Using Computational Significance and Resilience in System Software Stacks.:Keynote Talk

This talk explores how the runtime system and operating system can leverage metrics that express the significance and resilience of application components in order to reduce the energy footprint of parallel applications. We will explore in particular how software can tolerate and indeed exploit higher error rates in future processors and memory technologies that may operate outside their safe margins.

From data to applications in the Internet of Things

Con la crescita in complessità delle infrastrutture IT e la pervasività degli scenari di Internet of Things (IoT) emerge il bisogno di nuovi modelli computazionali basati su entità autonome capaci di portare a termine obiettivi di alto livello interagendo tra loro grazie al supporto di infrastrutture come il Fog Computing, per la vicinanza alle sorgenti dei dati, e del Cloud Computing per offrire servizi analitici complessi di back-end in grado di fornire risultati per milioni di utenti. Questi nuovi scenarii portano a ripensare il modo in cui il software viene progettato e sviluppato in una prospettiva agile. Le attività dei team di sviluppatori (Dev) dovrebbero essere strettamente legate alle attività dei team che supportano il Cloud (Ops) secondo nuove metodologie oggi note come DevOps. Tuttavia, data la mancanza di astrazioni adeguata a livello di linguaggio di programmazione, gli sviluppatori IoT sono spesso indotti a seguire approcci di sviluppo bottom-up che spesso risulta non adeguato ad affrontare la compessità delle applicazione del settore e l'eterogeneità dei compomenti software che le formano. Poichè le applicazioni monolitiche del passato appaiono difficilmente scalabili e gestibili in un ambiente Cloud con molteplici utenti, molti ritengono necessaria l'adozione di un nuovo stile architetturale, in cui un'applicazione dovrebbe essere vista come una composizione di micro-servizi, ciascuno dedicato a uno specifica funzionalità applicativa e ciascuno sotto la responsabilità di un piccolo team di sviluppatori, dall'analisi del problema al deployment e al management. Poichè al momento non si è ancora giunti a una definizione univoca e condivisa dei microservices e di altri concetti che emergono da IoT e dal Cloud, nè tantomento alla definzione di linguaggi sepcializzati per questo settore, la definzione di metamodelli custom associati alla produzione automatica del software di raccordo con le infrastrutture potrebbe aiutare un team di sviluppo ad elevare il livello di astrazione, incapsulando in una software factory aziendale i dettagli implementativi. Grazie a sistemi di produzione del sofware basati sul Model Driven Software Development (MDSD), l'approccio top-down attualmente carente può essere recuperato, permettendo di focalizzare l'attenzione sulla business logic delle applicazioni. Nella tesi viene mostrato un esempio di questo possibile approccio, partendo dall'idea che un'applicazione IoT sia in primo luogo un sistema software distribuito in cui l'interazione tra componenti attivi (modellati come attori) gioca un ruolo fondamentale.

Resumen

Con agrado, se presenta un nuevo número de la Revista Bibliotecas. Incluye investigaciones de interés acerca de los siguientes temas: arquetipos bibliotecarios, alfabetización informacional e informática en la nube. En el artículo dedicado a los arquetipos, el autor afirma que la profesión bibliotecológica, al igual que ocurre con otras disciplinas, está expuesta a una serie de estereotipos. Tales impresiones y creencias provocan que las personas, motivadas por el prejuicio, no incursionen en una fascinante disciplina; como consecuencia, inciden en la matrícula de estudiantes de primer ingreso. Este escrito expone la situación de la Escuela de Bibliotecología, Documentación e Información, de la Universidad Nacional, y plantea una propuesta didáctica en aras de mejorar la concepción de la bibliotecología entre la población estudiantil de secundaria.

Análise de risco para computação em nuvem

Têm-se notado nos últimos anos um crescimento na adoção de tecnologias de computação em nuvem, com uma adesão inicial por parte de particulares e pequenas empresas, e mais recentemente por grandes organizações. Esta tecnologia tem servido de base ao aparecimento de um conjunto de novas tendências, como a Internet das Coisas ligando os nossos equipamentos pessoais e wearables às redes sociais, processos de big data que permitem tipificar comportamentos de clientes ou ainda facilitar a vida ao cidadão com serviços de atendimento integrados. No entanto, tal como em todas as novas tendências disruptivas, que trazem consigo um conjunto de oportunidades, trazem também um conjunto de novos riscos que são necessários de serem equacionados. Embora este caminho praticamente se torne inevitável para uma grande parte de empresas e entidades governamentais, a sua adoção como funcionamento deve ser alvo de uma permanente avaliação e monitorização entre as vantagens e riscos associados. Para tal, é fundamental que as organizações se dotem de uma eficiente gestão do risco, de modo que possam tipificar os riscos (identificar, analisar e quantificar) e orientar-se de uma forma segura e metódica para este novo paradigma. Caso não o façam, os riscos ficam evidenciados, desde uma possível perda de competitividade face às suas congéneres, falta de confiança dos clientes, dos parceiros de negócio e podendo culminar numa total inatividade do negócio. Com esta tese de mestrado desenvolve-se uma análise genérica de risco tendo como base a Norma ISO 31000:2009 e a elaboração de uma proposta de registo de risco, que possa servir de auxiliar em processos de tomada de decisão na contratação e manutenção de serviços de Computação em Nuvem por responsáveis de organizações privadas ou estatais.

