Hardware-Assisted Trusted Execution Environment technologies have become a crucial component in providing security for cloud-based computing. One of such hardware-assisted countermeasures is Intel Software Guard Extension (SGX). Using additional dedicated hardware and a new set of CPU instructions, SGX is able to provide isolated execution of code within trusted hardware containers called enclaves. By utilizing private encrypted memory and various integrity authentication mechanisms, it can provide confidentiality and integrity guarantees to protected data. In spite of dedicated hardware, these extra layers of security add a significant performance overhead. Decryption of data using secret OTPs, which are generated by modified Counter Mode Encryption AES blocks, results in a significant latency overhead that contributes to the overall SGX performance loss. This thesis introduces a metadata prediction extension to SGX based on local metadata releveling and prediction mechanisms. Correct prediction of metadata allows to speculatively precompute OTPs, which can be immediately used in decryption of incoming ciphertext data. This hides a significant part of decryption latency and results in faster SGX performance without any changes to the original SGX security guarantees. / Master of Science / With the exponential growth of cloud computing, where critical data processing is happening on third-party computer systems, it is important to ensure data confidentiality and integrity against third-party access. Sometimes that may include not only external attackers, but also insiders, like cloud computing providers themselves. While software isolation using Virtual Machines is the most common method of achieving runtime security in cloud computing, numerous shortcomings of software-only countermeasures force companies to demand extra layers of security. Recently adopted general purpose hardware-assisted technology like Intel Software Guard Extension (SGX) add that extra layer of security at the significant performance overhead. One of the major contributors to the SGX performance overhead is data decryption latency. This work proposes a novel algorithm to speculatively predict metadata that is used during decryption. This allows the processor to hide a significant portion of decryption latency, thus improving the overall performance of Intel SGX without compromising security.

A Otimização por Enxame de Partículas (PSO, Particle Swarm Optimization) é uma técnica de otimização que vem sendo utilizada na solução de diversos problemas, em diferentes áreas do conhecimento. Porém, a maioria das implementações é realizada de modo sequencial. O processo de otimização necessita de um grande número de avaliações da função objetivo, principalmente em problemas complexos que envolvam uma grande quantidade de partículas e dimensões. Consequentemente, o algoritmo pode se tornar ineficiente em termos do desempenho obtido, tempo de resposta e até na qualidade do resultado esperado. Para superar tais dificuldades, pode-se utilizar a computação de alto desempenho e paralelizar o algoritmo, de acordo com as características da arquitetura, visando o aumento de desempenho, a minimização do tempo de resposta e melhoria da qualidade do resultado final. Nesta dissertação, o algoritmo PSO é paralelizado utilizando três estratégias que abordarão diferentes granularidades do problema, assim como dividir o trabalho de otimização entre vários subenxames cooperativos. Um dos algoritmos paralelos desenvolvidos, chamado PPSO, é implementado diretamente em hardware, utilizando uma FPGA. Todas as estratégias propostas, PPSO (Parallel PSO), PDPSO (Parallel Dimension PSO) e CPPSO (Cooperative Parallel PSO), são implementadas visando às arquiteturas paralelas baseadas em multiprocessadores, multicomputadores e GPU. Os diferentes testes realizados mostram que, nos problemas com um maior número de partículas e dimensões e utilizando uma estratégia com granularidade mais fina (PDPSO e CPPSO), a GPU obteve os melhores resultados. Enquanto, utilizando uma estratégia com uma granularidade mais grossa (PPSO), a implementação em multicomputador obteve os melhores resultados. / Particle Swarm Optimization (PSO) is an optimization technique that is used to solve many problems in different applications. However, most implementations are sequential. The optimization process requires a large number of evaluations of the objective function, especially in complex problems, involving a large amount of particles and dimensions. As a result, the algorithm may become inefficient in terms of performance, execution time and even the quality of the expected result. To overcome these difficulties,high performance computing and parallel algorithms can be used, taking into account to the characteristics of the architecture. This should increase performance, minimize response time and may even improve the quality of the final result. In this dissertation, the PSO algorithm is parallelized using three different strategies that consider different granularities of the problem, and the division of the optimization work among several cooperative sub-swarms. One of the developed parallel algorithms, namely PPSO, is implemented directly in hardware, using an FPGA. All the proposed strategies, namely PPSO ( Parallel PSO), PDPSO (Parallel Dimension PSO) and CPPSO (Cooperative Parallel PSO), are implemented in a multiprocessor, multicomputer and GPU based parallel architectures. The different performed assessments show that the GPU achieved the best results for problems with high number of particles and dimensions when a strategy with finer granularity is used, namely PDPSO and CPPSO. In contrast with this, when using a strategy with a coarser granularity, namely PPSO, the multi-computer based implementation achieved the best results.

