Configuração automática de plataforma de gestão de desempenho em ambientes NFV e SDN

Mestrado em Engenharia de Computadores e TelemáticaWith 5G set to arrive within the next three years, this next-generation of mobile networks will transform the mobile industry with a profound impact both on its customers as well as on the existing technologies and network architectures. Software-Defined Networking (SDN), together with Network Functions Virtualization (NFV), are going to play key roles for the operators as they prepare the migration from 4G to 5G allowing them to quickly scale their networks. This dissertation will present a research work done on this new paradigm of virtualized and programmable networks focusing on the performance management, supervision and monitoring domains, aiming to address Self-Organizing Networks (SON) scenarios in a NFV/SDN context, with one of the scenarios being the detection and prediction of potential network and service anomalies. The research work itself was done while participating in a R&D project designated SELFNET (A Framework for Self-Organized Network Management in Virtualized and Software Defined Networks) funded by the European Commission under the H2020 5G-PPP programme, with Altice Labs being one of the participating partners of this project. Performance management system advancements in a 5G scenario require aggregation, correlation and analysis of data gathered from these virtualized and programmable network elements. Both opensource monitoring tools and customized catalog-driven tools were either integrated on or developed with this purpose, and the results show that they were able to successfully address these requirements of the SELFNET project. Current performance management platforms of the network operators in production are designed for non virtualized (non- NFV) and non programmable (non-SDN) networks, and the knowledge gathered while doing this research work allowed Altice Labs to understand how its Altaia performance management platform must evolve in order to be prepared for the upcoming 5G next generation mobile networks.Com o 5G prestes a chegar nos próximos três anos, esta próxima geração de redes móveis irá transformar a indústria de telecomunicações móveis com um impacto profundo nos seus clientes assim como nas tecnologias e arquiteturas de redes. As redes programáveis (SDN), em conjunto com a virtualização de funções de rede (NFV), irão desempenhar papéis vitais para as operadoras na sua migração do 4G para o 5G, permitindo-as escalar as suas redes rapidamente. Esta dissertação irá apresentar um trabalho de investigação realizado sobre este novo paradigma de virtualização e programação de redes, concentrando-se no domínio da gestão de desempenho, supervisionamento e monitoria, abordando cenários de redes auto-organizadas (SON) num contexto NFV/SDN, sendo um destes cenários a deteção e predição de potenciais anomalias de redes e serviços. O trabalho de investigação foi enquadrado num projeto de I&D designado SELFNET (A Framework for Self-Organized Network Management in Virtualized and Software Defined Networks) financiado pela Comissão Europeia no âmbito do programa H2020 5G-PPP, sendo a Altice Labs um dos parceiros participantes deste projeto. Avanços em sistemas de gestão de desempenho em cenários 5G requerem agregação, correlação e análise de dados recolhidos destes elementos de rede programáveis e virtualizados. Ferramentas de monitoria open-source e ferramentas catalog-driven foram integradas ou desenvolvidas com este propósito, e os resultados mostram que estas preencheram os requisitos do projeto SELFNET com sucesso. As plataformas de gestão de desempenho das operadoras de rede atualmente em produção estão concebidas para redes não virtualizadas (non-NFV) e não programáveis (non- SDN), e o conhecimento adquirido durante este trabalho de investigação permitiu à Altice Labs compreender como a sua plataforma de gestão de desempenho (Altaia) terá que evoluir por forma a preparar-se para a próxima geração de redes móveis 5G

CloudBench: an integrated evaluation of VM placement algorithms in clouds

A complex and important task in the cloud resource management is the efficient allocation of virtual machines (VMs), or containers, in physical machines (PMs). The evaluation of VM placement techniques in real-world clouds can be tedious, complex and time-consuming. This situation has motivated an increasing use of cloud simulators that facilitate this type of evaluations. However, most of the reported VM placement techniques based on simulations have been evaluated taking into account one specific cloud resource (e.g., CPU), whereas values often unrealistic are assumed for other resources (e.g., RAM, awaiting times, application workloads, etc.). This situation generates uncertainty, discouraging their implementations in real-world clouds. This paper introduces CloudBench, a methodology to facilitate the evaluation and deployment of VM placement strategies in private clouds. CloudBench considers the integration of a cloud simulator with a real-world private cloud. Two main tools were developed to support this methodology, a specialized multi-resource cloud simulator (CloudBalanSim), which is in charge of evaluating VM placement techniques, and a distributed resource manager (Balancer), which deploys and tests in a real-world private cloud the best VM placement configurations that satisfied user requirements defined in the simulator. Both tools generate feedback information, from the evaluation scenarios and their obtained results, which is used as a learning asset to carry out intelligent and faster evaluations. The experiments implemented with the CloudBench methodology showed encouraging results as a new strategy to evaluate and deploy VM placement algorithms in the cloud.This work was partially funded by the Spanish Ministry of Economy, Industry and Competitiveness under the Grant TIN2016-79637-P “Towards Unifcation of HPC and Big Data Paradigms” and by the Mexican Council of Science and Technology (CONACYT) through a Ph.D. Grant (No. 212677)

Multi-layer quality-aware (MULQA) cloud framework

In the past few years, the popularity of cloud-based solutions in the IT domain has been increased significantly as the consequence of the industry shift towards IoT, super-fast computer networks and notably the benefits of emerged cloud computing. However, this leads to many technical challenges such as optimizing the infrastructure for heterogeneous applications especially the quality sensitive types, and issues toward addressing different quality attributes simultaneously. In this research, we propose MULQA, an autonomic framework that monitors and estimates the quality metrics in physical, infrastructure, platform and software layers of an open source cloud system, and ensures the quality of the targeted metrics by triggering appropriate actions. MULQA is a novel approach providing such framework which targets different quality metrics in all layers of the cloud. During this thesis, we describe MULQA framework where the analyze module, predicts the violation status of the quality metrics and this predicted information will be used to create events for the finite state machine of the planning platform. This control mechanism consists of Normal, Warning and Transition states. Warning state is used to prepare the cloud for the transition state, while transition state prevents the violations and brings back the system to the normal state. Being a modular framework, MULQA provides generic functionalities and modules that can be selectively changed by additional user-written code, which can be used to test proposed algorithms for Monitor, Analyze, Plan and Execute modules. MULQA framework is built to overcome the challenges in providing a loosely coupled system which can be easily distributed and customized through an API. Furthermore, this framework is compatible with Openstack architecture and is able to monitor and control the components that the cloud middleware doesn’t have access to. The use-case in this thesis, is a three-tier Web application which is deployed with Openstack. Experimental results of the tests which focus on the performance QA, show that MULQA can increase the success rate of requests sent by 32%, 69% and 94% for request concurrency numbers of 200, 500 and 1000 in order. Moreover, throughput has been improved five times with low impact on the CPU utilization

Advances in Information Security and Privacy

With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue