Thesis describes the concept of cloud computing and its development into today's form. First section defines cloud computing through several definitions. It also describes important capabilities of the technology. Second section is presenting case studies of cloud deployment in Czech companies. It is evaluating advantages and disadvantages of the particular solution in use. Third section is presenting results of quantitative research that was carried out on this topic with IT professionals working in Czech private sector.

Orientador: Maurício Ferreira Magalhães / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-27T08:36:36Z (GMT). No. of bitstreams: 1 Caciato_LucianoEduardo_M.pdf: 3777217 bytes, checksum: aaac15680ca349eb66c9bd9802c41a15 (MD5) Previous issue date: 2015 / Resumo: A disponibilidade dos serviços de tecnologia da informação é fundamental para empresas, bancos e instituições públicas. As informações são crucias para a tomada de decisão, elevando a competitividade e aumentando os lucros, além disso, empresas ou instituições com boa reputação na prestação dos serviços são sólidas e admiradas no mercado. Os sistemas de informações devem prover a maior disponibilidade possível de seus serviços e a alta disponibilidade e a virtualização são excelentes estratégias para alcançar este objetivo. A literatura mostra que existem várias implementações para garantir a disponibilidade dos serviços, porém todas são baseadas na alta disponibilidade no nível da virtualização, preocupando-se em manter, migrar ou iniciar uma ou várias máquinas virtuais em um data center. Nesta dissertação a proposta consiste na implementação da virtualização e da alta disponibilidade indo além dos hypervisors, ou seja, nos sistemas operacionais hospedados nas máquinas virtuais. O objetivo é garantir a disponibilidade dos serviços não controlados pela virtualização garantindo assim um menor tempo possível de indisponibilidade dos serviços oferecidos pelos sistemas de informação / Abstract: Availability of services of information technology is essential for companies, banks and public institutions. The information is vital for decision making, increasing the competitiveness and boosting profits, in addition, a company or institution with good reputation in service delivery are solid and admired by the market. Information systems must provide the highest possible availability of their services and high availability and virtualization are excellent strategies to achieve this goal. There are several implementations to ensure availability of services the literature, but all are based on the level of high availability virtualization, concerned to maintain, migrate or initiate one or more virtual machines in a data center. In this dissertation, the proposal consists of the implementation of virtualization and high availability that goes beyond hypervisors, ie the hosted operating systems in virtual machines with the objective to ensure the availability of virtualization services not controlled by ensuring lowest possible downtime of information systems / Mestrado / Engenharia de Computação / Mestre em Engenharia Elétrica

Institutions and enterprises are moving towards more service availability, managed risk and at the same time, aim at reducing cost. Cloud Computing is a growing technology, thriving in the fields of information communication and data storage. With the proliferation of online activity, more and more information is saved as data every day. This means that more data is being stored in the cloud than ever before. Data that is stored online often holds private information – such as addresses, payment details and medical documentation. These become the target of cyber criminals. There is therefore growing need to protect these data from threats and issues such as data breach and leakage, data loss, account takeover or hijackings, among others. Cryptography refers to securing the information and communication techniques based on mathematical concepts and algorithms which transform messages in ways that are hard to decipher. Cryptography is one of the techniques we could protect data stored in the cloud as it enables security properties of data confidentiality and integrity. This research investigates the security issues that affect storage of data in the cloud. This thesis also discusses the previous research work and the currently available technology and techniques that are used for securing data in the cloud. This thesis then presents a novel scheme for security of data stored in Cloud Computing by using Elliptic Curve Integrated Encryption Scheme (ECIES) that provides for confidentiality and integrity. This scheme also uses Identity Based Cryptography (IBC) for more efficient key management. The proposed scheme combines the security of Identity- Based Cryptography (IBC), Trusted cloud (TC), and Elliptic Curve Cryptography (ECC) to reduce system complexity and provide more security for cloud computing applications. The research shows that it is possible to securely store confidential user data on a Public Cloud such as Amazon S3 or Windows Azure Storage without the need to trust the Cloud Provider and with minimal overhead in processing time. The results of implementing the proposed scheme shows faster and more efficient communication operation when it comes to key generation as well as encryption and decryption. The difference in the time taken for these operations is as a result of the use of ECC algorithm which has a small key size and hence highly efficient compared with other types of asymmetric cryptography. The results obtained show the scheme is more efficient, when compared with other classification techniques in the literature.

