Centrality metrics are a key instrument for graph analysis and play a central role in many problems related to networking such as service placement, robustness analysis and network optimization. Betweenness centrality is one of the most popular and well-studied metric. While distributed algorithms to compute this metric exist, they are either approximated or limited to certain topologies (directed acyclic graphs or trees). Exact distributed algorithms for betweenness centrality are computationally complex, because its calculation requires the knowledge of all possible shortest paths within the graph. In this paper we consider load centrality, a metric that usually converges to betweenness, and we present the first distributed and exact algorithm to compute it. We prove its convergence, we estimate its complexity and we show it is directly applicable-with minimal modifications-to any distance-vector routing protocol based on Bellman-Ford. We finally implement it on top of the Babel routing protocol and we show that, exploiting centrality, we can significantly reduce Babel's convergence time upon node failure without increasing signalling overhead. Our contribution is relevant in the realm of wireless distributed networks, but the algorithm can be adopted in any distributed system where it is not possible, or computationally impractical, to reconstruct the whole network graph at each node and compute betweenness centrality with the classical approach based on Dijkstra's algorithm.

As graphs become bigger, the need to efficiently partition them becomes more pressing. Most graph partitioning algorithms subdivide the vertex set into partitions of similar size, trying to keep the number of cut edges as small as possible. An alternative approach divides the edge set, with the goal of obtaining more balanced partitions in presence of high-degree nodes, such as hubs in real world networks, that can be split between distinct partitions. We introduce dfep, a distributed edge partitioning algorithm based on the metaphor of currency distribution. Each partition starts from a random edge and expands independently by spending currency to buy neighboring edges. After each iteration, smaller partitions receive an higher amount of currency to help them recover lost ground and reach a similar size to the other partitions. Simulation experiments show that dfep is efficient and obtains consistently balanced partitions. Implementations on both Hadoop and Spark show the scalability of our approach.

Abstract

Abstract. The problem of identifying the most frequent items across multiple datasets has received considerable attention over the last few years. When storage is a scarce resource, the topic is already a challenge; yet, its complexity may be further exacerbated not only by the many independent data sources, but also by the dynamism of the data, i.e., the fact that new items may appear and old ones disappear at any time. In this work, we provide a novel approach to the problem by using an existing gossip-based algorithm for identifying the k most frequent items over a distributed collection of datasets, in ways that deal with the dynamic nature of the data. The algorithm has been thoroughly analyzed through trace-based simulations and compared to state-of-the-art decentralized solutions, showing better precision at reduced communication overhead.

Distributed estimation of global parameters in intermittently connected mobile environments is a challenging problem. In this paper, we introduce a set of methods, based on gossip techniques and population protocols, for performing such task. The applicability of such techniques to various environments, characterized by different mobility patterns, is evaluated through numerical simulations and discussed extensively. Guidelines are provided to help practitioners choosing the right method for their specific application problem.