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The Infrastructure as a Service (IaaS) platform in cloud computing provides resources as a service from a pool of compute, network, and storage resources. One of the major challenges facing cloud computing is to predict the usage of these resources in real time. By knowing future demands, cloud data centres can dynamically scale resources to decrease energy consumption while maintaining a high quality of service. However cloud resource consumption is ever changing, making it difficult for accurate predictions to be produced. This motivates the research presented in this paper which aims to predict in advance the level of CPU consumption of a host. This research implements evolutionary Neural Networks (NN), a powerful machine learning method, to make these predictions. A number of state of the art swarm and evolutionary optimization algorithms are implemented to train the neural networks to predict host utilization: Particle Swarm Optimization (PSO), Differential Evolution (DE) and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). The results of this research demonstrate that CMA-ES converges faster to a better solution on the training data. However when evaluated on the test data, DE performs statistically equal to CMA-ES. The results also demonstrate that the trained networks are still accurate when applied to CPU utilization data from different hosts with no further training needed. When evaluated to predict multiple steps into the future, the accuracy of the network understandably decreases but still performs well on average.

Multi-objective optimisation has received considerable attention in recent years as many real world problems have multiple conflicting objectives. There is an additional layer of complexity when considering multi-objective problems in dynamic environments due to the changing nature of the problem. A novel Multi-Objective Neural Network trained with Differential Evolution (MONNDE) is presented in this research. MONNDE utilizes Neural Network function approximators to address dynamic multi-objective optimisation problems. Differential Evolution (DE) is a state of the art single objective global optimisation problems and will be used to evolve neural networks capable of generating Pareto fronts. The proposed MONNDE algorithm has the added advantage of developing an approximation of the problem that can produce further Pareto fronts as the environment changes with no further optimisation needed. The MONNDE framework is applied to the Dynamic Economic Emission Dispatch (DEED) problem and performs equally optimal when compared to other state of the art algorithms in terms of the 24 h cost and emissions. This research also compares the performance of fully and partially connected networks and discovers that dynamically optimising the topology of the neural networks performs better in an online learning environment than simply optimising the network weights.

Neuroevolution refers to evolving neural networks using evolutionary methods. These algorithms have been applied to many problem domains, from game playing to robotics, which motivates this research. The problem of watershed management is addressed here in this research using the most prominent neuroevolution algorithms, i.e. NeuroEvolution of Augmenting Topologies (NEAT), neuro differential evolution and Enforced SubPopulations . The results indicate that neuroevolution is a suitable approach at addressing the watershed management problem, outperforming the other methods of neural network training.

Particle swarm optimisation (PSO) is a general purpose optimisation algorithm used to address hard optimisation problems. The algorithm operates as a result of a number of particles converging on what is hoped to be the best solution. How the particles move through the problem space is therefore critical to the success of the algorithm. This study utilises meta optimisation to compare a number of velocity update equations to determine which features of each are of benefit to the algorithm. A number of hybrid velocity update equations are proposed based on other high performing velocity update equations. This research also presents a novel application of PSO to train a neural network function approximator to address the watershed management problem. It is found that the standard PSO with a linearly changing inertia, the proposed hybrid Attractive Repulsive PSO with avoidance of worst locations (AR PSOAWL) and Adaptive Velocity PSO (AV PSO) provide the best performance overall. The results presented in this paper also reveal that commonly used PSO parameters do not provide the best performance. Increasing and negative inertia values were found to perform better.

The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine learning algorithms used for control problems and has had many successful applications in the area of building energy management. This research gives a comprehensive review of the literature relating to the application of reinforcement learning to developing autonomous building energy management systems. The main direction for future research and challenges in reinforcement learning are also outlined., 17 pages, 3 figures

Collecting, analyzing and gaining insight from large volumes of data is now the norm in an ever increasing number of industries. Data analytics techniques, such as machine learning, are powerful tools used to analyze these large volumes of data. Synthetic data sets are routinely relied upon to train and develop such data analytics methods for several reasons: to generate larger data sets than are available, to generate diverse data sets, to preserve anonymity in data sets with sensitive information, etc. Processing, transmitting and storing data is a key issue faced when handling large data sets. This paper presents an "On the fly" framework for generating big synthetic data sets, suitable for these data analytics methods, that is both computationally efficient and applicable to a diverse set of problems. An example application of the proposed framework is presented along with a mathematical analysis of its computational efficiency, demonstrating its effectiveness., Comment: 13 pages, 3 figures

One of the major challenges facing cloud computing is to accurately predict future resource usage for future demands. Cloud resource consumption is constantly changing, which makes it difficult for forecasting algorithms to produce accurate predictions. This motivates the research presented in this paper which aims to predict host machines CPU consumption for a single time-step and multiple time-steps into the future. This research implements a Recurrent Neural Network to predict CPU utilisation, due to their ability to retain information and accurately make predictions for time series problems, making it a promising candidate to predict CPU utilization with greater accuracy when compared to traditional approaches.

Reinforcement Learning (RL) is a powerful and well-studied Machine Learning paradigm, where an agent learns to improve its performance in an environment by maximising a reward signal. In multi-objective Reinforcement Learning (MORL) the reward signal is a vector, where each component represents the performance on a different objective. Reward shaping is a well-established family of techniques that have been successfully used to improve the performance and learning speed of RL agents in single-objective problems. The basic premise of reward shaping is to add an additional shaping reward to the reward naturally received from the environment, to incorporate domain knowledge and guide an agent’s exploration. Potential-Based Reward Shaping (PBRS) is a specific form of reward shaping that offers additional guarantees. In this paper, we extend the theoretical guarantees of PBRS to MORL problems. Specifically, we provide theoretical proof that PBRS does not alter the true Pareto front in both single- and multi-agent MORL. We also contribute the first published empirical studies of the effect of PBRS in single- and multi-agent MORL problems.

This research presents a novel framework for evolving Multi-Objective Neural Networks using Differential Evolution (MONNDE). In recent years, the Differential Evolution algorithm has shown to be an effective and robust global optimisation algorithm. The algorithm uses evolutionary operators to optimise complex and continuous problem spaces and has been applied to a range of problems, recently including neural networks. This research continues this trend by utilizing differential evolution to evolve neural networks capable of addressing dynamic problems with multiple objectives. The proposed MONNDE framework is applied to the Dynamic Economic Emission Dispatch (DEED) problem. This problem consists of scheduling a group of power generators in a manner that minimises both cost and emissions produced by the generators. The power generators must also meet a series of constraints relating to their power output, power demand and network loss. The proposed MONNDE is performs very competitively when compared to algorithms such as NSGA-II, PSO, PSOAWL and MARL.