In critical scenarios like disaster response or real-time monitoring, efficient and adaptive task scheduling is essential. This paper presents a decentralized framework using Nash Q-learning, a game-theoretic reinforcement learning technique, for urgent edge computing. Tasks are allocated among agents based on strategies derived from multi-agent interactions modeled as a Markov Game. The method promotes decentralized cooperation and improves responsiveness by adapting to dynamic conditions. We provide a formal model and outline its applicability to various edge environments.

We present Rusty-Cracker, a high-performance Rust library that implements a parallel version of the Cracker algorithm for efficiently identifying connected components in large-scale graphs. Designed to address the growing demands of graph analytics in fields such as social network analysis, bioinformatics, and infrastructure modeling, Rusty-Cracker leverages Rust's concurrency and memory safety features to ensure both speed and reliability. The adapted Cracker algorithm capitalizes on modern multi-core architectures through parallel processing, effectively minimizing synchronization overhead and optimizing workload distribution. This design significantly reduces computational time while maintaining accuracy. We present the implementation details and evaluate its performance on a real-world dataset of undirected graphs.

This paper presents SPARE, a novel serverless platform that supports self-adaptive resource allocation and reconfiguration, thereby increasing the availability of computing resources for time-critical tasks in urgent events. In emergency scenarios, SPARE reallocates resources by forwarding serverless function invocations to the nearest edge nodes having sufficient capacity. Additionally, the platform employs the use of unikernels and lightweight virtualization through Firecracker, which helps to reduce cold start times and improve function responsiveness. The experimental results demonstrate that SPARE is capable of releasing up to one-third of edge nodes within a serverless edge platform, while only experiencing a mild increase in latency, thus maintaining service continuity.

This paper presents a novel algorithm for the Vertex Cover problem, inspired by the Think-Like-A-Vertex (TLAV) paradigm. The Vertex Cover problem, a fundamental challenge in graph theory, finds significant relevance in the context of the compute continuum, where the optimal placement of application images across a diverse range of computational resources is a critical concern. Our proposed TLAV-based algorithm addresses this challenge by leveraging local information at each vertex to make intelligent decisions, thereby reducing the global complexity of the problem. While this approach could potentially lead to resource overprovisioning, we argue that in the context of the compute continuum, this trade-off can provide more flexibility and redundancy, enhancing the reliability of the system. Through extensive analysis and experimental results, we demonstrate the efficiency and scalability of our algorithm on large-scale graphs, making a significant contribution to the field of resource management in the compute continuum.

The transition from Cloud Computing to a Cloud-Edge continuum brings many new exciting possibilities for interactive and data-intensive Next Generation applications, but as many challenges. Approaches and solutions that successfully worked in the Cloud space now need to be rethought for the Edge's distributed, heterogeneous and dynamic ecosystem. The placement of application images needs to be proactively devised to reduce as much as possible the image transfer time and comply with the dynamic nature and strict requirements of the applications. To this end, this paper proposes an approach based on the combination of Graph Neural Networks and actor-critic Reinforcement Learning. The approach is analyzed empirically and compared with a state-of-the-art solution. The results show that the proposed approach exhibits a larger execution times but generally better results in terms of application image placement.

This paper proposes a continuum-aware programming model enabling the execution of application workflows across the compute continuum: cloud, fog and edge resources. It simplifies the management of heterogeneous nodes while alleviating the burden of programmers and unleashing innovation. This model optimizes the continuum through advanced development experiences by transforming workflows into autonomous service collaborations. It reduces complexity in positioning/interconnecting services across the continuum. A meta-model introduces high-level workflow descriptions as service networks with defined contracts and quality of service, thus enabling the deployment/management of workflows as first-class entities. It also provides automation based on policies, monitoring and heuristics. Tailored mechanisms orchestrate/manage services across the continuum, optimizing performance, cost, data protection and sustainability while managing risks. This model facilitates incremental development with visibility of design impacts and seamless evolution of applications and infrastructures. In this work, we explore this new computing paradigm showing how it can trigger the development of a new generation of tools to support the compute continuum progress.

