POCO-MOEA: Using Evolutionary Algorithms to Solve the Controller Placement Problem

One of the central tenets of a Software Defined Network (SDN) is the use of controllers, which are responsible for managing how traffic flows through switches, routers, and other data-passing devices on a computer network. Most modern SDNs use multiple controllers to divide responsibility for network switches while keeping communication latency low. A problem that has emerged since approximately 2011 is the decision of where to place these controllers to create the most \u27optimum\u27 network. This is known as the Controller Placement Problem (CPP). Such a decision is subject to multiple and sometimes con_icting goals, making the CPP a type of Multi-Objective Problem (MOP). The Controller Placement Problem is NP-Hard. This means finding the \u27optimum\u27 solution can become a time-intensive process as network size increases. Multiple algorithms exist to solve MOPs using shortcut (or \u27heuristic\u27) methods which can produce a \u27near-optimal\u27 solution in times much shorter than those necessary to guarantee an \u27optimal\u27 solution. One popular class of algorithms is known as Evolutionary Algorithms (EAs); EAs designed to solve Multi-Objective problems are called Multi-Objective Evolutionary Algorithms (MOEAs). While many MOEAs exist, their application to the Controller Placement Problem is not well explored. The theory of this thesis is that an MOEA can produce solutions to the Controller Placement Problem which are \u27nearly optimal\u27 while keeping execution time low compared to an exhaustive \u27optimal\u27 search. This research extends a network modeling tool called the Pareto Optimal Controller Placement (POCO) Framework with custom designed MOEA, called POCO-MOEA. A series of full-factorial experiments is designed and executed to gather data on POCO-MOEA performance to a series of model networks. The algorithm\u27s behavior is then evaluated and compared to exhaustive search through five metrics; fraction of solution space size, average distance between pareto fronts (δ1), worst-case distance between pareto fronts (δ2), relative hypervolume (hyprel), and relative execution time (brel). Results show that performance is dependent on the size of the network, the topology of the network, and the parameters chosen for POCO-MOEA. In general, performance for POCO-MOEA improves as the size of the network increases. Given a large network (60+ nodes), POCO-MOEA can achieve within 0.4% of δ1, 3% of δ2, and 6% of hyprel while still being 500 times faster than exhaustive search. This research demonstrates and adds a valuable tool to the methods of determining optimal device placement for an SDN while providing steps to using MOEAs in real SDN applications

The Role of Inter-Controller Traffic for Placement of Distributed SDN Controllers

We consider a distributed Software Defined Networking (SDN) architecture adopting a cluster of multiple controllers to improve network performance and reliability. Besides the Openflow control traffic exchanged between controllers and switches, we focus on the control traffic exchanged among the controllers in the cluster, needed to run coordination and consensus algorithms to keep the controllers synchronized. We estimate the effect of the inter-controller communications on the reaction time perceived by the switches depending on the data-ownership model adopted in the cluster. The model is accurately validated in an operational Software Defined WAN (SDWAN). We advocate a careful placement of the controllers, that should take into account both the above kinds of control traffic. We evaluate, for some real ISP network topologies, the delay tradeoffs for the controllers placement problem and we propose a novel evolutionary algorithm to find the corresponding Pareto frontier. Our work provides novel quantitative tools to optimize the planning and the design of the network supporting the control plane of SDN networks, especially when the network is very large and in-band control plane is adopted. We also show that for operational distributed controllers (e.g. OpenDaylight and ONOS), the location of the controller which acts as a leader in the consensus algorithm has a strong impact on the reactivity perceived by switches.Comment: 14 page

Controller Placement in Vehicular Networks: A Novel Algorithm Utilizing Elite Opposition-Based Salp Swarm and an Adaptable Approach

The rapid advancement of networking technology has enabled small devices to have communication capabilities, but the current decentralized communication system is not ideal for heterogeneous networks like vehicular networks. The integration of routing, switching, and decision-making capabilities in the same network device limits innovation and impedes performance in decentralized networks, especially in vehicular networks where network topologies change frequently. To address the demands of such networks, Software-Defined Networking (SDN) provides a promising solution that supports innovation. However, SDN's single-controller-based system may restrict the network's operational capabilities, despite being programmable and flexible. This paper suggests two methods to tackle the complex problem of controller placement in SDN: an adaptable approach based on OpenFlow protocol in OpenNet and an evolutionary algorithm called Elite Opposition-Based Salp Swarm Algorithm (EO-SSA) to minimize propagation latency, load imbalance, and network resilience. Multiple controllers increase the network's capabilities and provide fault tolerance, but their placement requires a trade-off among various objectives. The proposed methods have been evaluated and analyzed to confirm their effectiveness. The current decentralized network system is not adequate for vehicular networks, and SDN offers a promising solution that supports innovation and can meet the current demands of such networks

An Effective Approach to Controller Placement in Software Defined Wide Area Networks

This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers via the DOI in this record.One grand challenge in Software Defined Networking (SDN) is to select appropriate locations for controllers to shorten the latency between controllers and switches in wide area networks. In the literature, the majority of approaches are focused on the reduction of packet propagation latency, but propagation latency is only one of the contributors of the overall latency between controllers and their associated switches. In this paper, we explore and investigate more possible contributors of the latency, including the end-to-end latency and the queuing latency of controllers. In order to decrease the end-to-end latency, the concept of network partition is introduced and a Clustering-based Network Partition Algorithm (CNPA) is then proposed to partition the network. The CNPA can guarantee that each partition is able to shorten the maximum end-to-end latency between controllers and switches. To further decrease the queuing latency of controllers, appropriate multiple controllers are then placed in the subnetworks. Extensive simulations are conducted under two real network topologies from the Internet Topology Zoo. The results verify that the proposed algorithm can remarkably reduce the maximum latency between controllers and their associated switches