Switch-controller Mapping Research Articles

A Temporal Deep Q Learning for Optimal Load Balancing in Software-Defined Networks.

With the rapid advancement of the Internet of Things (IoT), there is a global surge in network traffic. Software-Defined Networks (SDNs) provide a holistic network perspective, facilitating software-based traffic analysis, and are more suitable to handle dynamic loads than a traditional network. The standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers; however, a logically centralized single controller faces severe bottleneck issues. Most proposed solutions in the literature are based on the static deployment of multiple controllers without the consideration of flow fluctuations and traffic bursts, which ultimately leads to a lack of load balancing among controllers in real time, resulting in increased network latency. Moreover, some methods addressing dynamic controller mapping in multi-controller SDNs consider load fluctuation and latency but face controller placement problems. Earlier, we proposed priority scheduling and congestion control algorithm (eSDN) and dynamic mapping of controllers for dynamic SDN (dSDN) to address this issue. However, the future growth of IoT is unpredictable and potentially exponential; to accommodate this futuristic trend, we need an intelligent solution to handle the complexity of growing heterogeneous devices and minimize network latency. Therefore, this paper continues our previous research and proposes temporal deep Q learning in the dSDN controller. A Temporal Deep Q learning Network (tDQN) serves as a self-learning reinforcement-based model. The agent in the tDQN learns to improve decision-making for switch-controller mapping through a reward-punish scheme, maximizing the goal of reducing network latency during the iterative learning process. Our approach-tDQN-effectively addresses dynamic flow mapping and latency optimization without increasing the number of optimally placed controllers. A multi-objective optimization problem for flow fluctuation is formulated to divert the traffic to the best-suited controller dynamically. Extensive simulation results with varied network scenarios and traffic show that the tDQN outperforms traditional networks, eSDNs, and dSDNs in terms of throughput, delay, jitter, packet delivery ratio, and packet loss.

Toward Flexible and Predictable Path Programmability Recovery Under Multiple Controller Failures in Software-Defined WANs

Software-Defined Networking (SDN) promises good network performance in Wide Area Networks (WANs) with the logically centralized control using physically distributed controllers. In Software-Defined WANs (SD-WANs), maintaining path programmability, which enables flexible path change on flows, is crucial for maintaining network performance under traffic variation. However, when controllers fail, existing solutions are essentially coarse-grained switch-controller mapping solutions and only recover the path programmability of a limited number of offline flows, which traverse offline switches controlled by failed controllers. In this paper, we propose () to provide predictable path programmability recovery under multiple controller failures in SD-WANs. The key idea of is to approximately realize flow-controller mappings using hybrid SDN/legacy routing supported by high-end commercial SDN switches. Using the hybrid routing, we can recover programmability by selecting a routing mode for each offline flow at each offline switch in a fine-grained way to fit the given control resource from active controllers and release a few control resource of active controllers by reasonably configuring some normal flows under legacy routing mode. Thus, can promise ample control resource to improve the recovery efficiency and further effectively map offline switches to active controllers. Simulation results show that outperforms existing switch-level solutions by maintaining balanced programmability and increasing the total programmability of recovered offline flows up to 660% under topology and 590% under topology.

Indirect Multi-Mapping for Burstiness Management in Software Defined Networks

Large software defined networks use a cluster of distributed controllers to process flow requests from a massive number of switches. To cope with traffic dynamics, this paper studies a new problem of how to improve the residual capacity available at the controllers to handle request bursts experienced at the switches. While the total residual capacity is a constant under a given total capacity of all controllers and a given total workload from all switches, this paper considers the residual capacity available to each individual switch, which depends on how the switches are mapped to the controllers for management. We focus on how to maximize the minimum residual capacity available to any switch . The prior work either provides poor residual capacity or incurs heavy synchronization overhead by simulation results. This paper proposes a new method called indirect multi-mapping that achieves both high residual capacity and low synchronization cost. We formally define a non-linear integer optimization problem for max-min residual capacity under indirect multi-mapping. We then approximate the problem as two sub-problems: switch-controller mapping selection and weight assignment for each switch-controller mapping. We solve these sub-problems and formally analyze their approximate factor. We implement the proposed solution on an SDN testbed for experimental studies and use simulations for large-scale investigation. Our evaluation shows that indirect multi-mapping improves the minimum residual capacity by 49.8% on average and reduces the synchronization cost by 41.9-60.3% on average when compared with the alternatives.