Amazon Spot Instances provide inexpensive service for high-performance computing. With spot instances, it is possible to get at most 90% off as discount in costs by bidding spare Amazon Elastic Computer Cloud (Amazon EC2) instances. In exchange for low cost, spot instances bring the reduced reliability onto the computing environment, because this kind of instance could be revoked abruptly by the providers due to supply and demand, and higher-priority customers are first served. To achieve high performance on instances with compromised reliability, Spark is applied to run jobs. In this thesis, a wide set of spark experiments are conducted to study its performance on spot instances. Without stateful replicating, Spark suffers from cascad- ing rollback and is forced to regenerate these states for ad hoc practices repeatedly. Such downside leads to discussion on trade-off between compatible slow checkpointing and regenerating on rollback and inspires us to apply multiple fault tolerance schemes. And Spark is proven to finish a job only with proper revocation rate. To validate and evaluate our work, prototype and simulator are designed and implemented. And based on real history price records, we studied how various checkpoint write frequencies and bid level affect performance. In case study, experiments show that our presented techniques can lead to ~20% shorter completion time and ~25% lower costs than those cases without such techniques. And compared with running jobs on full-price instance, the absolute saving in costs can be ~70%.

Provable Data Possession (PDP) schemes provide users with the ability to efficiently audit and verify the integrity of data stored with potentially unreliable third-parties, such as cloud storage service providers. While dozens of PDP schemes have been developed, no PDP schemes have been practically implemented with an existing cloud service. This work attempts to provide a starting point for the integration of PDP schemes with cloud storage service providers by providing a cost analysis of PDP schemes. This cost analysis is performed by implementing and analyzing five PDP schemes representative of the dozens of various PDP approaches. This paper provides analysis of the overhead and performance of each of these schemes to generate a comparable cost for each scheme using real-world cloud pricing models. Results show that the total cost of each scheme is comparable for smaller file sizes, but for larger files this cost can vary across schemes by an order of magnitude. Ultimately, the difference in cost between the simple MAC-based PDP scheme and the most "efficient" PDP scheme is negligible. While the MAC-PDP scheme may not be the most efficient, no other scheme improving upon it's complexity can be implemented without the use of additional services or APIs leading to the conclusion that the simplest, storage only PDP scheme is the most practical to implement. Furthermore, the findings in this paper suggest that, in general, PDP schemes optimize on an inaccurate cost model and that future schemes should consider the existing economic realities of cloud services.

