Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Scheduling

Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling

Cloud computing - The effect of generalized spring tensor algorithm on load balancing

© 2014 IEEE. In business world, competitors use innovative approaches to improve their performance and profits. Cloud computing is one of these creative concepts that allowed companies to further taking advantage of their potential. Cloud computing is assisting companies to execute their business plans more efficiently. As cloud computing has multi-tenancy structure, availability and efficiency of the resources is essential foundation of the cloud architecture. Recent studies showed that, optimized cloud computing could be seen as an elastic network of resources that are interacting with each other, to minimize the waiting time and utilize the throughput. Therefore load balancing and resource management can be highlighted as the main concerns in cloud computing as they are impacting the network performance directly. This research aims to discuss the current challenges existing in load balancing algorithms. Different metrics and policies of the relevant load balancer algorithms have been investigated and as a result, collective behavior has been proposed as a new policy for classification of elasticity mechanism in load balancing

Open Source Solutions for Building IaaS Clouds

Cloud Computing is not only a pool of resources and services offered through the internet, but also a technology solution that allows optimization of resources use, costs minimization and energy consumption reduction. Enterprises moving towards cloud technologies have to choose between public cloud services, such as: Amazon Web Services, Microsoft Cloud and Google Cloud services, or private self built clouds. While the firsts are offered with affordable fees, the others provide more privacy and control. In this context, many open source softwares approach the buiding of private, public or hybrid clouds depending on the users need and on the available capabilities. To choose among the different open source solutions, an analysis is necessary in order to select the most suitable according with the enterprise’s goals and requirements. In this paper, we present a depth study and comparison of five open source frameworks that are gaining more attention recently and growing fast: CloudStack, OpenStack, Eucalyptus, OpenNebula and Nimbus. We present their architectures and discuss different properties, features, useful information and our own insights on these frameworks

EPOS Security & GDPR Compliance

Since May 2018, companies have been required to comply with the General Data Protection Regulation (GDPR). This means that many companies had to change their methods of collecting and processing EU citizens’ data. The compliance process can be very expensive, for example, more specialized human resources are needed, who need to study the regulations and then implement the changes in the IT applications and infrastructures. As a result, new measures and methods need to be developed and implemented, making this process expensive. This project is part of the EPOS project. EPOS allows data on earth sciences from various research institutes in Europe to be shared and used. The data is stored in a database and in some file systems and in addition, there is web services for data mining and control. The EPOS project is a complex distributed system and therefore it is important to guarantee not only its security, but also that it is compatible with GDPR. The need to automate and facilitate this compliance and verification process was identified, in particular the need to develop a tool capable of analyzing applications web. This tool can provide companies in general an easier and faster way to check the degree of compliance with the GDPR in order to assess and implement any necessary changes. With this, PADRES was developed that contains the main points of GDPR organized by principles in the form of checklist which are answered manually. When submitted, a security analysis is also performed based on NMAP and ZAP together with the cookie analyzer. Finally, a report is generated with the information obtained together with a set of suggestions based on the responses obtained from the checklist. Applying this tool to EPOS, most of the points related to GDPR were answered as being in compliance although the rest of the suggestions were generated to help improve the level of compliance and also improve general data management. In the exploitation of vulnerabilities, some were found to be classified as high risk, but most were found to be classified as medium risk.Desde maio de 2018 que as empresas precisam de cumprir o Regulamento Geral de Proteção de Dados (GDPR). Isso significa que muitas empresas tiveram que mudar seus métodos de como recolhem e processam os dados dos cidadãos da UE. O processo de conformidade pode ser muito caro, por exemplo, são necessários recursos humanos mais especializados, que precisam estudar os regulamentos e depois implementar as alterações nos aplicativos e infraestruturas de TI. Com isso novas medidas e métodos precisam ser desenvolvidos e implementados, tornando esse processo caro. Este projeto está inserido no projeto European Plate Observing System (EPOS). O EPOS permite que dados sobre ciências da terra de vários institutos de pesquisa na Europa sejam compartilhados e usados. Os dados são armazenados em base de dados e em alguns sistema de ficheiros e além disso, existem web services para controle e mineração de dados. O projeto EPOS é um sistema distribuído complexo e portanto, é importante garantir não apenas sua segurança, mas também que seja compatível com o GDPR. Foi identificada a necessidade de automatizar e facilitar esse processo, em particular a necessidade de desenvolver uma ferramenta capaz de analisar aplicações web. Essa ferramenta, chamada PrivAcy, Data REgulation and Security (PADRES) pode fornecer às empresas uma maneira mais fácil e rápida de verificar o grau de conformidade com o GDPR com o objetivo de avaliar e implementar quaisquer alterações necessárias. Com isto, esta ferramenta contém os pontos principais do General Data Protection Regulation (GDPR) organizado por princípios em forma duma lista de verificação, os quais são respondidos manualmente. Como os conceitos de privacidade e segurança se complementam, foi também incluída a procura por vulnerabilidades em aplicações web. Ao integrar as ferramentas de código aberto como o Network Mapper (NMAP) ou Zed Attack Proxy (ZAP), é possível então testar a aplicações contra as vulnerabilidades mais frequentes segundo o Open Web Application Security Project (OWASP) Top 10. Aplicando esta ferramenta no EPOS, a maioria dos pontos relativos ao GDPR foram respondidos como estando em conformidade apesar de nos restantes terem sido geradas as respetivas sugestões para ajudar a melhorar o nível de conformidade e também melhorar o gerenciamento geral dos dados. Na exploração das vulnerabilidades foram encontradas algumas classificadas com risco elevado mas na maioria foram encontradas mais com classificação média

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot