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The world is suffering from a pandemic called COVID-19, caused by the SARS-CoV-2 virus. National governments have problems evaluating the reach of the epidemic, due to having limited resources and tests at their disposal. This problem is especially acute in low and middle-income countries (LMICs). Hence, any simple, cheap and flexible means of evaluating the incidence and evolution of the epidemic in a given country with a reasonable level of accuracy is useful. In this paper, we propose a technique based on (anonymous) surveys in which participants report on the health status of their contacts. This indirect reporting technique, known in the literature as network scale-up method, preserves the privacy of the participants and their contacts, and collects information from a larger fraction of the population (as compared to individual surveys). This technique has been deployed in the CoronaSurveys project, which has been collecting reports for the COVID-19 pandemic for more than two months. Results obtained by CoronaSurveys show the power and flexibility of the approach, suggesting that it could be an inexpensive and powerful tool for LMICs., Comment: Presented at The KDD Workshop on Humanitarian Mapping, San Diego, California USA, August 24, 2020

This paper addresses the consensus problem in homonymous distributed systems where processes are prone to crash failures and have no initial knowledge of the system membership ("homonymous" means that several processes may have the same identifier). New classes of failure detectors suited to these systems are first defined. Among them, the classes H\Omega\ and H\Sigma\ are introduced that are the homonymous counterparts of the classes \Omega\ and \Sigma, respectively. (Recall that the pair <\Omega,\Sigma> defines the weakest failure detector to solve consensus.) Then, the paper shows how H\Omega\ and H\Sigma\ can be implemented in homonymous systems without membership knowledge (under different synchrony requirements). Finally, two algorithms are presented that use these failure detectors to solve consensus in homonymous asynchronous systems where there is no initial knowledge of the membership. One algorithm solves consensus with , while the other uses only H\Omega, but needs a majority of correct processes. Observe that the systems with unique identifiers and anonymous systems are extreme cases of homonymous systems from which follows that all these results also apply to these systems. Interestingly, the new failure detector class H\Omega\ can be implemented with partial synchrony, while the analogous class A\Omega\ defined for anonymous systems can not be implemented (even in synchronous systems). Hence, the paper provides us with the first proof showing that consensus can be solved in anonymous systems with only partial synchrony (and a majority of correct processes).

Peer to peer (P2P) systems are moving from application specific architectures to a generic service oriented design philosophy. This raises interesting problems in connection with providing useful P2P middleware services capable of dealing with resource assignment and management in a large-scale, heterogeneous and unreliable environment. The slicing service, has been proposed to allow for an automatic partitioning of P2P networks into groups (slices) that represent a controllable amount of some resource and that are also relatively homogeneous with respect to that resource. In this paper we propose two gossip-based algorithms to solve the distributed slicing problem. The first algorithm speeds up an existing algorithm sorting a set of uniform random numbers. The second algorithm statistically approximates the rank of nodes in the ordering. The scalability, efficiency and resilience to dynamics of both algorithms rely on their gossip-based models. These algorithms are proved viable theoretically and experimentally.

Peer to peer (P2P) systems are moving from application specific architectures to a generic service oriented design philosophy. This raises interesting problems in connection with providing useful P2P middleware services that are capable of dealing with resource assignment and management in a large-scale, heterogeneous and unreliable environment. One such service, the slicing service, has been proposed to allow for an automatic partitioning of P2P networks into groups (slices) that represent a controllable amount of some resource and that are also relatively homogeneous with respect to that resource, in the face of churn and other failures. In this report we propose two algorithms to solve the distributed slicing problem. The first algorithm improves upon an existing algorithm that is based on gossip-based sorting of a set of uniform random numbers. We speed up convergence via a heuristic for gossip peer selection. The second algorithm is based on a different approach: statistical approximation of the rank of nodes in the ordering. The scalability, efficiency and resilience to dynamics of both algorithms relies on their gossip-based models. We present theoretical and experimental results to prove the viability of these algorithms.