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.

Many industrial systems include components interacting with each other that evolve with possibly very different speeds. To deal with this situation many formalisms adopt the abstraction of ``zero-time transitions'', which do not consume time. These, however, have several drawbacks in terms of naturalness and logic consistency, as a system is modeled to be in different states at the same time. We introduce a metric temporal logic, called X-TRIO, that uses non-standard analysis to elegantly deal with zero-time transitions in an abstract, descriptive way. We study the decidability of the logic, and we introduce a decision procedure for a subset thereof. X-TRIO has been applied in companion works to the design and verification of industrial systems.

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.