Serverless computing is a promising paradigm for deploying and managing applications on edge infrastructures. It provides small granularity and high flexibility by decomposing applications into lightweight functions. Although this modularity facilitates efficient resource allocation and function placement on edge nodes, complex dependencies among functions pose significant challenges to their effective management. Existing research has explored various optimization techniques for serverless computing platforms, but dependency-aware function placement remains an open challenge. In this paper, we propose PLUTO, an efficient solution for the placement of serverless functions that supports complex dependencies. First, we present an optimal non-linear formulation of the placement problem. Then, we introduce a heuristic approach, derived from the optimal formulation, that ensures efficiency as the number of functions increases. An extensive empirical evaluation against state-of-the-art solutions shows that PLUTO significantly reduces the overall delay and memory consumption by up to 85% and 78%, respectively.
Efficient and Dependency-Aware Placement of Serverless Functions on Edge Infrastructures
Baresi, Luciano;Quattrocchi, Giovanni;Ticongolo, Inacio Gaspar
2025-01-01
Abstract
Serverless computing is a promising paradigm for deploying and managing applications on edge infrastructures. It provides small granularity and high flexibility by decomposing applications into lightweight functions. Although this modularity facilitates efficient resource allocation and function placement on edge nodes, complex dependencies among functions pose significant challenges to their effective management. Existing research has explored various optimization techniques for serverless computing platforms, but dependency-aware function placement remains an open challenge. In this paper, we propose PLUTO, an efficient solution for the placement of serverless functions that supports complex dependencies. First, we present an optimal non-linear formulation of the placement problem. Then, we introduce a heuristic approach, derived from the optimal formulation, that ensures efficiency as the number of functions increases. An extensive empirical evaluation against state-of-the-art solutions shows that PLUTO significantly reduces the overall delay and memory consumption by up to 85% and 78%, respectively.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.