Intelligent Pulverised Collective-Adaptive Systems

The concept of Edge-cloud Continuum (ECC) serves as a strategic infrastructure for deploying modern Collective-adaptive Systems (CASs). In this framework, heterogeneous devices create a continuum between the edge and the cloud, offering new opportunities and challenges for deploying collective systems such as smart cities, IoT applications, and more. Pre-liminary work, like the pulverisation approach, models a system as an ensemble of logical entities connected forming a dynamic graph, where each device is decomposed into five independent components (i.e., sensors, actuators, state, communication, and behaviour). This approach addresses the challenge of devising an application partitioning strategy to effectively deploy collective systems in the continuum but does not provide an explicit mechanism to handle dynamic system reconfiguration. For this reason, learning approaches can be effective in managing the dynamic and continuously evolving requirements of the ECC (e.g., latency, power consumption, computational resources). In this paper we propose a new generation of 'Intelligent Collective Services' that uses advanced partitioning models and learning approaches, such as Graph Neural Network (GNN) and Many-agent Reinforcement Learning (MARL), to enhance adaptability and pave the way for the next generation of CAS in the ECC.