In the Cloud-Edge Continuum, dynamic infrastructure change and variable workloads complicate efficient resource management. Centralized methods can struggle to adapt, whilst purely decentralized policies lack global oversight. This paper proposes a hybrid framework using Graph Neural Network (GNN) embeddings and collaborative multi-agent reinforcement learning (MARL). Local agents handle neighbourhood-level decisions, and a global orchestrator coordinates system-wide. This work contributes to decentralized application placement strategies with centralized oversight, GNN integration and collaborative MARL for efficient, adaptive and scalable resource management.

The TEACHING project enhances AI applications in pervasive environments via Humanistic Intelligence, fostering synergy between humans and Cyber-Physical Systems of Systems (CPSoS). Here, we present the TEACHING Platform, a microservice-based framework providing the technological advancements to represent humans and CPSoS as containerized software models that interact to mutually empower each other.

An increasing number of application domains require high-throughput processing to extract insights from massive data streams. The Data Stream Processing (DSP) paradigm provides formal approaches to analyze structured data streams considered as special, unbounded relations. The most used class of stateful operators in DSP are the ones running sliding-window aggregation, which continuously extracts insights from the most recent portion of the stream. This article presentsSpringald, an efficient sliding-window operator leveraging GPU devices.Springald, incorporated in theWindFlowparallel library, processes out-of-order data streams with watermarks propagation. These two features—GPU processing and out-of-orderliness—makeSpringalda novel contribution to this research area. This article describes the methodology behindSpringald, its design and implementation. We also provide an extensive experimental evaluation to understand the behavior ofSpringalddeeply, and we showcase its superior performance against state-of-the-art competitors.

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.

The 2nd International Workshop on Flexible Resource and Application Management on the Edge (FRAME 2022) is dedicated to the so-called Cloud/Edge Continuum, where Cloud and Edge infrastructures can work together to fulfill requirements from a variety of NextGen applications. Clouds provide appropriate levels of performance to large groups of different users, whereas Edge resources act as a first layer of computing capacity that is closer to the user, to reduce the service latency. With respect to Clouds, Edge infrastructures typically are composed of heterogeneous and constrained resources and introduce new challenges from the viewpoint of security, orchestration and resource management. Tackling these new issues calls for innovative combinations of tools and abstractions, where AI and machine learning techniques complement algorithmic orchestration and optimization, bringing about new levels of distributed adaptivity and self-management. As real-time data-driven decisions can be promptly taken on the spot, without the need to wait for data to travel to the Cloud and back, also interactive and time-sensitive services like the immersive data processing of Extended Reality (XR) applications can be partially extended toward the edge, thus exploiting a better computation to communication tradeoff and smoother connections to improve their QoE and remote collaboration. The FRAME'22 workshop proceedings are available at: https://dl.acm.org/citation.cfm?id=3526059.

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.

Summary A fundamental problem for the development of peer‐to‐peer (P2P) distributed virtual environments, like massively multiplayer on‐line games, is the definition of an overlay supporting interest management, that is, determining all the entities of the virtual world that are relevant for a given player. To this end, this paper proposes a gossip‐based approach that considers the coverage of the area of interest of peers as the guiding principle for the definition of the P2P overlay and its maintenance. The resulting overlay provides a support for a best‐effort resolution of interest management, mostly supported through communications on the P2P overlay, with minimal intervention of a centralized entity. The paper presents a set of extensive simulations based on realistic mobility traces. The experimental results show the effectiveness of gossiping for the construction and maintenance of a best‐effort overlay for interest management. Copyright © 2014 John Wiley & Sons, Ltd.

Summary This paper presents a novel Area Of Interest (AOI)‐cast algorithm for distributed virtual environments targeted to Delaunay‐based P2P overlays. The algorithm exploits the mathematical properties of Delaunay triangulations to build a spanning tree supporting the notification of the events generated by a peer to the other ones located in its AOI. The spanning tree is computed by thereversing compass routing, a routing algorithm proposed for geometric networks. Our approach presents a set of novel features. First, it requires only the knowledge of the peer's neighbors, so that the amount of traffic load on the P2P overlay is minimized. Second, we prove that, for circular shaped AOI, the algorithm builds a spanning tree covering all and only the peers of the AOI. Finally, our approach takes into account the possible inconsistencies among the local views of the peers, because the network latency, by introducing a tolerance threshold in the reverse compass routing. We present a set of simulations considering both synthetic data and real data traces taken from a real multiplayer game, which show the effectiveness of our proposal. Copyright © 2012 John Wiley & Sons, Ltd.