To achieve the best performance out of an IaaS cloud, the resource management layer must be able to distribute the workloads it is tasked with optimally on the underlying infrastructure. A utilization-based scheduler can take advantage of the fact that allocated resources and actual resource usage often differ to make better-informed decisions of where to place future requests. This thesis presents the design, implementation and evaluation of an initial placement controller that uses host utilization data as one of its inputs to help place virtual machines according to one of a number of supported management objectives. The implementation, which builds on top of the OpenStack cloud platform, deals with two different objectives, namely, balanced load and energy efficiency. The thesis also discusses additional objectives and how they can be supported. A testbed and demonstration platform consisting of the aforementioned controller, a synthetic load generator and a monitoring system are built and used during evaluation of the system. Results indicate that the scheduler performs equally well for both objectives using synthetically generated request patterns of both interactive and batch type workloads. A discussion of current limitations of the scheduler and ways to overcome those conclude the thesis. Among the things discussed are how the rate at which host utilization data is collected limits the performance of the scheduler and under which circumstances dynamic placement of virtual machines must be used to complement utilization-based scheduling to avoid the risk of overloading the cloud. / För att erhålla maximal prestanda ur ett IaaS moln måste dess resurshanteringssystem kunna schemalägga resursutnyttjandet av den underliggande infrastrukturen på ett optimalt sätt. En nyttjandebaserad schemaläggare kan dra nytta av det faktum att allokerade resurser och faktiskt använda resurser ofta skiljer sig åt, för att på så sätt kunna fatta mer välgrundade beslut om var framtida förfrågningar skall placeras. Detta examensarbete presenterar såväl utformning, implementation samt utvärdering av en kontrollenhet för initial placering som till sin hjälp använder information om värdutnyttjande som indata för placering av virtuella maskiner enligt ett av ett antal stödda förvaltningssyften. Implementationen, som baseras på molnplattformen OpenStack, behandlar två sådana syften, balanserad last och energieffektivitet. I tillägg diskuteras krav för att stödja ytterligare syften. En testmiljö och demonstrationsplattform, bestående av ovan nämnda kontrollenhet, en syntetisk lastgenererare samt en övervakningsplattform byggs upp och används vid utvärdering av systemet. Resultat av utvärderingen påvisar att schemaläggaren presterar likvärdigt för båda syftena vid användande av last bestående av såväl interaktiva som applikationer av batch-typ. En diskussion om nuvarande begränsningar hos schemaläggaren och hur dessa kan överbryggas sammanfattar arbetet. Bland det som tas upp diskuteras bl.a. hur hastigheten vid vilken värdutnyttjande data samlas in påverkar prestandan hos schemaläggaren samt under vilka förhållanden dynamisk placering av virtuella maskiner bör användas som komplement till nyttjandebaserad schemaläggning för att undvika risk för överbelastning i molnet.

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Stress is now recognized as one of the major causes of physical and psychological illness. It is known as a reaction to surrounding environmental threats and the best way to manage it is to understand its triggers. Although people continuously react to their surrounding environments, they sometimes are not aware that certain elements in their environment are considered to be stressors. Based on this fact, researchers have recently proposed context-aware stress management systems. Most of the proposed systems use context data to provide real time stress monitoring and visualization, along with intervention techniques. However, these interventions are limited to the second and tertiary stages and very little attention has been given to the primary stage. In this thesis, we introduce a system called CASP. The system’s objective is to provide stress status predictions based on a user’s current contextual data. Therefore, a detection method is developed using heart rate variability (HRV) as a stress indicator to deliver personalized context-aware stress reports. Based on the predicted status, the system provides users with stress interventions at an early stage in order to help avoid and/or eliminate the occurrence of stress. Our evaluation results show that the CASP system is able to predict the stress status of a user with an averaged accuracy of 78.23% through our limited activity, when compare to a stress status measured using physiological signals. Moreover, it provides prediction models that adapt to the changing nature of both the user’s stress status and the surrounding environment.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In recent years, the increased use of digital tools within the Peruvian tourism industry has created a corresponding increase in revenues. However, both factors have caused increased competition in the sector that in turn puts pressure on small and medium enterprises’ (SME) revenues and profitability. This study aims to apply neural network based sentiment analysis on social networks to generate a new information search channel that provides a global understanding of user trends and preferences in the tourism sector. A working data-analysis framework will be developed and integrated with tools from the cloud to allow a visual assessment of high probability outcomes based on historical data, to help SMEs estimate the number of tourists arriving and places they want to visit, so that they can generate desirable travel packages in advance, reduce logistics costs, increase sales, and ultimately improve both quality and precision of customer service.

Solving large-scale linear systems of equations (LSEs) is one of the most common and fundamental problems in big data. But such problems are often too expensive to solve for resource-limited users. Cloud computing has been proposed as an efficient and costeffective way of solving such tasks. Nevertheless, one critical concern in cloud computing is data privacy. Many previous works on secure outsourcing of LSEs have high computational complexity and share a common serious problem, i.e., a huge number of external memory I/O operations, which may render those outsourcing schemes impractical. We develop a practical secure outsourcing algorithm for solving large-scale LSEs, which has both low computational complexity and low memory I/O complexity and can protect clients privacy well. We implement our algorithm on a real-world cloud server and a laptop. We find that the proposed algorithm offers significant time savings for the client (up to 65%) compared to previous algorithms.