous network environment information, such as the controller traffic load, working status and resources’ utilization. The objective is to tackle controller switch dynamic mapping problem for optimizing the long-term system performance (cumulative system performance in the long run) by considering the network topology dynamics including both controllers’ and switches’ dynamic behavior. The dynamic switch controller mapping problem is proposed to optimize the long-term (cumulative performance in the long run) overall system performance in terms of switches’ response time, controllers’ load balancing and system stability considering the dynamics of network topology including both controllers’ and switches’ dynamics. A DQN based switch controller mapping algorithm is proposed for solving the long-term optimization problem.
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