TY - GEN
T1 - Composable Energy Modeling for ML-Driven Drone Applications
AU - Trihinas, Demetris
AU - Agathocleous, Michalis
AU - Avogian, Karlen
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - We are now witnessing the extensive deployment of drones in a diverse set of applications with Machine Learning (ML) constituting a key enabler empowering the uptake of drone technology. With the advancements of robotics and edge computing, on-board ML is on the uprise. However, testing ML solutions for drones before release to production is a daunting task for ML practitioners. This usually involves the testing on a robotics emulator to collect various key performance indicators ranging from algorithm correctness to resource utilization. Thus, to thoroughly evaluate performance, a true understanding of the ML algorithm impact on the drones most scarce resource is required. Without a doubt, this is the drones battery, which entails continuously monitoring energy consumption. In this paper we introduce HornEt, a modular framework enabling the customization and composition of various monitorable components to produce realistic energy models that can be used during the testing of ML-driven drone applications. To show the wide applicability of our framework, we introduce a proof-of-concept use-case illustrating the energy profiling of a drone application at different levels of granularity.
AB - We are now witnessing the extensive deployment of drones in a diverse set of applications with Machine Learning (ML) constituting a key enabler empowering the uptake of drone technology. With the advancements of robotics and edge computing, on-board ML is on the uprise. However, testing ML solutions for drones before release to production is a daunting task for ML practitioners. This usually involves the testing on a robotics emulator to collect various key performance indicators ranging from algorithm correctness to resource utilization. Thus, to thoroughly evaluate performance, a true understanding of the ML algorithm impact on the drones most scarce resource is required. Without a doubt, this is the drones battery, which entails continuously monitoring energy consumption. In this paper we introduce HornEt, a modular framework enabling the customization and composition of various monitorable components to produce realistic energy models that can be used during the testing of ML-driven drone applications. To show the wide applicability of our framework, we introduce a proof-of-concept use-case illustrating the energy profiling of a drone application at different levels of granularity.
KW - Drones
KW - Energy Modeling
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85121592634&partnerID=8YFLogxK
U2 - 10.1109/IC2E52221.2021.00039
DO - 10.1109/IC2E52221.2021.00039
M3 - Conference contribution
AN - SCOPUS:85121592634
T3 - Proceedings - 2021 IEEE International Conference on Cloud Engineering, IC2E 2021
SP - 231
EP - 237
BT - Proceedings - 2021 IEEE International Conference on Cloud Engineering, IC2E 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Cloud Engineering, IC2E 2021
Y2 - 4 October 2021 through 8 October 2021
ER -