Towards a unified management of applications on heterogeneous clouds

The diversity in the way cloud providers o↵er their services, give their SLAs, present their QoS, or support di↵erent technologies, makes very difficult the portability and interoperability of cloud applications, and favours the well-known vendor lock-in problem. We propose a model to describe cloud applications and the required resources in an agnostic, and providers- and resources-independent way, in which individual application modules, and entire applications, may be re-deployed using different services without modification. To support this model, and after the proposal of a variety of cross-cloud application management tools by different authors, we propose going one step further in the unification of cloud services with a management approach in which IaaS and PaaS services are integrated into a unified interface. We provide support for deploying applications whose components are distributed on different cloud providers, indistinctly using IaaS and PaaS services.

Modelo de gestión CRM en VIMAR Ltda.

117 p.

Competing in the clouds: a strategic challenge for ITSP Ltd

By 2010, cloud computing had become established as a new model of IT provisioning for service providers. New market players and businesses emerged, threatening the business models of established market players. This teaching case explores the challenges arising through the impact of the new cloud computing technology on an established, multinational IT service provider called ITSP. Should the incumbent vendors adopt cloud computing offerings? And, if so, what form should those offerings take? The teaching case focuses on the strategic dimensions of technological developments, their threats and opportunities. It requires strategic decision making and forecasting under high uncertainty. The critical question is whether cloud computing is a disruptive technology or simply an alternative channel to supply computing resources over the Internet. The case challenges students to assess this new technology and plan ITSP’s responses.

Enabling efficient publicly verifiable outsourcing computation for matrix multiplication

Outsourcing heavy computational tasks to remote cloud server, which accordingly significantly reduce the computational burden at the end hosts, represents an effective and practical approach towards extensive and scalable mobile applications and has drawn increasing attention in recent years. However, due to the limited processing power of the end hosts yet the keen privacy concerns on the outsourced data, it is vital to ensure both the efficiency and security of the outsourcing computation in the cloud computing. In this paper, we address the issue by developing a publicly verifiable outsourcing computation proposal. In particular, considering a large amount of applications of matrix multiplication in large datasets and image processing, we propose a publicly verifiable outsourcing computation scheme for matrix multiplication in the amortized model. Security analysis demonstrates that the proposed scheme is provable secure by blinding input and output in a simple way. By comparing the developed scheme with existing proposals, we show that our proposal is more efficient in terms of functionality, as well as the computation, communication and storage overhead.

Real time cyber attack analysis on Hadoop ecosystem using machine learning algorithms

Big Data technologies are exciting cutting-edge technologies that generate, collect, store and analyse tremendous amount of data. Like any other IT revolution, Big Data technologies also have big challenges that are obstructing it to be adopted by wider community or perhaps impeding to extract value from Big Data with pace and accuracy it is promising. In this paper we first offer an alternative view of «Big Data Cloud» with the main aim to make this complex technology easy to understand for new researchers and identify gaps efficiently. In our lab experiment, we have successfully implemented cyber-attacks on Apache Hadoop's management interface «Ambari». On our thought about «attackers only need one way in», we have attacked the Apache Hadoop's management interface, successfully turned down all communication between Ambari and Hadoop's ecosystem and collected performance data from Ambari Virtual Machine (VM) and Big Data Cloud hypervisor. We have also detected these cyber-attacks with 94.0187% accurateness using modern machine learning algorithms. From the existing researchs, no one has ever attempted similar experimentation in detection of cyber-attacks on Hadoop using performance data.

An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data

Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on-demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first-class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy-aware manner. In this paper, we propose an energy-aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method.