Cost_EACP: Cost-effective adaptive controller provisioning in software defined DCNs

Software defined networking (SDN) is regarded as a new paradigm of flow management in data center networks (DCNs). In SDN, the existing methods of managing flows are centralized by controller in the control plane and highly rely on the switches in the data plane to get forwarding rules from controller. Therefore, it is of great significance to consider the mapping relationship between the controller and the switch based on flow types. To address this issue, this paper first designs a flow-based two-tier centralized management framework for software defined DCNs and formulates a dynamic mapping problem using integer programming called cost-effective adaptive controller provisioning (Cost_EACP) problem, which is proved to be NP-complete. Then, in order to solve this problem, we transform the integer programming problem into fraction programming problem. Subsequently, we design a rounding-based Cost_EACP algorithm (RC_EACP) and propose a switch controller mapping algorithm (SCM_EACP) based on RC_EACP algorithm to address the mapping problem. Furthermore, we analyze the approximation performance and time complexity of proposed algorithm. Finally, experimental results demonstrate that the proposed algorithm is more effective than existing algorithms in terms of reducing the control channel bandwidth cost and delay cost for setting up flow rules, and the gap between the proposed algorithm and the optimal one is less than 24%.

Placement of Controllers in Software Defined Networking under Multiple Controller Mapping

This work focuses on the placement of controllers in software-defined networking architectures. A mathematical model is developed to place controllers under multi- controller switch-controller mapping, where a switch can be assigned to multiple controllers. Resiliency, scalability, and inter-plane latency are all modeled in the proposed model. A scalability factor is introduced to increase the load to capacity gap at controllers, preventing controllers to work near their capacity limit. The proposed model is shown to be effective and resilient under different failure scenarios while, at the same time, taking latency and scalability into consideration.
 Keywords: Controller Placement, Software-defined Networking, Reliability, Scalability

Byzantine-Resilient Controller Mapping and Remapping in Software Defined Networks

In software-defined networks, a compromised controller that is Byzantine in nature would issue inconsistent messages selectively to its communicating nodes. Defending against such threats is very challenging since the infected messages look legitimate. To defend against $f$ simultaneous controller failures using the conventional Byzantine fault tolerance approach requires a switch to be mapped to $3f+1$ controllers. This approach increases the network load due to multiple requests from switches and also requires a large number of controllers for the entire network. In this paper, we propose a novel primary-backup controller mapping approach in which a switch is mapped to only $f+1$ primary and $f$ backup controllers to defend against $f$ simultaneous controller failures. We develop an optimization programming formulation for the switch-controller mapping problem that minimizes the number of controllers required considering latency and capacity constraints. We also develop an optimization programming formulation for the remapping problem upon a controller failure, minimizing the number of remappings of unaffected switches. Since the optimization formulations are computationally prohibitive, we develop heuristic algorithms for both problems. The performance study shows that the optimal mapping requires up to 50% fewer controllers compared to an existing scheme and the heuristics perform within $8\%$ of the optimum.

A Deep Reinforcement Learning Based Switch Controller Mapping Strategy in Software Defined Network

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.

QoC-Aware Control Traffic Engineering in Software Defined Networks

In a distributed Software Defined Networking (SDN) architecture, the Quality of Service (QoS) experienced by a traffic flow through an SDN switch is primarily dependant on the SDN controller to which that switch is mapped. We propose a new controller-quality metric known as the Quality of Controller (QoC) which is defined based on the controller’s reliability and response time. We model the controller reliability based on Bayesian inference while its response time is modelled as a linear approximation of the M/M/1 queue. We develop a QoC-aware approach for solving (i) the switch-controller mapping problem and, (ii) control traffic distribution among the mapped controllers. Each switch is mapped to multiple controllers to enable resilience with the switch-controller mapping and control traffic distribution based on the QoC metric which is the combined cost of controller reliability and response time. We first develop an optimization programming formulation that maximizes the minimum QoC among the set of controllers to solve the above problem. Since the optimization problem is computationally prohibitive for large networks, we develop a heuristic algorithm — Qoc-Aware switch-coNTroller Mapping (QuANTuM) — that solves the problem of switch-controller mapping and control traffic distribution in two stages such that the minimum of the controller QoC is maximized. Through simulations, we show that the heuristic results are within 18% of the optimum while achieving a fair control traffic distribution with a QoC min-max ratio of up to 95%.

