Édition collaborative décentralisée dans les navigateurs

Collaborative editors allow users to distribute the writing of a document across space and time. Thanks to their ease of use, real-time collaborative editors working in Web browsers vastly contributed to the adoption of such tools. However, current editors are centralized: a service provider's server hosts an editing session. It raises privacy and scalability issues.Recently, the enabling of browser-to-browser connection establishments opened new opportunities in favor of a decentralized Web. Decentralized real-time collaborative editors working in Web browsers must efficiently handle highly dynamic groups of different size.Contributions of this thesis are threefold:(i) To represent the document, we propose a replicated data structure for sequences using metadata the size of which scales sub-linearly compared to the number of inserted characters.(ii) To efficiently propagate the changes to all editors involved in the collaborative writing, we propose a random peer sampling protocol that supports Web browsers constraints and self-adjusts its functioning to the variations of network membership.(iii) To demonstrate the feasibility of a decentralized real-time collaborative editors running in Web browsers, we propose an editor using (i) and (ii), and we highlight its scalability.Un éditeur collaboratif permet de répartir la tâche de rédaction d'un document à travers le temps et l'espace. Par leur simplicité d'utilisation, les éditeurs collaboratifs temps réel du Web ont contribué à l'adoption massive de ces outils par le grand public. Cependant, les éditeurs actuels sont centralisés : un serveur appartenant à un fournisseur de services gère une session d'édition. En résultent des problèmes de confidentialité, de censure, de propriété, de passage à l'échelle et de tolérance aux pannes.Récemment, la possibilité d'établir des communications d'un navigateur Web à l'autre a ouvert de nouvelles opportunités en faveur d'un Web décentralisé. Un éditeur collaboratif temps réel décentralisé fonctionnant dans les navigateurs Web doit gérer efficacement des groupes de taille variable et hautement dynamiques.Cette thèse comporte trois contributions :(i) Pour représenter le document, nous proposons une structure de données répliquée dont la taille des métadonnées croît de manière sous-linéaire par rapport au nombre de caractères insérés dans le document.(ii) Pour propager efficacement les changements à tous les éditeurs participant à l'édition, nous proposons un protocole d'échantillonnage aléatoire de pairs adapté aux contraintes des navigateurs Web et s'ajustant automatiquement au logarithme de la taille de la session d'édition.(iii) Pour démontrer la faisabilité d'un éditeur collaboratif temps réel décentralisé fonctionnant dans les navigateurs Web, nous proposons un éditeur réunissant (i) et (ii), et dont les performances passent à l'échelle

Breaking the Scalability Barrier of Causal Broadcast for Large and Dynamic Systems

Many distributed protocols and applications rely on causal broadcast to ensure consistency criteria. However, none of causality tracking state-of-the-art approaches scale in large and dynamic systems. This paper presents a new non-blocking causal broadcast protocol suited for dynamic systems. The proposed protocol outperforms state-of-the-art in size of messages, execution time complexity, and local space complexity. Most importantly, messages piggyback control information the size of which is constant. We prove that for both static and dynamic systems. Consequently, large and dynamic systems can finally afford causal broadcast.Comment: 11 page

AS-cast: Lock Down the Traffic of Decentralized Content Indexing at the Edge

International audienceAlthough the holy grail to store and manipulate data in Edge infrastructures is yet to be found, state-of-the-art approaches demonstrated the relevance of replication strategies that bring content closer to consumers: The latter enjoy better response time while the volume of data passing through the network decreases overall. Unfortunately, locating the closest replica of a specific content requires indexing every live replica along with its location. Relying on remote services for such a aim enters in contradiction with the properties of Edge infrastructures as locating replicas may effectively take more time than actually downloading content. At the opposite, maintaining such an index at every node would prove overly costly in terms of memory and traffic. In this paper, we propose a decentralized implementation of content indexing called AS-cast. Using AS-cast, every node only indexes its closest replica; and all connected nodes with a similar index compose a partition. AS-cast is (i) efficient, for it uses partitions to lock down the traffic generated by its operations to relevant nodes, yet it (ii) guarantees that every node eventually acknowledges its closest replica despite concurrent operations. Our prototype, implemented on PeerSim, shows that AS-cast scales well in terms of generated messages and termination time. As such, AS-cast can constitute a novel building block for geo-distributed services in need of efficient resource sharing in the vicinity of regions

AS-cast: Lock Down the Traffic of Decentralized Content Indexing at the Edge

International audienceAlthough the holy grail to store and manipulate data in Edge infrastructures is yet to be found, state-of-the-art approaches demonstrated the relevance of replication strategies that bring content closer to consumers: The latter enjoy better response time while the volume of data passing through the network decreases overall. Unfortunately, locating the closest replica of a specific content requires indexing every live replica along with its location. Relying on remote services for such a aim enters in contradiction with the properties of Edge infrastructures as locating replicas may effectively take more time than actually downloading content. At the opposite, maintaining such an index at every node would prove overly costly in terms of memory and traffic. In this paper, we propose a decentralized implementation of content indexing called AS-cast. Using AS-cast, every node only indexes its closest replica; and all connected nodes with a similar index compose a partition. AS-cast is (i) efficient, for it uses partitions to lock down the traffic generated by its operations to relevant nodes, yet it (ii) guarantees that every node eventually acknowledges its closest replica despite concurrent operations. Our prototype, implemented on PeerSim, shows that AS-cast scales well in terms of generated messages and termination time. As such, AS-cast can constitute a novel building block for geo-distributed services in need of efficient resource sharing in the vicinity of regions

