Security, Performance and Energy Trade-offs of Hardware-assisted Memory Protection Mechanisms

The deployment of large-scale distributed systems, e.g., publish-subscribe platforms, that operate over sensitive data using the infrastructure of public cloud providers, is nowadays heavily hindered by the surging lack of trust toward the cloud operators. Although purely software-based solutions exist to protect the confidentiality of data and the processing itself, such as homomorphic encryption schemes, their performance is far from being practical under real-world workloads. The performance trade-offs of two novel hardware-assisted memory protection mechanisms, namely AMD SEV and Intel SGX - currently available on the market to tackle this problem, are described in this practical experience. Specifically, we implement and evaluate a publish/subscribe use-case and evaluate the impact of the memory protection mechanisms and the resulting performance. This paper reports on the experience gained while building this system, in particular when having to cope with the technical limitations imposed by SEV and SGX. Several trade-offs that provide valuable insights in terms of latency, throughput, processing time and energy requirements are exhibited by means of micro- and macro-benchmarks.Comment: European Commission Project: LEGaTO - Low Energy Toolset for Heterogeneous Computing (EC-H2020-780681

TURISMO SOCIAL: Um estudo dos meios de hospedagem de Santa Catarina que atuam nesse segmento

O tema deste trabalho é o turismo social, sendo o objeto principal estudar, por meio dos Objetivos de Desenvolvimento Sustentável – ODS, se as ações dos meios de hospedagem que atuam neste segmento em Santa Catarina vêm de encontro a estas premissas. Como procedimentos metodológicos, a pesquisa adotou a pesquisa exploratória, o levantamento e utilizou do questionário para coletar os dados. Os resultados parciais obtidos até o momento permitiram identificar quais empreendimentos de hospedagem atuam no segmento de turismo social no estado de Santa Catarina, bem como identificar algumas características dos mesmos, por meio da literatura disponível. Infelizmente, apesar da insistência, não obtivemos o retorno dos questionários e, portanto, ainda não respondemos ao problema de pesquisa

LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft

LEGaTO: towards energy-efficient, secure, fault-tolerant toolset for heterogeneous computing

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft

EDGETUNE: Inference-Aware Multi-Parameter Tuning

Deep Neural Networks (DNNs) have demonstrated impressive performance on many machine-learning tasks such as image recognition and language modeling, and are becoming prevalent even on mobile platforms. Despite so, designing neural architectures still remains a manual, time-consuming process that requires profound domain knowledge. Recently, Parameter Tuning Servers have gathered the attention o industry and academia. Those systems allow users from all domains to automatically achieve the desired model accuracy for their applications. However, although the entire process of tuning and training models is performed solely to be deployed for inference, state-of-the-art approaches typically ignore system-oriented and inference-related objectives such as runtime, memory usage, and power consumption. This is a challenging problem: besides adding one more dimension to an already complex problem, the information about edge devices available to the user is rarely known or complete. To accommodate all these objectives together, it is crucial for tuning system to take a holistic approach to parameter tuning and consider all levels of parameters simultaneously into account. We present EdgeTune, a novel inference-aware parameter tuning server. It considers the tuning of parameters in all levels backed by an optimization function capturing multiple objectives. Our approach relies on inference estimated metrics collected from our emulation server running asynchronously from the main tuning process. The latter can then leverage the inference performance while still tuning the model. We propose a novel one-fold tuning algorithm that employs the principle of multi-fidelity and simultaneously explores multiple tuning budgets, which the prior art can only handle as suboptimal case of single type of budget. EdgeTune outputs inference recommendations to the user while improving tuning time and energy by at least 18\% and 53\% when compared to the baseline.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Distributed System