Fractional switch migration in multi-controller software-defined networking

Mapping switches to controllers in multi-controller software-defined networking (SDN) is still a hot research topic. Many factors have to be considered when establishing this mapping. Among them are the load balancing and mapping stability. Load balancing is important to improve resources utilization, and mapping stability reduces the control plane overhead created when exchanging information triggered by new mappings. This article presents a model for dynamic switch-controller mapping to achieve load balancing and minimize the number of new switch-controller assignments. To that end, for load balancing, flows from a switch are allowed to be handled by multiple controllers, and to increase assignment stability, the assignments at time t−1 are taken into consideration when calculating the assignments at time t. The model is formulated as a convex quadratic programming problem, and the properties and feasibility of this model are mathematically analyzed. In addition, a heuristic algorithm is developed to deal with large-scale networks. The experimental results show the effectiveness of the proposed approach when compared to recent academic work, where the proposed model leads to a slight improvement in the load balancing and increases the stability of the switch-controller assignment by ≈ 91%.

On Load Balancing via Switch Migration in Software-Defined Networking

Switch-controller assignment is an essential task in multi-controller software-defined networking. Static assignments are not practical because network dynamics are complex and difficult to predetermine. Since network load varies both in space and time, the mapping of switches to controllers should be adaptive to sudden changes in the network. To that end, switch migration plays an important role in maintaining dynamic switch-controller mapping. Migrating switches from overloaded to underloaded controllers brings flexibility and adaptability to the network but, at the same time, deciding which switches should be migrated to which controllers, while maintaining a balanced load in the network, is a challenging task. This work presents a heuristic approach with solution shaking to solve the switch migration problem. Shift and swap moves are incorporated within a search scheme. Every move is evaluated by how much benefit it will give to both the immigration and outmigration controllers. The experimental results show that the proposed approach is able to outweigh the state-of-art approaches, and improve the load balancing results up to ≈ 14% in some scenarios when compared to the most recent approach. In addition, the results show that the proposed work is more robust to controller failure than the state-of-art methods.

A Load-Balancing Mechanism for Distributed SDN Control Plane Using Response Time

Software-defined networking (SDN) has become a popular paradigm for managing large-scale networks including cloud servers and data centers because of its advantages of centralized management and programmability. The issues of scalability and reliability that a single centralized controller suffers makes distributed controller architectures emerge. One key limitation of distributed controllers is the statically configured switch-controller mapping, easily causing uneven load distribution among controllers. Previous works have proposed load-balancing methods with switch migration to address this issue. However, the higher-load controller is always directly considered as the overloaded controller that need to shift its load to other controllers, even if it has no response time delay. The pursuit of absolute load-balancing effect can also result in frequent network delays and service interruptions. Additionally, if there are several overloaded controllers, just one controller with the maximum load can be addressed within a single load-balancing operation, reducing load-balancing efficiency. To address these problems, we propose SMCLBRT, a load-balancing strategy of multiple SDN controllers based on response time, considering the changing features of real-time response times versus controller loads. By selecting the appropriate response time threshold and dealing with multiple overloading controllers simultaneously, it can well solve load-balancing problem in SDN control plane with multiple overloaded controllers. Simulation experiments exhibit the effectiveness of our scheme.

On Multiple Controller Mapping in Software Defined Networks With Resilience Constraints

We propose an effective switch-controller mapping scheme for distributed controller architectures in software defined networks. Our scheme maps a switch to multiple controllers and distributes flow setup requests among them to minimize flow setup time, satisfying the resilience constraint, which requires that a specified fraction of setup requests at each switch is not affected upon a controller failure. We develop an optimization formulation for the problem and compare our scheme against the single controller mapping. The results show that our scheme reduces flow setup time, provides better fairness among switches and that it is more stable against dynamic traffic fluctuations.