Optimization and Management Techniques for Geo-distributed SDN-enabled Cloud Datacenters\u27 Provisioning

Cloud computing has become a business reality that impacts technology users around the world. It has become a cornerstone for emerging technologies and an enabler of future Internet services as it provides on-demand IT services delivery via geographically distributed data centers. At the core of cloud computing, virtualization technology has played a crucial role by allowing resource sharing, which in turn allows cloud service providers to offer computing services without discrepancies in platform compatibility. At the same time, a trend has emerged in which enterprises are adopting a software-based network infrastructure with paradigms, such as software-defined networking, gaining further attention for large-scale networks. This trend is due to the flexibility and agility offered to networks by such paradigms. Software-defined networks allow for network resource sharing by facilitating network virtualization. Hence, combining cloud computing with a software-defined network architecture promises to enhance the quality of services that are delivered to clients and reduces the operational costs to service providers. However, this combined architecture introduces several challenges to cloud service providers, including resource management, energy efficiency, virtual network provisioning, and controller placement. This thesis tackles these challenges by proposing innovative resource provisioning techniques and developing novel frameworks to improve resource utilization, power efficiency, and quality of service performance. These metrics have a direct impact on the capital and operational expenditure of service providers. In this thesis, the problem of virtual computing and network provisioning in geographically distributed software-defined network-enabled cloud data centers is modeled and formulated. It proposes and evaluates optimal and sub-optimal heuristic solutions to validate their efficiency. To address the energy efficiency of cloud environments that are enabled for software-defined networks, this thesis presents an innovative architecture and develops a comprehensive power consumption model that accurately describes the power consumption behavior of such environments. To address the challenge of the number of software-defined network controllers and locations, a sub-optimal solution is proposed that combines unsupervised hierarchical clustering. Finally, betweenness centrality is proposed as an efficient solution to the controller placement problem

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

Secure Multi-Path Selection with Optimal Controller Placement Using Hybrid Software-Defined Networks with Optimization Algorithm

The Internet's growth in popularity requires computer networks for both agility and resilience. Recently, unable to satisfy the computer needs for traditional networking systems. Software Defined Networking (SDN) is known as a paradigm shift in the networking industry. Many organizations are used SDN due to their efficiency of transmission. Striking the right balance between SDN and legacy switching capabilities will enable successful network scenarios in architecture networks. Therefore, this object grand scenario for a hybrid network where the external perimeter transport device is replaced with an SDN device in the service provider network. With the moving away from older networks to SDN, hybrid SDN includes both legacy and SDN switches. Existing models of SDN have limitations such as overfitting, local optimal trapping, and poor path selection efficiency. This paper proposed a Deep Kronecker Neural Network (DKNN) to improve its efficiency with a moderate optimization method for multipath selection in SDN. Dynamic resource scheduling is used for the reward function the learning performance is improved by the deep reinforcement learning (DRL) technique. The controller for centralised SDN acts as a network brain in the control plane. Among the most important duties network is selected for the best SDN controller. It is vulnerable to invasions and the controller becomes a network bottleneck. This study presents an intrusion detection system (IDS) based on the SDN model that runs as an application module within the controller. Therefore, this study suggested the feature extraction and classification of contractive auto-encoder with a triple attention-based classifier. Additionally, this study leveraged the best performing SDN controllers on which many other SDN controllers are based on OpenDayLight (ODL) provides an open northbound API and supports multiple southbound protocols. Therefore, one of the main issues in the multi-controller placement problem (CPP) that addresses needed in the setting of SDN specifically when different aspects in interruption, ability, authenticity and load distribution are being considered. Introducing the scenario concept, CPP is formulated as a robust optimization problem that considers changes in network status due to power outages, controller’s capacity, load fluctuations and changes in switches demand. Therefore, to improve network performance, it is planned to improve the optimal amount of controller placements by simulated annealing using different topologies the modified Dragonfly optimization algorithm (MDOA)

Joint Energy Efficient and QoS-aware Path Allocation and VNF Placement for Service Function Chaining

Service Function Chaining (SFC) allows the forwarding of a traffic flow along a chain of Virtual Network Functions (VNFs, e.g., IDS, firewall, and NAT). Software Defined Networking (SDN) solutions can be used to support SFC reducing the management complexity and the operational costs. One of the most critical issues for the service and network providers is the reduction of energy consumption, which should be achieved without impact to the quality of services. In this paper, we propose a novel resource (re)allocation architecture which enables energy-aware SFC for SDN-based networks. To this end, we model the problems of VNF placement, allocation of VNFs to flows, and flow routing as optimization problems. Thereafter, heuristic algorithms are proposed for the different optimization problems, in order find near-optimal solutions in acceptable times. The performance of the proposed algorithms are numerically evaluated over a real-world topology and various network traffic patterns. The results confirm that the proposed heuristic algorithms provide near optimal solutions while their execution time is applicable for real-life networks.Comment: Extended version of submitted paper - v7 - July 201