Thanks to the Software-Defined Networking (SDN) paradigm, which segregates the control and data layers of traditional networks, large and scalable networks can now be dynamically configured and managed. It is a game-changing networking technology that provides increased flexibility and scalability through centralized management. The Controller Placement Problem (CPP), however, poses a crucial problem in SDN because it directly impacts the efficiency and performance of the network. The CPP attempts to determine the most ideal number of controllers for any network and their corresponding relative positioning. This is to generally minimize communication delays between switches and controllers and maintain network reliability and resilience. In this thesis, we present a modified Genetic Algorithm (GA) technique to solve the CPP efficiently. Our approach makes use the GA’s capabilities to obtain the best controller placement correlation based on important factors such as network delay, reliability and availability. We further optimize the process by means of certain deduced constraints to allow faster convergence. In this study, our primary objective is to optimize the control plane design by identifying the optimal controller placement, which minimizes delay and significantly improves both the switch-to-controller and controller-to-controller link availability. We introduce an advanced genetic algorithm methodology and showcase a precise technique for optimizing the inherent availability constraints. To evaluate the trade-offs between the deployment of controllers and the associated costs of enhancing particular node link availabilities, we performed computational experiments on three distinct networks of varying sizes. Overall, our work contributes to the growth trajectory of SDN research by offering a novel GA-based resolution to the controller placement problem that can improve network performance and dependability.

Data centers consume a significant amount of energy. This problem is aggravated by the fact that most servers and desktops are underutilized when powered on, and still consume a majority of the energy of a fully utilized computer even when idle This problem would be much worse were it not for the growing use of virtual machines. Virtual machines allow system administrators to more fully utilize hardware capabilities by putting more than one virtual system on the same physical server. Many times, virtual machines are placed onto physical servers inefficiently. To address this inefficiency, I developed a new family of packing algorithms. This family of algorithms is meant to solve the problem of packing virtual machines onto a cluster of physical servers. This problem is different than the conventional bin packing problem in two ways. First, each server has multiple resources that can be consumed. Second, loads on virtual machines are probabilistic and not completely known to the packing algorithm. We first compare our developed algorithm with other bin packing algorithms and show that it performs better than state-of-the-art genetic algorithms in literature. We then show the general feasibility of our algorithm in packing real virtual machines on physical servers.

Artificial Neural networks have found many applications in various fields such as function approximation, time-series prediction, and adaptive control. The performance of a neural network depends on many factors, including the network structure, the selection of activation functions, the learning rate of the training algorithm, and initial synaptic weight values, etc. Genetic algorithms are inspired by Charles Darwin’s theory of natural selection (“survival of the fittest”). They are heuristic search techniques that are based on aspects of natural evolution, such as inheritance, mutation, selection, and crossover. This research utilizes a genetic algorithm to optimize multi-layer feedforward neural network performance and structure. The goal is to minimize both the function of output errors and the number of connections of network. The algorithm is modeled in C++ and tested on several different data sets. Computer simulation results show that the proposed algorithm can successfully determine the appropriate network size for optimal performance. This research also includes studies of the effects of population size, crossover type, probability of bit mutation, and the error scaling factor.

In the design of material handling systems with complex and unpredictable dynamics, conventional search and optimization approaches that are based only on performance measures offer little guarantee of robustness. Using evidence from research into complex systems, the use of behavior-based optimization is proposed, which takes advantage of observed relationships between complexity and optimality with respect to both performance and robustness. Based on theoretical complexity measures, particularly algorithmic complexity, several simple complexity measures are created. The relationships between these measures and both performance and robustness are examined, using a model of a directly connected material handling system as a backdrop. The fundamental causes of the relationships and their applicability in the proposed behavior-based optimization approach are discussed. / Ph. D.

The shipboard power system supplies energy to sophisticated systems for weapons, communications, navigation, and operation. After a fault is encountered, reconfiguration of a shipboard power system becomes a critical activity that is required to either restore service to a lost load or to meet some operational requirements of the ship. Reconfiguration refers to changing the topology of the power system in order to isolate system damage and/or optimize certain characteristics of the system related to power efficiency. When finding the optimal state, it is important to have a method that finds the desired state within a short amount of time, in order to allow fast response for the system. Since the reconfiguration problem is highly nonlinear over a domain of discrete variables, the genetic algorithm method is a suitable candidate. In this thesis, a reconfiguration methodology, using a genetic algorithm, is presented that will reconfigure a network, satisfying the operational requirements and priorities of loads. Graph theory is utilized to represent the shipboard power system topology in matrices. The reconfiguration process and the genetic algorithm are implemented in MATLAB and tested on an 8-bus power system model and on larger power system with distributed generators by considering different fault scenarios. Each test system was reconfigured in three different ways: by considering load priority, without considering load priority, and by combining priority factor and magnitude factor. The test results accuracy was verified through hand checking.

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