Federated learning is a popular framework that enables harvesting edge resources' computational power to train a machine learning model distributively. However, it is not always feasible or profitable to have a centralized server that controls and synchronizes the training process. In this paper, we consider the problem of training a machine learning model over a network of nodes in a fully decentralized fashion. In particular, we look for empirical evidence on how sensitive is the training process for various network characteristics and communication parameters. We present the outcome of several simulations conducted with different network topologies, datasets, and machine learning models.

Data management using Device-to-Device (D2D) communications and opportunistic networks (ONs) is one of the main focuses of human-centric pervasive Internet services. In the recently proposed "Internet of People" paradigm, accessing relevant data dynamically generated in the environment nearby is one of the key services. Moreover, personal mobile devices become proxies of their human users while exchanging data in the cyber world and, thus, largely use ONs and D2D communications for exchanging data directly. Recently, researchers have successfully demonstrated the viability of embedding human cognitive schemes in data dissemination algorithms for ONs. In this paper, we consider one such scheme based on the recognition heuristic, a human decision-making scheme used to efficiently assess the relevance of data. While initial evidence about its effectiveness is available, the evaluation of its behaviour in large-scale settings is still unsatisfactory. To overcome these limitations, we have developed a novel hybrid modelling methodology, which combines an analytical model of data dissemination within small-scale communities of mobile users, with detailed simulations of interactions between different communities. This methodology allows us to evaluate the algorithm in large-scale city- and country-wide scenarios. Results confirm the effectiveness of cognitive data dissemination schemes, even when content popularity is very heterogenous.

In cyber-physical convergence scenarios information flows seamlessly between the physical and the cyber worlds. Here, users’ mobile devices represent a natural bridge through which users process acquired information and perform actions. The sheer amount of data available in this context calls for novel, autonomous and lightweight data-filtering solutions, where only relevant information is finally presented to users. Moreover, in many real-world scenarios data is not categorised in predefined topics, but it is generally accompanied by semantic descriptions possibly describing users’ interests. In these complex conditions, user devices should autonomously become aware not only of the existence of data in the network, but also of their semantic descriptions and correlations between them. To tackle these issues, we present a set of algorithms for knowledge and data dissemination in opportunistic networks, based on simple and very effective models (called cognitive heuristics) coming from cognitive sciences. We show how to exploit them to disseminate both semantic data and the corresponding data items. We provide a thorough performance analysis, under various different conditions comparing our results against non-cognitive solutions. Simulation results demonstrate the superior performance of our solution towards a more effective semantic knowledge acquisition and representation, and a more tailored content acquisition.

In typical Opportunistic Networking (OppNets) scenarios, mobile devices collaborate to cooperatively disseminate data toward interested nodes. However, the limited resources and knowledge available at each node, compared to possibly vast amounts of data to be delivered, makes it difficult to devise efficient dissemination schemes. Recent solutions propose to use data dissemination algorithms built on human information processing schemes, modelled in cognitive sciences as Cognitive Heuristics. In general, they are methods used by the human brain to quickly assess relevance of information so to drop what is irrelevant. Recent solutions for data dissemination in OppNets based on these heuristics proved to be effective and efficient in terms of network overhead. However, to the best of our knowledge, none takes into consideration the structure of users' social relationships, which is known to determine movement patterns and thus contact opportunities between nodes. In this paper we propose a social-based data dissemination scheme, built on the Social Circle Heuristic (SCH). SCH exploits the structure of the social environment of users to infer the relevance of discovered information for the individual and their social communities. We compare the proposed scheme against state-of-the-art solutions based on non-social cognitive heuristics, both in terms of effectiveness (i.e., bringing messages to users that request it) and efficiency (i.e., doing so minimising the network traffic). We show that the scheme based on SCH significantly outperforms non-social cognitive schemes along both dimensions. In particular, the difference becomes more and more evident as scenarios becomes more and more dynamic. We finally show that in scenarios where new content is generated over time, the scheme based on SCH is the only one able to bring content to the interested users, while non-social schemes fail to do so while at the same time generating significant higher network traffic.

