Abstract
Although epidemic or gossip-based multicast is a robust and scalable approach to reliable data dissemination, its inherent redundancy results in high resource consumption on both links and nodes. Tins problem is aggravated in settings that have costlier or resource constrained links as happens in Cloud Computing infrastructures composed by several interconnected data centers across the globe. The goal of this work is therefore to improve the efficiency of gossip-based reliable multicast by reducing the load imposed on those constrained links. hi detail, the proposed CLON protocol combines an overlay that gives preference to local links and a dissemination strategy that takes into account locality. Extensive experimental evaluation using a very large number of simulated nodes shows that this results in a reduction of traffic in constrained links by an order of magnitude, while at the same time preserving the resilience properties that make gossip-based protocols so attractive.

Abstract
Several techniques for database replication using group communication have recently been proposed, namely, the Database State Machine, PostgresR, and the NODO protocol. Although all rely on a totally ordered multicast for consistency, they differ substantially on how multicast is used. This results in different performance trade-offs which are hard to compare as each protocol is presented using a different load scenario and evaluation method. In this paper we evaluate the suitability of such protocols for replication of On-Line Transaction Processing (OLTP) applications in clusters of servers and over wide area networks. This is achieved by implementing them using a common infra-structure and by using a standard workload. The results allows us to select the best protocol regarding performance and scalability in a demanding but realistic usage scenario.

Abstract
Gossip-based protocols have been gaining an increasing interest from the research community due to the high resilience to node churn and high scalability, thus making them suitable to modern large-scale dynamic systems. Unfortunately, these properties come at the cost of redundant message transmissions to ensure bimodal delivery to all interested peers. In systems with high message throughput, those additional messages could pose a significant burden on the excess of required bandwidth. Furthermore, the overlays upon which message dissemination takes place are oblivious to the underlying network, or rely on posterior optimizations that bias the overlay to mimic the network topology. This contributes even more to the required bandwidth as 'undesirable' paths are chosen with equal probability among desired ones. In a Cloud Computing scenario, nodes tend to be aggregated in sub-nets inside a data-center or in multiple data-centers, which are connected by costlier, long-distance links. The goal of this work is, therefore, to build an overlay that approximates the structure of the physical network, while ensuring the connectivity properties desirable to ensure reliable dissemination. By having each node judiciously choose which nodes are in its dissemination list at construction time, i.e. by giving preference to local nodes, we are able to significantly reduce the number of messages traversing the long-distance links. In a later stage, this overlay shall be presented as a service upon which data dissemination and management protocols could be run. Copyright 2009 ACM.

Abstract
This paper investigates the use of partial replication in the Database State Machine approach introduced earlier for fully replicated databases. It builds on the order and atomicity properties of group communication primitives to achieve strong consistency and proposes two new abstractions: Resilient Atomic Commit and Fast Atomic Broadcast. Even with atomic broadcast, partial replication requires a termination protocol such as atomic commit to ensure transaction atomicity. With Resilient Atomic Commit our termination protocol allows the commit of a transaction despite the failure of some of the participants. Preliminary, performance studies suggest that the additional cost of supporting partial replication can be mitigated through the use of Fast Atomic Broadcast.

Abstract
AbstractWith the rapid growth of Deep Learning models and neural networks, the medical data available for training – which is already significantly less than other types of data – is becoming scarce. For that purpose, Generative Adversarial Networks (GANs) have received increased attention due to their ability to synthesize new realistic images. Our preliminary work shows promising results for brain MRI images; however, there is a need to distribute the workload, which can be supported by High-Performance Computing (HPC) environments. In this paper, we generate 256×256 MRI images of the brain in a distributed setting. We obtained an FIDRadImageNetof 10.67 for the DCGAN and 23.54 for the WGAN-GP, which are consistent with results reported in several works published in this scope. This allows us to conclude that distributing the GAN generation process is a viable option to overcome the computational constraints imposed by these models and, therefore, facilitate the generation of new data for training purposes.