Les enjeux juridiques concernant les nouveaux modèles d’affaires basés sur la commercialisation des données

Cet essai est présenté en tant que mémoire de maîtrise dans le cadre du programme de droit des technologies de l’information. Ce mémoire traite de différents modèles d’affaires qui ont pour caractéristique commune de commercialiser les données dans le contexte des technologies de l’information. Les pratiques commerciales observées sont peu connues et l’un des objectifs est d’informer le lecteur quant au fonctionnement de ces pratiques. Dans le but de bien situer les enjeux, cet essai discutera d’abord des concepts théoriques de vie privée et de protection des renseignements personnels. Une fois ce survol tracé, les pratiques de « data brokerage », de « cloud computing » et des solutions « analytics » seront décortiquées. Au cours de cette description, les enjeux juridiques soulevés par chaque aspect de la pratique en question seront étudiés. Enfin, le dernier chapitre de cet essai sera réservé à deux enjeux, soit le rôle du consentement et la sécurité des données, qui ne relèvent pas d’une pratique commerciale spécifique, mais qui sont avant tout des conséquences directes de l’évolution des technologies de l’information.

Interoperable Resource Brokering with Policy-based Provisioning and Job Allocation

The increasing needs for computational power in areas such as weather simulation, genomics or Internet applications have led to sharing of geographically distributed and heterogeneous resources from commercial data centers and scientific institutions. Research in the areas of utility, grid and cloud computing, together with improvements in network and hardware virtualization has resulted in methods to locate and use resources to rapidly provision virtual environments in a flexible manner, while lowering costs for consumers and providers. However, there is still a lack of methodologies to enable efficient and seamless sharing of resources among institutions. In this work, we concentrate in the problem of executing parallel scientific applications across distributed resources belonging to separate organizations. Our approach can be divided in three main points. First, we define and implement an interoperable grid protocol to distribute job workloads among partners with different middleware and execution resources. Second, we research and implement different policies for virtual resource provisioning and job-to-resource allocation, taking advantage of their cooperation to improve execution cost and performance. Third, we explore the consequences of on-demand provisioning and allocation in the problem of site-selection for the execution of parallel workloads, and propose new strategies to reduce job slowdown and overall cost.

Les enjeux juridiques concernant les nouveaux modèles d’affaires basés sur la commercialisation des données

Cet essai est présenté en tant que mémoire de maîtrise dans le cadre du programme de droit des technologies de l’information. Ce mémoire traite de différents modèles d’affaires qui ont pour caractéristique commune de commercialiser les données dans le contexte des technologies de l’information. Les pratiques commerciales observées sont peu connues et l’un des objectifs est d’informer le lecteur quant au fonctionnement de ces pratiques. Dans le but de bien situer les enjeux, cet essai discutera d’abord des concepts théoriques de vie privée et de protection des renseignements personnels. Une fois ce survol tracé, les pratiques de « data brokerage », de « cloud computing » et des solutions « analytics » seront décortiquées. Au cours de cette description, les enjeux juridiques soulevés par chaque aspect de la pratique en question seront étudiés. Enfin, le dernier chapitre de cet essai sera réservé à deux enjeux, soit le rôle du consentement et la sécurité des données, qui ne relèvent pas d’une pratique commerciale spécifique, mais qui sont avant tout des conséquences directes de l’évolution des technologies de l’information.

Towards near-threshold server processors

The popularity of cloud computing has led to a dramatic increase in the number of data centers in the world. The ever-increasing computational demands along with the slowdown in technology scaling has ushered an era of power-limited servers. Techniques such as near-threshold computing (NTC) can be used to improve energy efficiency in the post-Dennard scaling era. This paper describes an architecture based on the FD-SOI process technology for near-threshold operation in servers. Our work explores the trade-offs in energy and performance when running a wide range of applications found in private and public clouds, ranging from traditional scale-out applications, such as web search or media streaming, to virtualized banking applications. Our study demonstrates the benefits of near-threshold operation and proposes several directions to synergistically increase the energy proportionality of a near-threshold server.

Big data, smart homes and ambient assisted living

Objectives: To discuss how current research in the area of smart homes and ambient assisted living will be influenced by the use of big data. Methods: A scoping review of literature published in scientific journals and conference proceedings was performed, focusing on smart homes, ambient assisted living and big data over the years 2011-2014. Results: The health and social care market has lagged behind other markets when it comes to the introduction of innovative IT solutions and the market faces a number of challenges as the use of big data will increase. First, there is a need for a sustainable and trustful information chain where the needed information can be transferred from all producers to all consumers in a structured way. Second, there is a need for big data strategies and policies to manage the new situation where information is handled and transferred independently of the place of the expertise. Finally, there is a possibility to develop new and innovative business models for a market that supports cloud computing, social media, crowdsourcing etc. Conclusions: The interdisciplinary area of big data, smart homes and ambient assisted living is no longer only of interest for IT developers, it is also of interest for decision makers as customers make more informed choices among today's services. In the future it will be of importance to make information usable for managers and improve decision making, tailor smart home services based on big data, develop new business models, increase competition and identify policies to ensure privacy, security and liability.