Until recently crowdsourcing has been primarily conceived as an online activity to harness resources for problem solving. However, the emergence of Opportunistic Networking (ON) has opened up crowdsourcing to the spatial domain. In this article, we bring the ON model for potential crowdsourcing in the smart city environment. We introduce cognitive features of the ON that allow users’ mobile devices to become aware of the surrounding physical environment. Specifically, we exploit cognitive psychology studies on dynamic memory structures and cognitive heuristics—mental models that describe how the human brain handles decision making among complex and real-time stimuli. Combined with ON, these cognitive features allow devices to act as proxies in their users’ cyberworlds and exchange knowledge to deliver awareness of places in an urban environment. This is done through tags associated with locations. They represent features that are perceived by humans about a place. We consider the extent to which this knowledge becomes available to participants using interactions with locations and other nodes. This is assessed taking into account a wide range of cognitive parameters. Outcomes are important because this functionality could support a new type of recommendation system that is independent of the traditional forms of networking.

Over recent years, we experienced a huge diffusion of internet connected computing devices. As a consequence, this leaded to research for efficient and scalable approaches for managing the burden caused by the highly increased volume of data to be exchanged and processed. Efficient communication protocols are fundamental building blocks for realizing such approaches [1], [2]. Thus, several peer-to-peer protocols have been proposed. Gossip protocols [3]-[7] are a family of peer-to-peer protocols that proved to be well-suited for supporting a scalable and decentralized strategy for peer and data aggregation and diffusion. However, one of the typical limitation of Gossip protocols consists in the selfish behavior adopted by peers in defining their neighborhood and, as a consequence, the topology of the overlay they build. GROUP [8] is a Gossip protocol we conceived to overcome this limitation. It builds explicit defined communities of peers that are identified by their leaders, each one elected in a distributed fashion. This protocol experimentally proved to be efficient and effective with respect to its aim. Anyhow, no analytical study has been realized so far. This work presents a currently ongoing work we are conducting for exploring the properties of GROUP in a more formal way. We conduct this preliminary investigation using a formalization based on Markov chains.

Opportunistic networking is one of the key paradigms to support direct communication between devices in a mobile scenario. In this context, the high volatility and dynamicity of information and the fact that mobile nodes have to make decisions in condition of partial or incomplete knowledge, makes the development of effective and efficient data dissemination schemes very challenging. In this paper we present algorithms based on well-established models in cognitive sciences, in order to disseminate both data items, and semantic information associated with them. In our approach, semantic information represents both meta-data associated to data items (e.g., tags associated to them), and meta-data describing the interests of the users (e.g., topics for which they would like to receive data items). Our solution exploits dissemination of semantic data about the users' interests to guide the dissemination of the corresponding data items. Both dissemination processes are based on models coming from the cognitive sciences field, named cognitive heuristics, which describe how humans organise information in their memory and exchange it during interactions based on partial and incomplete information. We exploit a model describing how semantic data can be organised in each node in a semantic network, based on how humans organise information in their memory. Then, we define algorithms based on cognitive heuristics to disseminate both semantic data and data items between nodes upon encounters. Finally, we provide initial performance results about the diffusion of interests among users, and the corresponding diffusion of data items.

