In this paper, we introduce MAD-FELLOWS, a distributed job offloading framework utilizing Multi-Player Multi-Armed Bandit algorithms. The primary objective of MAD-FELLOWS is to facilitate job offloading processes within networks of Unmanned Ground Vehicles (UGVs). The framework is designed to meet stringent job latency requirements while simultaneously minimizing energy consumption, thereby extending the UGV mission duration. We validate the effectiveness of MAD-FELLOWS through an extensive numerical evaluation, comparing its performance with various baselines, including a centralized, oracle-based approach. Our results demonstrate that: i) MAD-FELLOWS surpasses the baselines, achieving rapid convergence to the performance of the centralized approach in a fully-distributed manner, aligning with latency and energy efficiency criteria; ii) MAD-FELLOWS enhances UGV mission duration by up to 57%, outperforming the baseline approaches

MAD-FELLOWS: A Multi-Armed Bandit Framework for Energy-Efficient, Low-Latency Job Offloading in Robotic Networks

Busacca F.;Palazzo S.;Raftopoulos R.;Schembra G.
2024-01-01

Abstract

In this paper, we introduce MAD-FELLOWS, a distributed job offloading framework utilizing Multi-Player Multi-Armed Bandit algorithms. The primary objective of MAD-FELLOWS is to facilitate job offloading processes within networks of Unmanned Ground Vehicles (UGVs). The framework is designed to meet stringent job latency requirements while simultaneously minimizing energy consumption, thereby extending the UGV mission duration. We validate the effectiveness of MAD-FELLOWS through an extensive numerical evaluation, comparing its performance with various baselines, including a centralized, oracle-based approach. Our results demonstrate that: i) MAD-FELLOWS surpasses the baselines, achieving rapid convergence to the performance of the centralized approach in a fully-distributed manner, aligning with latency and energy efficiency criteria; ii) MAD-FELLOWS enhances UGV mission duration by up to 57%, outperforming the baseline approaches
2024
Edge Computing; Job Offloading; Multi-armed Bandit; Robotic Ad-Hoc Networks
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/640754
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact