Dynamic Network Slicing Using Deep Reinforcement Learning

Nowadays network slicing is one of the biggest drivers of new elements in the 5G network business. This is because this paradigm allows the creation of independent slices, with their virtually and logically separated radio, network and computational resources. Using network slicing, operators sell infrastructure resources of any kind to tenants, while tenants use these resources to sell services to their customers, the end users. In this context, a problem that is essential to solve is how to improve the operator’s profit, ensuring compliance with the requests’ SLAs and distributing network resources in order to increase its usage rate. This dissertation proposes to design two algorithms based on DRL for slice admission in the transport network, learning which request to accept and reject while guaranteeing the requirements of the tenants requests. The contributions of this study start with the formalization of the problem of slice admission, followed by its simulation and implementation of DRL agents using Containernet, the Ryu controller, OpenAI Gym and the PyTorch framework. The result is two DRL-based algorithms capable of achieving good performances in this simulated scenario.Atualmente o network slicing é um dos maiores potenciadores de novos elementos no negócio das redes 5G. Isto deve-se ao facto de este paradigma permitir a criação de slices independentes, com os seus recursos rádio, de rede e computacionais virtual e logicamente separados. Utilizando network slicing, as operadoras poderão vender recursos de infraestrutura de qualquer tipo a tenants. Os tenants utilizam estes recursos para vender serviços aos seus clientes, os utilizadores finais. Neste contexto, um problema que é fundamental resolver é o de como melhorar o lucro da operadora, garantindo o cumprimento dos SLAs dos pedidos e distribuindo os recursos da rede de forma a aumentar a sua utilização. Nesta dissertação propõe-se desenhar dois algoritmos baseados em DRL para a admissão de slices na rede de transporte, aprendendo que pedidos aceitar e rejeitar, procurando satisfazer sempre os requisitos dos pedidos dos tenants. Os contributos deste estudo passam pela formalização do problema da admissão de slices na rede, seguindo-se a sua simulação e implementação dos agentes utilizando conjuntamente o Containernet, o controlador Ryu, o OpenAI Gym e o framework PyTorch. O resultado são dois algoritmos baseados em DRL capazes de atingir boas performances neste cenário simulado

Performance modelling for scalable deep learning

Performance modelling for scalable deep learning is very important to quantify the efficiency of large parallel workloads. Performance models are used to obtain run-time estimates by modelling various aspects of an application on a target system. Designing performance models requires comprehensive analysis in order to build accurate models. Limitations of current performance models include poor explainability in the computation time of the internal processes of a neural network model and limited applicability to particular architectures. Existing performance models in deep learning have been proposed, which are broadly categorized into two methodologies: analytical modelling and empirical modelling. Analytical modelling utilizes a transparent approach that involves converting the internal mechanisms of the model or applications into a mathematical model that corresponds to the goals of the system. Empirical modelling predicts outcomes based on observation and experimentation, characterizes algorithm performance using sample data, and is a good alternative to analytical modelling. However, both these approaches have limitations, such as poor explainability in the computation time of the internal processes of a neural network model and poor generalisation. To address these issues, hybridization of the analytical and empirical approaches has been applied, leading to the development of a novel generic performance model that provides a general expression of a deep neural network framework in a distributed environment, allowing for accurate performance analysis and prediction. The contributions can be summarized as follows: In the initial study, a comprehensive literature review led to the development of a performance model based on synchronous stochastic gradient descent (S-SGD) for analysing the execution time performance of deep learning frameworks in a multi-GPU environment. This model’s evaluation involved three deep learning models (Convolutional Neural Networks (CNN), Autoencoder (AE), and Multilayer Perceptron (MLP)), implemented in three popular deep learning frameworks (MXNet, Chainer, and TensorFlow) respectively, with a focus on following an analytical approach. Additionally, a generic expression for the performance model was formulated, considering intrinsic parameters and extrinsic scaling factors that impact computing time in a distributed environment. This formulation involved a global optimization problem with a cost function dependent on unknown constants within the generic expression. Differential evolution was utilized to identify the best fitting values, matching experimentally determined computation times. Furthermore, to enhance the accuracy and stability of the performance model, regularization techniques were applied. Lastly, the proposed generic performance model underwent experimental evaluation in a real-world application. The results of this evaluation provided valuable insights into the influence of hyperparameters on performance, demonstrating the robustness and applicability of the performance model in understanding and optimizing model behavior

Performance Analysis of Distributed and Scalable Deep Learning

With renewed global interest for Artificial Intelligence (AI) methods, the past decade has seen a myriad of new programming models and tools that enable better and faster Machine Learning (ML). More recently, a subset of ML known as Deep Learning (DL) raised an increased interest due to its inherent ability to tackle efficiently novel cognitive computing applications. DL allows computational models that are composed of multiple processing layers to learn in an automated way representations of data with multiple levels of abstraction, and can deliver higher predictive accuracy when trained on larger data sets. Based on Artificial Neural Networks (ANN), DL is now at the core of state of the art voice recognition systems (which enable easy control over e.g. Internet-of- Things (IoT) smart home appliances for instance), self-driving car engine, online recommendation systems. The ecosystem of DL frameworks is fast evolving, as well as the DL architectures that are shown to perform well on specialized tasks and to exploit GPU accelerators. For this reason, the frequent performance evaluation of the DL ecosystem is re- quired, especially since the advent of novel distributed training frameworks such as Horovod allowing for scalable training across multiple computing resources. In this paper, the scalability evaluation of the reference DL frameworks (Tensorflow, Keras, MXNet, and PyTorch) is performed over up-to-date High Performance Comput- ing (HPC) resources to compare the efficiency of differ- ent implementations across several hardware architectures (CPU and GPU). Experimental results demonstrate that the DistributedDataParallel features in the Pytorch library seem to be the most efficient framework for distributing the training process across many devices, allowing to reach a throughput speedup of 10.11 when using 12 NVidia Tesla V100 GPUs when training Resnet44 on the CIFAR10 dataset