In the convergence of the Cyber-Physical World , user devices will act as proxies of the humans in the cyber world. They will be required to act in a vast information landscape, asserting the relevance of data spread in the cyber world, in order to let their human users become aware of the content they really need. This is a remarkably similar situation to what the human brain has to do all the time when deciding what information coming from the surrounding environment is interesting and what can simply be ignored. The brain performs this task using so called cognitive heuristics, i.e. simple, rapid, yet very effective schemes. In this article, we propose a new approach that exploits one of these heuristics, the recognition heuristic , for developing a self-adaptive system that deals with effective data dissemination in opportunistic networks. We show how to implement it and provide an extensive analysis via simulation. Specifically, results show that the proposed solution is as effective as state-of-the-art solutions for data dissemination in opportunistic networks, while requiring far less resources. Finally, our sensitiveness analysis shows how various parameters depend on the context where nodes are situated, and suggest corresponding optimal configurations for the algorithm.

When faced with large amounts of data, human brains are able to swiftly react to stimuli and assert relevance of discovered information, even under uncertainty and partial knowledge. These efficient decision-making abilities rely on so-called cognitive heuristics, which are rapid, adaptive, light-weight yet very effective schemes used by the brain to solve complex problems. In a content-centric future Internet where users generate and disseminate large amounts of content through opportunistic networking techniques, individual nodes should exhibit those properties to support a scalable content dissemination system. We therefore study whether such cognitive heuristics can also be used in such a networking environment. To this end, in this paper we develop an analytical model that describes a content dissemination mechanism for opportunistic networks based on one such heuristics, known as the recognition heuristic. Our model takes into account the different popularities of content types, and highlights the impact of the shared memory contributed by individual nodes to make the dissemination process more efficient. Furthermore, our model allows us to investigate the performance of the dissemination process for very large number of nodes, which might be very difficult to carry out through a simulation-based study.

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 generate, 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 relevance of discovered data under uncertainty and only partial information. The human brain performs the task of information filtering and selection using the so-called cognitive heuristics, i.e. simple, rapid, low-resource demanding, yet very effective schemes that can be modeled using a functional 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 model an algorithm that exploits the environmental information in order to implement an effective dissemination of data based on the recognition heuristic, and provide a performance evaluation of such a solution via simulation.

Several complex and time-critical applications require the existence of novel distributed and dynamical platforms composed of a variety of fixed and mobile processing nodes and networks. Notable examples of such applications are crisis and emergency management and natural phenomenon prediction. In this scenario we need the development of applications able to adapt their behavior according to the dynamical platform conditions, such as the presence of specific classes of computing resources and the actual network availability. For these reasons such adaptive applications need to interact with a fast and reliable resource discovery support, which ensures required response times by means of an high-degree of reconfigurability and selectivity. In this paper we present an integrated approach between our programming model for distributed adaptive time-critical computations and a suitable resource discovery support.

The problem of defining a support for multidimensional range queries on P2P overlays is currently an active field of research. Several approaches based on the extension of the basic functionalities offered by Distributed Hash Tables have been recently proposed. The main drawback of these approaches is that the locality required for the resolution of a range query cannot be guaranteed by uniform hashing. On the other way, locality preserving hashing functions do not guarantee a good level of load balancing. This paper presents Hivory, a P2P overlay based on a Voronoi tessellation defined by the objects published by peers. Each object is mapped to a site of the Voronoi tessellation and the corresponding Delaunay Triangulation defines the P2P overlay. A hierarchy of Voronoi diagrams is defined by exploiting clusters of objects paired with the same site of the Voronoi diagram. A new Voronoi diagram including the peers of the cluster is created so that the query resolution may be refined by a top down visit of the Voronoi hierarchy. The paper presents the proposed solution, analysis its complexity, and provides a set of experimental results.

Locating data on peer-to-peer networks is a complex issue addressed by many P2P protocols. Most of the research in this area only considers static content, that is, it is often assumed that data in P2P systems do not vary over time. In this paper, we describe a data location strategy for dynamic content on P2P networks. Data location exploits a distributed index based on bit vectors: this index is used to route queries towards areas of the system where matches can be found. The bit vectors can be efficiently updated when data is modified. Simulation results show that the proposed algorithms for queries and updates propagation have good performances, also on large networks, even if content exhibits a high degree of variability.