Breaking the Scalability Barrier of Causal Broadcast for Large and Dynamic Systems

International audienceMany distributed protocols and applications rely on causal broadcast to ensure consistency criteria. However, none of causality tracking state-of-the-art approaches scale in large and dynamic systems. This paper presents a new non-blocking causal broadcast protocol suited for dynamic systems. The proposed protocol outperforms state-of-the-art in size of messages, execution time complexity, and local space complexity. Most importantly, messages piggyback control information the size of which is constant. We prove that for both static and dynamic systems. Consequently, large and dynamic systems can finally afford causal broadcast

A scalable sequence encoding for collaborative editing

International audienceDistributed real-time editors made real-time editing easy for millions of users. However, main stream editors rely on Cloud services to mediate sessions raising privacy and scalability issues. Decentralized editors tackle privacy issues but scalability issues remains. We aim to build a decentralized editor that allows real-time editing anytime, anywhere, whatever is the number of participants. In this paper, we propose an approach based on a massively replicated sequence data structure that represents the shared document. We establish an original tradeoff on communication, time and space complexity to maintain this sequence over a network of browsers. We prove a sublinear upper bound on communication complexity while preserving an affordable time and space complexity. In order to validate this tradeoff, we built a full working editor and measured its performance on large scale experiments involving up till 600 participants. As expected, the results show a traffic increasing as O((log I)^2 ln R) where I is the number of insertions in the document, and R the number of participants

CRATE: Writing Stories Together with our Browsers

International audienceReal-time collaborative editors are common tools for distributing work across space, time, and organizations. Unfortunately , mainstream editors such as Google Docs rely on central servers and raise privacy and scalability issues. Crate is a real-time decentralized collaborative editor that runs directly in web browsers thanks to WebRTC. Compared to state-of-the-art, Crate is the first real-time editor that only requires browsers in order to support collaborative editing and to transparently handle from small to large groups of users. Consequently, Crate can also be used in massive online lectures, TV shows or large conferences to allow users to share their notes. Crate's properties rely on two scientific results: (i) a replicated sequence structure with sub-linear upper bound on space complexity; this prevents the editor from running costly distributed garbage collectors , (ii) an adaptive peer sampling protocol; this prevent the editor from oversizing routing tables, hence from letting small networks pay the price of large networks. This paper describes Crate, its properties and its usage

Breaking the Scalability Barrier of Causal Broadcast for Large and Dynamic Systems

International audienceMany distributed protocols and applications rely on causal broadcast to ensure consistency criteria. However, none of causality tracking state-of-the-art approaches scale in large and dynamic systems. This paper presents a new non-blocking causal broadcast protocol suited for dynamic systems. The proposed protocol outperforms state-of-the-art in size of messages, execution time complexity, and local space complexity. Most importantly, messages piggyback control information the size of which is constant. We prove that for both static and dynamic systems. Consequently, large and dynamic systems can finally afford causal broadcast

Causal Broadcast: How to Forget?

International audienceCausal broadcast constitutes a fundamental communication primitive of many distributed protocols and applications. However, state-of-the-art implementations fail to forget obsolete control information about already delivered messages. They do not scale in large and dynamic systems. In this paper, we propose a novel implementation of causal broadcast. We prove that all and only obsolete control information is safely removed, at cost of a few lightweight control messages. The local space complexity of this protocol does not monotonically increase and depends at each moment on the number of messages still in transit and the degree of the communication graph. Moreover, messages only carry a scalar clock. Our implementation constitutes a sustainable communication primitive for causal broadcast in large and dynamic systems

Spray: an Adaptive Random Peer Sampling Protocol

GDD_HCERES2020The introduction of WebRTC has opened a new playground for large-scale distributed applications consisting of large numbers of directly-communicating web browsers. In this context, gossip-based peer-sampling protocols appear as a particularly promising tool thanks to their inherent ability to build overlay networks that can cope with network dynamics. However, the dynamic nature of browser-to-browser communication combined with the connection establishment procedures that characterize WebRTC make current peer sampling solutions inefficient or simply unreliable. In this paper, we address the limitations of current peer-sampling approaches by introducing Spray, a novel peer-sampling protocol designed to avoid the constraints introduced by WebRTC. Unlike most recent peer-sampling approaches, Spray has the ability to adapt its operation to networks that can grow or shrink very rapidly. Moreover, by using only neighbor-to-neighbor interactions, it limits the impact of the threeway connection establishment process that characterizes WebRTC. Our experiments demonstrate the ability of Spray to adapt to dynamic networks and highlight its efficiency improvements with respect to existing protocols