Peter Van Roy

We provide a lightweight decentralized publish-subscribe framework for supporting large-scale actor communication on edge networks. Our framework, called Loquat, does not depend on any reliable central nodes (e.g., data centers), provides reliability in the face of massive node failures and network partitioning, and provides scalability as the number of nodes increases. We consider that high reliability, i.e., that send operations reach close to 100% of live destination nodes, is a critical property for communication frameworks on edge networks. But reliability is difficult to achieve in a scalable way on edge networks because of the network's dynamicity, i.e., frequent node failures and partitioning. For example, both Internet of Things networks and mobile phone networks consist of devices that are often offline. To achieve reliability, our framework is based on two hybrid gossip algorithms, namely HyParView and Plumtree. Hybrid gossip algorithms combine gossip with other distributed algorithms to achieve both efficiency and high resilience. Our current implementation is written in Erlang and has demonstrated scalability up to 1024 nodes in Amazon's cloud computing environment.

Fault-tolerance and lookup consistency are considered crucial properties for building applications on top of structured overlay networks. Many of these networks use the ring topology for the organization or their peers. The network must handle multiple joins, leaves and failures of peers while keeping the connection between every pair of successor-predecessor correct. This property makes the maintenance of the ring very costly and temporarily impossible to achieve, requiring periodic stabilization for fixing the ring. We introduce the relaxed-ring topology that does not rely on a perfect successor-predecessor relationship and it does not need a any periodic maintenance. Leaves and failures are considered as the same type of event providing a fault-tolerant and self-organizing maintenance of the ring. Relaxed-ring's limitations with respect to failure handling are formally identified, providing strong guarantees to develop applications on top of the architecture. Besides permanent failures, the paper analyses temporary failures and false suspicions caused by broken links, which are often ignored.

Oz is a multiparadigm language that supports logic programming as one of its major paradigms. A multiparadigm language is designed to support different programming paradigms (logic, functional, constraint, object-oriented, sequential, concurrent, etc.) with equal ease. This paper has two goals: to give a tutorial of logic programming in Oz; and to show how logic programming fits naturally into the wider context of multiparadigm programming. Our experience shows that there are two classes of problems, which we call algorithmic and search problems, for which logic programming can help formulate practical solutions. Algorithmic problems have known efficient algorithms. Search problems do not have known efficient algorithms but can be solved with search. The Oz support for logic programming targets these two problem classes specifically, using the concepts needed for each. This is in contrast to the Prolog approach, which targets both classes with one set of concepts, which results in less than optimal support for each class. We give examples that can be run interactively on the Mozart system, which implements Oz. To explain the essential difference between algorithmic and search programs, we define the Oz execution model. This model subsumes both concurrent logic programming (committed-choice-style) and search-based logic programming (Prolog-style). Furthermore, as consequences of its multiparadigm nature, the model supports new abilities such as first-class top levels, deep guards, active objects, and sophisticated control of the search process. Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We give a brief history of Oz that traces the development of its main ideas and we summarize the lessons learned from this work. Finally, we give many entry points into the Oz literature.