Abstract
Interactions between internet users are mediated by their devices and the common support infrastructure in data centres. Keeping track of causality amongst actions that take place in this distributed system is key to provide a seamless interaction where effects follow causes. Tracking causality in large scale interactions is difficult due to the cost of keeping large quantities of metadata; even more challenging when dealing with resource-limited devices. In this paper, we focus on keeping partial knowledge on causality and address deduction from that knowledge. We provide the first proof-theoretic causality modelling for distributed partial knowledge. We prove computability and consistency results. We also prove that the partial knowledge gives rise to a weaker model than classical causality. We provide rules for offline deduction about causality and refute some related folklore. We define two notions of forward and backward bisimilarity between devices, using which we prove two important results. Namely, no matter the order of addition/ removal, two devices deduce similarly about causality so long as: (1) the same causal information is fed to both. (2) they start bisimilar and erase the same causal information. Thanks to our establishment of forward and backward bisimilarity, respectively, proofs of the latter two results work by simple induction on length.

Abstract
Searching for resources over unstructured networks is usually supported by broadcast communication primitives. Ideally, the broadcast process should be cancelled as soon as possible after a successful discovery, to avoid flooding the entire network. However, cancelling an ongoing broadcast is challenging and may increase the number of exchanged messages. In this paper, we compare the cancellation mechanisms used by BERS and BERS* with new proposed cancellation approaches BCIR and BCIR*. The formulation of a simplified analytical model and the simulation results show that: i) it is possible to reduce the number of retransmitted messages, without increasing the latency observed in BERS*; and ii) BCIR is more energy efficient, which can contribute to extend the availability of mobile battery powered devices. Copyright 2013 ACM.

Abstract
Distributed Computing is becoming more and more led by technological and application advances. Many works consider new computing models compared to the classical closed model with a fixed number of participants and strong hypothesis on communication and structuration. Indeed, it is hard to imagine some application or computational activity and process that falls outside Distributed Computing. Internet and the web (e.g. social networks, clouds) are becoming the main application field for distributed computing. In addition to the classical challenges that developers have to face (asynchrony and failures) they have to deal with load balancing, malicious and selfish behaviors, mobility, heterogeneity and the dynamic nature of participating processes. © 2013 Springer-Verlag.

Abstract
Client-side logic and storage are increasingly used in web and mobile applications to improve response time and availability. Current approaches tend to be ad-hoc and poorly integrated with the server-side logic. We present a principled approach to integrate client-and server-side storage. We support both mergeable and strongly consistent transactions that target either client or server replicas and provide access to causally-consistent snapshots efficiently. In the presence of infrastructure faults, a client-assisted failover solution allows client execution to resume immediately and seamlessly access consistent snapshots without waiting. We implement this approach in SwiftCloud, the first transactional system to bring geo-replication all the way to the client machine. Example applications show that our programming model is useful across a range of application areas. Our experimental evaluation shows that SwiftCloud provides better fault tolerance and at the same time can improve both latency and throughput by up to an order of magnitude, compared to classical geo-replication techniques. © 2014 IEEE.

Abstract
The information infrastructure that pervades urban environments represents a major opportunity for collecting information about Human mobility. However, this huge potential has been undermined by the overwhelming privacy risks that are associated with such forms of large scale sensing. In this research, we are concerned with the problem of how to enable a set of autonomous sensing nodes, e.g. a Bluetooth scanner or a Wi-Fi hotspot, to collaborate in the observation of movement patterns of individuals without compromising their privacy. We describe a novel technique that generates Precedence Filters and allows probabilistic estimations of sequences of visits to monitored locations and we demonstrate how this technique can combine plausible deniability by an individual with valuable information about aggregate movement patterns.

Abstract
Programming models for building large-scale distributed applications assist the developer in reasoning about consistency and distribution. However, many of the programming models for weak consistency, which promise the largest scalability gains, have little in the way of evaluation to demonstrate the promised scalability. We present an experience report on the implementation and largescale evaluation of one of these models, Lasp, originally presented at PPDP '15, which provides a declarative, functional programming style for distributed applications. We demonstrate the scalability of Lasp's prototype runtime implementation up to 1024 nodes in the Amazon cloud computing environment. It achieves high scalability by uniquely combining hybrid gossip with a programming model based on convergent computation. We report on the engineering challenges of this implementation and its evaluation, specifically related to operating research prototypes in a production cloud environment. © 2017 Copyright held by the owner/author(s).