Epidemic Information Diffusion: A Simple Solution to Support Community-based Recommendations in P2P Overlays

Epidemic protocols proved to be very efficient solutions for supporting dynamic and complex information diffusion in highly dis- tributed computing infrastructures, like P2P environments. They are useful bricks for building and maintaining virtual network topologies, in the form of overlay networks as well as to support pervasive diffusion of information when it is injected into the network. This paper proposes a simple architecture exploiting the features of epidemic approaches to foster a collaborative percolation of information between computing nodes belonging to the network aimed at building a system that groups similar users and spread useful information among them.Comment: 8 pages, 2 figure

Design and Performance Evaluation of Data Dissemination Systems for Opportunistic Networks Based on Cognitive Heuristics

It is often argued that the Future Internet will be a very large scale content-centric network. Scalability issues will stem even more from the amount of content nodes will gen- erate, share and consume. In order to let users become aware and retrieve the content they really need, these nodes will be required to swiftly react to stimuli and assert the rele- vance of discovered data under uncertainty and only partial information. The human brain performs the task of infor- mation ltering and selection using the so-called cognitive heuristics, i.e. simple, rapid, low-resource demanding, yet very eective schemes that can be modeled using a func- tional approach. In this paper we propose a solution based on one such heuristics, namely the recognition heuristic, for dealing with data dissemination in opportunistic networks. We show how to implement an algorithm that exploits the environmental information in order to implement an eec- tive dissemination of data based on the recognition heuristic, and provide a performance evaluation of such a solution via simulation

A Holistic Approach for High-level Programming of Next-generation Data-intensive Applications Targeting Distributed Heterogeneous Computing Environment

AbstractThe intrinsic richness and heterogeneity of large amount of data is paired with the extreme complexity in its storing and processing, as well as with the heterogeneity of their processing environments, ranging from super computers to federations of Cloud data-centres. This makes the conception, definition and implementation of software tools for programming applications dealing with very large amount of data really challenging from different perspectives, ranging from technological issues to economic concerns. We propose an approach focused on data-intensive applications that goes beyond the state of the art allowing a seamless exploitation of heterogeneous and distributed resources and satisfying users’ needs on data processing providing a dynamically determined set of features, depending on the running environment, the application, the user requirements

AoI-based Multicast Routing over Voronoi Overlays with Minimal Overhead

The increasing pervasive and ubiquitous presence of devices at the edge of the Internet is creating new scenarios for the emergence of novel services and applications. This is particularly true for location- and context-aware services. These services call for new decentralized, self-organizing communication schemes that are able to face issues related to demanding resource consumption constraints, while ensuring efficient locality-based information dissemination and querying. Voronoi-based communication techniques are among the most widely used solutions in this field. However, when used for forwarding messages inside closed areas of the network (called Areas of Interest, AoIs), these solutions generally require a significant overhead in terms of redundant and/or unnecessary communications. This fact negatively impacts both the devices' resource consumption levels, as well as the network bandwidth usage. In order to eliminate all unnecessary communications, in this paper we present the MABRAVO (Multicast Algorithm for Broadcast and Routing over AoIs in Voronoi Overlays) protocol suite. MABRAVO allows to forward information within an AoI in a Voronoi network using only local information, reaching all the devices in the area, and using the lowest possible number of messages, i.e., just one message for each node included in the AoI. The paper presents the mathematical and algorithmic descriptions of MABRAVO, as well as experimental findings of its performance, showing its ability to reduce communication costs to the strictly minimum required.Comment: Submitted to: IEEE Access; CodeOcean: DOI:10.24433/CO.1722184.v1; code: https://github.com/michelealbano/mabrav

On Democracy in Peer-to-Peer systems

The information flow inside a P2P network is highly dependent on the network structure. In order to ease the diffusion of relevant data toward interested peers, many P2P protocols gather similar nodes by putting them in direct contact. With this approach the similarity between nodes is computed in a point-to-point fashion: each peer individually identifies the nodes that share similar interests with it. This leads to the creation of a sort of "private" communities, limited to each peer neighbors list. This "private" knowledge do not allow to identify the features needed to discover and characterize the correlations that collect similar peers in broader groups. In order to let these correlations to emerge, the collective knowledge of peers must be exploited. One common problem to overcome in order to avoid the "private" vision of the network, is related to how distributively determine the representation of a community and how nodes may decide to belong to it. We propose to use a gossip-like approach in order to let peers elect and identify leaders of interest communities. Once leaders are elected, their profiles are used as community representatives. Peers decide to adhere to a community or another by choosing the most similar representative they know about

Energy and QoE aware Placement of Applications and Data at the Edge

Recent years are witnessing extensions of cyber-infrastructures towards distributed environments. The Edge of the network is gaining a central role in the agenda of both infrastructure and application providers. Following the actual distributed structure of such a computational environment, nowadays, many solutions face resource and application management needs in Cloud/Edge continua. One of the most challenging aspects is ensuring highly available computing and data infrastructures while optimizing the system's energy consumption. In this paper, we describe a decentralized solution that limits the energy consumption by the system without failing to match the users' expectations, defined as the services' Quality of Experience (QoE) when accessing data and leveraging applications at the Edge. Experimental evaluations through simulation conducted with PureEdgeSim demonstrate the effectiveness of the approach

Decentralized Federated Learning and Network Topologies: An Empirical Study on Convergence

Federated Learning is a well-known learning paradigm that allows the distributed training of machine learning models. Federated Learning keeps data in the source devices and communicates only the model's coefficients to a centralized server. This paper studies the decentralized flavor of Federated Learning. A peer-to-peer network replaces the centralized server, and nodes exchange model's coefficients directly. In particular, we look for empirical evidence on the effect of different network topologies and communication parameters on the convergence in the training of distributed models. Our observations suggest that small-world networks converge faster for small amounts of nodes, while xx are more suitable for larger setups

SmartORC: smart orchestration of resources in the compute continuum

The promise of the compute continuum is to present applications with a flexible and transparent view of the resources in the Internet of Things–Edge–Cloud ecosystem. However, such a promise requires tackling complex challenges to maximize the benefits of both the cloud and the edge. Challenges include managing a highly distributed platform, matching services and resources, harnessing resource heterogeneity, and adapting the deployment of services to the changes in resources and applications. In this study, we present SmartORC, a comprehensive set of components designed to provide a complete framework for managing resources and applications in the Compute Continuum. Along with the description of all the SmartORC subcomponents, we have also provided the results of an evaluation aimed at showcasing the framework's capability

A cognitive-based solution for semantic knowledge and content dissemination in opportunistic networks

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GROUP: A Gossip Based Building Community Protocol

The detection of communities of peers characterized by similar interests is currently a challenging research area. To ease the diffusion of relevant data to interested peers, similarity based overlays define links between similar peers by exploiting a similarity function. However, existing solutions neither give a clear definition of peer communities nor define a clear strategy to partition the peers into communities. As a consequence, the spread of the information cannot be confined within a well defined region of an overlay. This paper proposes a distributed protocol for the detection of communities in a P2P network. Our approach is based on the definition of a distributed voting algorithm where each peer chooses the more similar peers among those in a limited neighbourhood range. The identifier of the most representative peer is exploited to identify a community. The paper shows the effectiveness of our approach by presenting a set of experimental results