Recent research on Software-Defined Networking (SDN) strongly promotes the adoption of distributed controller architectures. To achieve high network performance, designing a scheduling function (SF) to properly dispatch requests from each switch to suitable controllers becomes critical. However, existing literature tends to design the SF targeted at specific network settings. In this paper, a reinforcement-learning-based (RL) approach is proposed with the aim to automatically learn a general, effective, and efficient SF. In particular, a new dispatching system is introduced in which the SF is represented as a neural network that determines the priority of each controller. Based on the priorities, a controller is selected using our proposed probability selection scheme to balance the trade-off between exploration and exploitation during learning. In order to train a general SF, we first formulate the scheduling function design problem as an RL problem. Then a new training approach is developed based on a state-of-the-art deep RL algorithm. Our simulation results show that our RL approach can rapidly design (or learn) SFs with optimal performance. Apart from that, the trained SF can generalize well and outperforms commonly used scheduling heuristics under various network settings.
In recent years, distributed controller architectures @cite_20 @cite_8 have been widely adopted in SDN to enhance the network performance. Although multiple controllers can be deployed in the control plane, the network performance still heavily relies on effective utilization of the controller resources. Thus, designing an effective and efficient SF for request dispatching is of great importance.
@cite_20 @cite_8
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In this paper we address the problem of Software Defined Networking (SDN) controller placement in large networks. Indeed, to solve the scalability issue raised by the centralized architecture of SDN, multi-controllers deployment (or distributed controllers system) is envisioned. However, the number and the location of controllers in large networks remain an issue. In this context, several works have been proposed to find the optimal placement of SDN controllers. Most of them consider latency among SDN controllers and switches as the main metric. In this work, we go beyond the state of art by proposing a solution that considers at the same time three critical objectives for the optimal placement of controllers: (i) the latency and communication overhead between switches and controllers; (ii)the latency and communication overhead between controllers; (iii) the guarantee of load balancing between controllers. We then solve the system by using Bargaining Game in order to find a fair trade off between these objectives. Simulation results clearly demonstrate the effectiveness of the proposed solution in finding the optimal placement of controllers that enforces this trade-off. Distributed controller architectures have been proposed for Software-Defined Networking (SDN) to ensure scalability and reliability. One major drawback of the existing architectures is the uneven load distribution among controllers stemming from the static binding between controllers and switches. To address this issue, several existing studies introduce dynamic binding by adopting some switch migration mechanisms that re-associate switches from overloaded controllers to underutilized controllers. However, the migration process adds a considerable amount of complexity to the system and may incur significant network latency. In this paper, we propose BLAC, a novel BindingLess Architecture for distributed Controllers (BLAC), in which load balance is achieved with the help of the proposed scheduling layer, which intercepts flow requests from switches and dispatches them to different controllers as determined by selected scheduling algorithms. The process is proceeded transparently with no extra modification required for off-the-shelf SDN switches. Besides, the scheduling layer can flexibly support various scheduling algorithms and causes neither disruption of service nor significant network delay. We build a prototype that can work with various distributed controller systems and conduct experiments to demonstrate its efficacy. The results show that our design outperforms the static-binding controller system in terms of both system throughput and response time without the complexity of the dynamic-binding controller system. Software Defined Networking (SDN) has become a popular paradigm for centralized control in many modern networking scenarios such as data centers and cloud. For large data centers hosting many hundreds of thousands of servers, there are few thousands of switches that need to be managed in a centralized fashion, which cannot be done using a single controller node. Previous works have proposed distributed controller architectures to address scalability issues. A key limitation of these works, however, is that the mapping between a switch and a controller is statically configured, which may result in uneven load distribution among the controllers as traffic conditions change dynamically. To address this problem, we propose ElastiCon, an elastic distributed controller architecture in which the controller pool is dynamically grown or shrunk according to traffic conditions. To address the load imbalance caused due to spatial and temporal variations in the traffic conditions, ElastiCon automatically balances the load across controllers thus ensuring good performance at all times irrespective of the traffic dynamics. We propose a novel switch migration protocol for enabling such load shifting, which conforms with the Openflow standard. We further design the algorithms for controller load balancing and elasticity. We also build a prototype of ElastiCon and evaluate it extensively to demonstrate the efficacy of our design. This paper presents novel QoS extensions to distributed control plane architectures for multimedia delivery over large-scale, multi-operator Software Defined Networks (SDNs). We foresee that large-scale SDNs shall be managed by a distributed control plane consisting of multiple controllers, where each controller performs optimal QoS routing within its domain and shares summarized (aggregated) QoS routing information with other domain controllers to enable inter-domain QoS routing with reduced problem dimensionality. To this effect, this paper proposes (i) topology aggregation and link summarization methods to efficiently acquire network topology and state information, (ii) a general optimization framework for flow-based end-to-end QoS provision over multi-domain networks, and (iii) two distributed control plane designs by addressing the messaging between controllers for scalable and secure inter-domain QoS routing. We apply these extensions to streaming of layered videos and compare the performance of different control planes in terms of received video quality, communication cost and memory overhead. Our experimental results show that the proposed distributed solution closely approaches the global optimum (with full network state information) and nicely scales to large networks.