Abstract
Healthcare environments are generating a deluge of sensitive data. Nonetheless, dealing with large amounts of data is an expensive task, and current solutions resort to the cloud environment. Additionally, the intersection of the cloud environment and healthcare data opens new challenges regarding data privacy. With this in mind, we propose MEDCLOUDCARE (MCC), a healthcare application offering medical image viewing and processing tools while integrating cloud computing and AI. Moreover, MCC provides security and privacy features, scalability and high availability. The system is intended for two user groups: health professionals and researchers. The former can remotely view, process and share medical imaging information in the DICOM format. Also, it can use pre-trained Machine Learning (ML) models to aid the analysis of medical images. The latter can remotely add, share, and deploy ML models to perform inference on DICOM images. MCC incorporates a DICOM web viewer enabling users to view and process DICOM studies, which they can also upload and store. Regarding the security and privacy of the data, all sensitive information is encrypted at rest and in transit. Furthermore, MCC is intended for cloud environments. Thus, the system is deployed using Kubernetes, increasing the efficiency, availability and scalability of the ML inference process.

Abstract
Medical imaging, mainly Magnetic Resonance Imaging (MRI), plays a predominant role in healthcare diagnosis. Nevertheless, the diagnostic process is prone to errors and is conditioned by available medical data, which might be insufficient. A novel solution is resorting to image generation algorithms to address these challenges. Thus, this paper presents a Deep Learning model based on a Deep Convolutional Generative Adversarial Network (DCGAN) architecture. Our model generates 2D MRI images of size 256x256, containing an axial view of the brain with a tumor. The model was implemented using ChainerMN, a scalable and flexible framework that enables faster and parallel training of Deep Learning networks. The images obtained provide an overall representation of the brain structure and the tumoral area and show considerable brain-tumor separation. For this purpose, and owing to their previous state-of-the-art results in general image-generation tasks, we conclude that GAN-based models are a promising approach for medical imaging.

Abstract
Deduplication is widely accepted as an effective technique for eliminating duplicated data in backup and archival systems. Nowadays, deduplication is also becoming appealing in cloud computing, where large-scale virtualized storage infrastructures hold huge data volumes with a significant share of duplicated content. There have thus been several proposals for embedding deduplication in storage appliances and file systems, providing different performance trade-offs while targeting both user and application data, as well as virtual machine images. It is however hard to determine to what extent is deduplication useful in a particular setting and what technique will provide the best results. In fact, existing disk I/O micro-benchmarks are not designed for evaluating deduplication systems, following simplistic approaches for generating data written that lead to unrealistic amounts of duplicates. We address this with DEDISbench, a novel micro-benchmark for evaluating disk I/O performance of block based deduplication systems. As the main contribution, we introduce the generation of a realistic duplicate distribution based on real datasets. Moreover, DEDISbench also allows simulating access hotspots and different load intensities for I/O operations. The usefulness of DEDISbench is shown by comparing it with Bonnie++ and IOzone open-source disk I/O micro-benchmarks on assessing two open-source deduplication systems, Opendedup and Lessfs, using Ext4 as a baseline. As a secondary contribution, our results lead to novel insight on the performance of these file systems. © 2012 Springer-Verlag.

Abstract

Abstract
Total order multicast greatly simplifies the implementation of fault-tolerant services using the replicated state machine approach. The additional latency of total ordering can be masked by taking advantage of spontaneous ordering observed in LANs: A tentative delivery allows the application to proceed in parallel with the ordering protocol. The effectiveness of the technique rests on the optimistic assumption that a large share of correctly ordered tentative deliveries offsets the cost of undoing the effect of mistakes. This paper proposes a simple technique which enables the usage of optimistic delivery also in WANs with much larger transmission delays where the optimistic assumption does not normally hold. Our proposal exploits local clocks and the stability of network delays to reduce the mistakes in the ordering of tentative deliveries. An experimental evaluation of a modified sequencer-based protocol is presented, illustrating the usefulness of the approach in fault-tolerant database management.

Abstract
Database replication based on group communication systems has recently been proposed as an efficient and resilient solution for large-scale data management. However, its evaluation has been conducted either on simplistic simulation models, which fail to assess concrete implementations, or on complete system implementations which are costly to test with realistic large-scale scenarios. This paper presents a tool that combines implementations of replication and communication protocols under study with simulated network, database engine, and traffic generator models. Replication components can therefore be subjected to realistic large scale loads in a variety of scenarios, including fault-injection, while at the same time providing global observation and control. The paper shows first how the model is configured and validated to closely reproduce the behavior of a real system, and then how it is applied, allowing us to derive interesting conclusions both on replication and communication protocols and on their implementations.