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.

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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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