A Otimização por Enxame de Partículas (PSO, Particle Swarm Optimization) é uma técnica de otimização que vem sendo utilizada na solução de diversos problemas, em diferentes áreas do conhecimento. Porém, a maioria das implementações é realizada de modo sequencial. O processo de otimização necessita de um grande número de avaliações da função objetivo, principalmente em problemas complexos que envolvam uma grande quantidade de partículas e dimensões. Consequentemente, o algoritmo pode se tornar ineficiente em termos do desempenho obtido, tempo de resposta e até na qualidade do resultado esperado. Para superar tais dificuldades, pode-se utilizar a computação de alto desempenho e paralelizar o algoritmo, de acordo com as características da arquitetura, visando o aumento de desempenho, a minimização do tempo de resposta e melhoria da qualidade do resultado final. Nesta dissertação, o algoritmo PSO é paralelizado utilizando três estratégias que abordarão diferentes granularidades do problema, assim como dividir o trabalho de otimização entre vários subenxames cooperativos. Um dos algoritmos paralelos desenvolvidos, chamado PPSO, é implementado diretamente em hardware, utilizando uma FPGA. Todas as estratégias propostas, PPSO (Parallel PSO), PDPSO (Parallel Dimension PSO) e CPPSO (Cooperative Parallel PSO), são implementadas visando às arquiteturas paralelas baseadas em multiprocessadores, multicomputadores e GPU. Os diferentes testes realizados mostram que, nos problemas com um maior número de partículas e dimensões e utilizando uma estratégia com granularidade mais fina (PDPSO e CPPSO), a GPU obteve os melhores resultados. Enquanto, utilizando uma estratégia com uma granularidade mais grossa (PPSO), a implementação em multicomputador obteve os melhores resultados. / Particle Swarm Optimization (PSO) is an optimization technique that is used to solve many problems in different applications. However, most implementations are sequential. The optimization process requires a large number of evaluations of the objective function, especially in complex problems, involving a large amount of particles and dimensions. As a result, the algorithm may become inefficient in terms of performance, execution time and even the quality of the expected result. To overcome these difficulties,high performance computing and parallel algorithms can be used, taking into account to the characteristics of the architecture. This should increase performance, minimize response time and may even improve the quality of the final result. In this dissertation, the PSO algorithm is parallelized using three different strategies that consider different granularities of the problem, and the division of the optimization work among several cooperative sub-swarms. One of the developed parallel algorithms, namely PPSO, is implemented directly in hardware, using an FPGA. All the proposed strategies, namely PPSO ( Parallel PSO), PDPSO (Parallel Dimension PSO) and CPPSO (Cooperative Parallel PSO), are implemented in a multiprocessor, multicomputer and GPU based parallel architectures. The different performed assessments show that the GPU achieved the best results for problems with high number of particles and dimensions when a strategy with finer granularity is used, namely PDPSO and CPPSO. In contrast with this, when using a strategy with a coarser granularity, namely PPSO, the multi-computer based implementation achieved the best results.

Η ασφάλεια των υπολογιστικών συστημάτων αποτελεί πλέον μια πολύ ενεργή περιοχή και αναμένεται να γίνει μια νέα παράμετρος σχεδίασης ισάξια μάλιστα με τις κλασσικές παραμέτρους σχεδίασης των συστημάτων, όπως είναι η απόδοση, η κατανάλωση ισχύος και το κόστος. Οι φερέγγυες υπολογιστικές πλατφόρμες έχουν προταθεί σαν μια υποσχόμενη λύση, ώστε να αυξήσουν τα επίπεδα ασφάλειας των συστημάτων και να παρέχουν προστασία από μη εξουσιοδοτημένη άδεια χρήσης των πληροφοριών που είναι αποθηκευμένες σε ένα σύστημα. Ένα φερέγγυο σύστημα θα πρέπει να διαθέτει τους κατάλληλους μηχανισμούς, ώστε να είναι ικανό να αντιστέκεται στο σύνολο, τόσο γνωστών όσο και νέων, επιθέσεων άρνησης υπηρεσίας. Οι επιθέσεις αυτές μπορεί να έχουν ως στόχο να βλάψουν το υλικό ή/και το λογισμικό του συστήματος. Ωστόσο, η μεγαλύτερη βαρύτητα στην περιοχή έχει δοθεί στην αποτροπή επιθέσεων σε επίπεδο λογισμικού. Στην παρούσα διατριβή προτείνονται έξι μεθοδολογίες σχεδίασης ικανές να θωρακίσουν ένα υπολογιστικό σύστημα από επιθέσεις άρνησης υπηρεσίας που έχουν ως στόχο να πλήξουν το υλικό του συστήματος. Η κύρια έμφαση δίνεται στο υποσύστημα της μνήμης (κρυφές μνήμες). Στις κρυφές μνήμες αφιερώνεται ένα μεγάλο μέρος της επιφάνειας του ολοκληρωμένου, είναι αυτές που καλούνται να "αποκρύψουν" τους αργούς χρόνους απόκρισης της κύριας μνήμης και ταυτόχρονα σε αυτές οφείλεται ένα μεγάλο μέρος της συνολικής κατανάλωσης ισχύος. Ως εκ τούτου, παρέχοντας βελτιστοποιήσεις στις κρυφές μνήμες καταφέρνουμε τελικά να μειώσουμε τον χρόνο εκτέλεσης του λογισμικού, να αυξήσουμε το ρυθμό μετάδοσης των ψηφιακών δεδομένων και να θωρακίσουμε το σύστημα από επιθέσεις άρνησης υπηρεσίας σε επίπεδο υλικού. / Data security concerns have recently become very important, and it can be expected that security will join performance, power and cost as a key distinguish factor in computer systems. Trusted platforms have been proposed as a promising approach to enhance the security of the modern computer system and prevent unauthorized accesses and modifications of the sensitive information stored in the system. Unfortunately, previous approaches only provide a level of security against software-based attacks and leave the system wide open to hardware attacks. This dissertation thesis proposes six design methodologies to shield a uniprocessor or a multiprocessor system against a various number of Denial of Service (DoS) attacks at the architectural and the operating system level. Specific focus is given to the memory subsystem (i.e. cache memories). The cache memories account for a large portion of the silicon area, they are greedy power consumers and they seriously determine system performance due to the even growing gap between the processor speed and main memory access latency. As a result, in this thesis we propose methodologies to optimize the functionality and lower the power consumption of the cache memories. The goal in all cases is to increase the performance of the system, the achieved packet throughput and to enhance the protection against a various number of passive and Denial of Service attacks.