The broad diffusion of Cloud Computing has fostered the proliferation of a large number of cloud computing providers. The need of Cloud Brokers arises for helping consumers in discovering, considering and comparing services with different capabilities and offered by different providers. Also, consuming services exposed by different providers, when possible, may alleviate the vendor lock-in. While it can be straightforward to choose the best provider when deploying small and homogeneous applications, things get harder if the size and complexity of applications grow up. In this paper we propose a genetic approach for Cloud Brokering, focusing on finding Infrastructure-as-a-Service (IaaS) resources for satisfying Quality of Service (QoS) requirements of applications. We performed a set of experiments with an implementation of such broker. Results show that our broker can find near-optimal solutions even when dealing with hundreds of providers, trying at the same time to mitigate the vendor lock-in.

Cloud Federation is a promising approach to enhance cross-cloud application execution. Nevertheless, such approach emphasizes open challenges in Cloud Computing, such as revoking long-lasting authorization on resources as soon as conditions granting the access right are no longer valid. To tackle this kind of issues, we built a prototype of Cloud Federation that leverages the concept of Usage Control (UCON), by continuously monitoring and reassessing the users right on resources. We exploited an extension of the XACML standard and measured the overhead caused by different security policies and distributions of requests. Results suggest that the UCON model can be effectively applied in Cloud Federations and its performance is sustainable when applied to the relevant actions of the lifecycle of applications.

Scaling down in very deep submicron (VDSM) technologies increases the delay, power consumption of on-chip interconnects, while the reliability and yield decrease. In high performance integrated circuits wires become the performance bottleneck and we are shifting towards communication centric design paradigms. Networks-on-chip and stacked 3D integration are two emerging technologies that alleviate the performance difficulties of on-chip interconnects in nano-scale designs. In this paper we present a design-time configurable error correction scheme integrated at link-level in the 3D Spidergon STNoC on-chip communication platform. The proposed scheme detects errors and selectively corrects them on the fly, depending on the critical nature of the transmitted information, making thus the correction software controllable. Moreover, the proposed scheme can correct multiple error patterns by using interleaved single error correction codes, providing an increased level of reliability. The performance of the link and its cost in silicon and vertical wires are evaluated for various configurations.

This paper defines and evaluates a hierarchical distributed data structure, distributed digest trie, supporting range queries in P2P systems. Providing efficient support for these queries is currently a challenging research issue in the P2P field, as classical approaches based on distributed hash ta

Reconfigurable architectures and NoC (Network-on-Chip) have introduced new research directions for technology and flexibility issues, which have been largely investigated in the last decades. Exploiting run-time adaptivity opens a new area of research by considering dynamic reconfiguration. In this paper, we present the architecture and associated development tools of an heterogeneous reconfigurable SoC focusing on the chosen communication infrastructure. The SOC integrates units of various sizes of reconfiguration granularity. The included NoC approach demonstrates the mentioned benefits and scalability for actual and future SoC design. On a reference CMOS090 implementation the described interconnect system works at the system reference frequency of 200 MHZ sustaining the required run-time bandwidth on a set of reference applications, at a price ≪ 10% in area in power consumption with respect to the overall system.

The concept of a SOC platform architecture introduces the concept of a communication infrastructure. In the transaction-level, a finite set of architecture components (memories, arithmetic units, address generators, caches, etc) communicate among each other over shared resources (buses). Until recently, modeling architectures required pin-level hardware descriptions, typically coded in RTL. Great effort is required to design and verify the models, and simulation at this level of detail is tediously slow. Transaction level modeling is the solution. Transaction level models (TLMs) effectively create an executable platform model that simulates orders of magnitude faster than a RTL model.