Abstract
Online applications now routinely replicate their data at multiple sites around the world. In this paper we present ATLAS, the first state-machine replication protocol tailored for such planet-scale systems. ATLAS does not rely on a distinguished leader, so clients enjoy the same quality of service independently of their geographical locations. Furthermore, clientperceived latency improves as we add sites closer to clients. To achieve this, ATLAS minimizes the size of its quorums using an observation that concurrent data center failures are rare. It also processes a high percentage of accesses in a single round trip, even when these conflict. We experimentally demonstrate that ATLAS consistently outperforms state-of-the-art protocols in planet-scale scenarios. In particular, ATLAS is up to two times faster than Flexible Paxos with identical failure assumptions, and more than doubles the performance of Egalitarian Paxos in the YCSB benchmark.

Abstract
AbstractThe world is suffering from a pandemic called COVID-19, caused by the SARS-CoV-2 virus. The different national governments have problems evaluating the reach of the epidemic, having limited resources and tests at their disposal. Hence, any means to evaluate the number of persons with symptoms compatible with COVID-19 with reasonable level of accuracy is useful. In this paper we present the initial results of the @CoronaSurveys project. The objective of this project is the collection and publication of data concerning the number of people that show symptoms compatible with COVID-19 in different countries using open anonymous surveys. While this data may be biased, we conjecture that it is still useful to estimate the number of infected persons with the COVID-19 virus at a given point in time in these countries, and the evolution of this number over time. We show here the initial results of the @CoronaSurveys project in Spain and Portugal.

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Although hardware is generally seen as the main culprit for a computer's energy usage, software too has a tremendous impact on the energy spent. Unfortunately, there is still not enough support for software developers so they can make their code more energy-aware. This paper proposes a technique to detect energy inefficient fragments in the source code of a software system. Test cases are executed to obtain energy consumption measurements, and a statistical method, based on spectrum-based fault localization, is introduced to relate energy consumption to the source code. The result of our technique is an energy ranking of source code fragments pointing developers to possible energy leaks in their code. This technique was implemented in the SPELL toolkit. Finally, in order to evaluate our technique, we conducted an empirical study where we asked participants to optimize the energy efficiency of a software system using our tool, while also having two other groups using no tool assistance and a profiler, respectively. We showed statistical evidence that developers using our technique were able to improve the energy efficiency by 43% on average, and even out performing a profiler for energy optimization.