Abstract
Distributed transaction processing has benefited greatly from optimistic concurrency control protocols thus avoiding costly fine-grained synchronization. However, the performance of these protocols degrades significantly when the workload increases, namely, by leading to a substantial amount of aborted transactions due to concurrency conflicts. Our approach stems from the observation that when the abort rate increases with the load as already executed transactions queue for longer periods of time waiting for their turn to be certified and committed. We thus propose an adaptive algorithm for judiciously scheduling transactions to minimize the time during which these are vulnerable to being aborted by concurrent transactions, thereby reducing the overall abort rate. We do so by throttling transaction execution using an adaptive mechanism based on the locally known state of globally executing transactions, that includes out-of-order execution. Our evaluation using traces from the industry standard TPC-E workload shows that the amount of aborted transactions can be kept bounded as system load increases, while at the same time fully utilizing system resources and thus scaling transaction processing throughput. © 2013 IFIP International Federation for Information Processing.

Abstract
NoSQL databases were initially devised to support a few concrete extreme scale applications. Since the specificity and scale of the target systems justified the investment of manually crafting application code their limited query and indexing capabilities were not a major impediment. However, with a considerable number of mature alternatives now available there is an increasing willingness to use NoSQL databases in a wider and more diverse spectrum of applications and, to most of them, hand-crafted query code is not an enticing trade-off. In this paper we address this shortcoming of current NoSQL databases with an effective approach for executing SQL queries while preserving their scalability and schema flexibility. We show how a full-fledged SQL engine can be integrated atop of HBase leading to an ANSI SQL compliant database. Under a standard TPC-C workload our prototype scales linearly with the number of nodes in the system and outperforms a NoSQL TPC-C implementation optimized for HBase. © 2013 IFIP International Federation for Information Processing.

Abstract
With the increasing number of connected devices, it becomes essential to find novel data management solutions that can leverage their computational and storage capabilities. However, developing very large scale data management systems requires tackling a number of interesting distributed systems challenges, namely continuous failures and high levels of node churn. In this context, epidemic-based protocols proved suitable and effective and have been successfully used to build DataFlasks, an epidemic data store for massive scale systems. Ensuring resiliency in this data store comes with a significant cost in storage resources and network bandwidth consumption. Deduplication has proven to be an efficient technique to reduce both costs but, applying it to a large-scale distributed storage system is not a trivial task. In fact, achieving significant space-savings without compromising the resiliency and decentralized design of these storage systems is a relevant research challenge. In this paper, we extend DataFlasks with deduplication to design DDFlasks. This system is evaluated in a real world scenario using Wikipedia snapshots, and the results are twofold. We show that deduplication is able to decrease storage consumption up to 63% and decrease network bandwidth consumption by up to 20%, while maintaining a fullydecentralized and resilient design. © IFIP International Federation for Information Processing 2017.

Abstract
Window functions are extremely useful and have become increasingly popular, allowing ranking, cumulative sums and other analytic aggregations to be computed over a highly flexible and configurable sliding window. This powerful expressiveness comes naturally at the expense of heavy computational requirements which, so far, have been addressed through optimizations around centralized approaches by works both from the industry and academia. Distribution and parallelization has the potential to improve performance, but introduces several challenges associated with data distribution that may harm data locality. In this paper, we show how data similarity can be employed across partitions during the distributed execution of these operators to improve data co-locality between instances of a Distributed Query Engine and the associated data storage nodes. Our contribution can attain network gains in the average of 3 times and it is expected to scale as the number of instances increase. In the scenario with 8 nodes, we were to able attain bandwidth and time savings of 7.3 times and 2.61 times respectively. © IFIP International Federation for Information Processing 2017.

Abstract
Buffer caching mechanisms are paramount to improve the performance of today's massive scale NoSQL databases. In this work, we show that in fact there is a direct and univocal relationship between the resource usage and the cache hit ratio in NoSQL databases. In addition, this relationship can be leveraged to build a mechanism that is able to estimate resource usage of the nodes composing the NoSQL cluster.

Abstract
The ordering of events is a fundamental problem of distributed computing and has been extensively studied over several decades. From all the available orderings, total ordering is of particular interest as it provides a powerful abstraction for building reliable distributed applications. Unfortunately, deterministic total order algorithms scale poorly and are therefore unfit for modern large-scale applications. The main contribution of this paper is EPTO, a total order algorithm with probabilistic agreement that scales both in the number of processes and events. EPTO provides deterministic safety and probabilistic liveness: integrity, total order and validity are always preserved, while agreement is achieved with arbitrarily high probability. We show that EPTO is well-suited for large-scale dynamic distributed systems: it does not require a global clock nor synchronized processes, and it is highly robust even when the network suffers from large delays and significant churn and message loss.