In the mobile-edge-cloud continuum, a plethora of heterogeneous data sources and computation-capable nodes are available. Such nodes can cooperate to perform a distributed learning task, aided by a learning controller (often located at the network edge). The controller is required to make decisions concerning (i) data selection, i.e., which data sources to use; (ii) model selection, i.e., which machine learning model to adopt, and (iii) matching between the layers of the model and the available physical nodes. All these decisions influence each other, to a significant extent and often in counter-intuitive ways. In this paper, we formulate a problem addressing all of the above aspects and present a solution concept called RightTrain, aiming at making the aforementioned decisions in a joint manner, minimizing energy consumption subject to learning quality and latency constraints. RightTrain leverages an expanded-graph representation of the system and a delay-aware Steiner tree to obtain a provably near-optimal solution while keeping the time complexity low. Specifically, it runs in polynomial time and its decisions exhibit a competitive ratio of 2(1 + ε), outperforming state-of-the-art solutions by over 50%. Our approach is also validated through a real-world implementation.

Efficient Distributed DNNs in the Mobile-edge-cloud Continuum / Malandrino, Francesco; Chiasserini, Carla Fabiana; DI GIACOMO, Giuseppe. - In: IEEE-ACM TRANSACTIONS ON NETWORKING. - ISSN 1063-6692. - STAMPA. - (2023).

Efficient Distributed DNNs in the Mobile-edge-cloud Continuum

Carla Fabiana Chiasserini;Giuseppe Di Giacomo
2023

Abstract

In the mobile-edge-cloud continuum, a plethora of heterogeneous data sources and computation-capable nodes are available. Such nodes can cooperate to perform a distributed learning task, aided by a learning controller (often located at the network edge). The controller is required to make decisions concerning (i) data selection, i.e., which data sources to use; (ii) model selection, i.e., which machine learning model to adopt, and (iii) matching between the layers of the model and the available physical nodes. All these decisions influence each other, to a significant extent and often in counter-intuitive ways. In this paper, we formulate a problem addressing all of the above aspects and present a solution concept called RightTrain, aiming at making the aforementioned decisions in a joint manner, minimizing energy consumption subject to learning quality and latency constraints. RightTrain leverages an expanded-graph representation of the system and a delay-aware Steiner tree to obtain a provably near-optimal solution while keeping the time complexity low. Specifically, it runs in polynomial time and its decisions exhibit a competitive ratio of 2(1 + ε), outperforming state-of-the-art solutions by over 50%. Our approach is also validated through a real-world implementation.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2973031