International audiencePublish/Subscribe systems provide a useful platform for delivering data (events) from publishers to subscribers in an anonymous fashion in distributed networks. In this pa-per, we promote a novel design principle for self-* dynamic and reliable content-based publish/subscribe systems and perform a comparative analysis of its probabilistic and de-terministic implementations. More specifically, we present a generic content-based publish/subscribe system, called DPS (Dynamic Publish/Subscribe). DPS combines classi-cal content-based filtering with self-* (self-organizing, self-configuring, and self-healing) subscription-driven cluster-ing of subscribers. DPS gracefully adapts to failures and changes in the system while achieving scalable events deliv-ery. DPS includes a variety of fault-tolerant deterministic and probabilistic content-based publication/subscription schemes. These schemes are targeted toward scalability, and aim at reducing and distributing the number of mes-sages exchanged. Reliability and scalability of our system are shown through analytical and experimental evaluation

Abstract

Abstract-The population protocol model provides theoretical foundations for analyzing the properties emerging from simple and pairwise interactions among a very large number n of anonymous agents. The problem tackled in this paper is the following one: is there an efficient population protocol that exactly counts the difference κ between the number of agents that initially and independently set their state to A and the one that initially set it to B, assuming that each agent only uses a finite set of states ? We propose a solution which guarantees with any high probability that after O(log n) interactions any agent outputs the exact value of κ. Simulation results illustrate our theoretical analysis.

A causal ordering protocol ensures that if two messages are causally related and have the same destination, they are delivered to the application in their sending order. Causal order strongly simplifies the development of distributed object-oriented systems. To prevent causal order violation, either messages may be forced to wait for messages in their past, or late messages may have to be discarded. For a real-time setting, the first approach is not suitable since when a message misses a deadline, all the messages that causally depend on it may also be forced to miss their deadlines. We propose a novel causal ordering abstraction that takes messages deadlines into consideration. Two implementations are proposed in the context of multicast and broadcast communication that delivers as many messages as possible to the application. Examples of distributed soft real-time applications that benefit from the use of a deadline-constrained causal ordering primitive are given.

This paper is the first to specify blockchains as a composition of abstract data types all together with a hierarchy of consistency criteria that formally characterizes the histories admissible for distributed programs that use them. The paper presents as well some results on implementability of the presented abstractions and a mapping of representative existing blockchains from both academia and industry in our framework.

Abstract. We study in this paper a generalized coupon collector problem, which consists in determining the distribution and the moments of the time needed to collect a given number of distinct coupons that are drawn from a set of coupons with an arbitrary probability distribution. We suppose that a special coupon called the null coupon can be drawn but never belongs to any collection. In this context, we obtain expressions of the distribution and the moments of this time. We also prove that the almost-uniform distribution, for which all the non-null coupons have the same drawing probability, is the distribution which minimizes the expected time to get a fixed subset of distinct coupons. This optimization result is extended to the complementary distribution of that time when the full collection is considered, proving by the way this well-known conjecture. Finally, we propose a new conjecture which expresses the fact that the almost-uniform distribution should minimize the complementary distribution of the time needed to get any fixed number of distinct coupons.

Key grouping is a technique used by stream processing frameworks to simplify the development of parallel stateful operators. Through key grouping a stream of tuples is partitioned in several disjoint sub-streams depending on the values contained in the tuples themselves. Each operator instance target of one sub-stream is guaranteed to receive all the tuples containing a specific key value. A common solution to implement key grouping is through hash functions that, however, are known to cause load imbalances on the target operator instances when the input data stream is characterized by a skewed value distribution. In this paper we present DKG, a novel approach to key grouping that provides near-optimal load distribution for input streams with skewed value distribution. DKG starts from the simple observation that with such inputs the load balance is strongly driven by the most frequent values; it identifies such values and explicitly maps them to sub-streams together with groups of less frequent items to achieve a near-optimal load balance. We provide theoretical approximation bounds for the quality of the mapping derived by DKG and show, through both simulations and a running prototype, its impact on stream processing applications.

The computational model of population protocols is a formalism that allows the analysis of properties emerging from simple and pairwise interactions among a very large number of anonymous finite-state agents. Significant work has been done so far to determine which problems are solvable in this model and at which cost in terms of states used by the agents and time needed to converge. The problem tackled in this paper is the population proportion problem: each agent starts independently from each other in one of two states, say A or B, and the objective is for each agent to determine the proportion of agents that initially started in state A, assuming that each agent only uses a finite set of states, and does not know the number n of agents. We propose a solution which guarantees that in presence of a uniform probabilistic scheduler every agent outputs the population proportion with any precision ε ∈ (0, 1) with any high probability after having interacted O(log n) times. The number of states maintained by every agent is optimal and is equal to 2 3/(4ε) + 1. Finally, we show that our solution is optimal in time and space to solve the counting problem, a generalization of the proportion problem. Finally, simulation results illustrate our theoretical analysis.