Physics-inspired models for agile code and data in federated edges

Abstract

We study the problem of flexibly, dynamically, and adaptively moving, positioning, and instantiating computing tasks and data in federated, distributed edge systems. We call this process 'agile code and agile data' (ACAD). We explore the adaptation of physics-inspired models, used for atomistic simulations, to the ACAD problem, treating the code and data as particles on a graph, interacting through different potential energy models. We discuss the mapping between the different elements of ACAD problem and our particles-on-A-graph model, considering different frameworks for data analytics. We explore gravitational, elastic and Coulombic models, both with global and local energy minimization, finding that the Coulombic model obtains the most efficient solution.