TY - GEN
T1 - On Computation Offloading and Energy Efficiency on Android Devices
AU - Carvalho, Goncalo
AU - Velasquez, Karima
AU - Fernandes, Joao Paulo
AU - Cabral, Bruno
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - As resource-demanding mobile applications become increasingly popular, smartphones, which are resource- and battery-dependent by nature, can migrate their workload to other devices on the Cloud-to-Edge continuum. This is known as computation offloading and allows for the 'heavy' processing to be carried out on another, typically more powerful equipment that prompts the result back to the smartphone. The goal is to improve overall performance, reduce energy consumption, and/or prolong the smartphone's battery life. However, the offloading process might have practical implications, such as performance degradation by increasing the latency of the response time or even increasing the energy consumption of the device if the application requires heavy data transfer. Also, one should consider that the chips in today's smartphones are extremely energy efficient and offer outstanding performance. 5G networks also increase the data transfer bandwidth between devices. We aim to shed light on the circumstances under which computation offloading is a robust architectural solution for mobile apps. We used the EdgeBench benchmark in our experimental evaluation, namely the audio, image, and scalar applications, over three smartphones. The results highlight different performance and energy consumption depending on the type of device and manufacturer, considering the same application, showing that the offloading decision is not linear, thus it is not always the best solution to minimize battery consumption.
AB - As resource-demanding mobile applications become increasingly popular, smartphones, which are resource- and battery-dependent by nature, can migrate their workload to other devices on the Cloud-to-Edge continuum. This is known as computation offloading and allows for the 'heavy' processing to be carried out on another, typically more powerful equipment that prompts the result back to the smartphone. The goal is to improve overall performance, reduce energy consumption, and/or prolong the smartphone's battery life. However, the offloading process might have practical implications, such as performance degradation by increasing the latency of the response time or even increasing the energy consumption of the device if the application requires heavy data transfer. Also, one should consider that the chips in today's smartphones are extremely energy efficient and offer outstanding performance. 5G networks also increase the data transfer bandwidth between devices. We aim to shed light on the circumstances under which computation offloading is a robust architectural solution for mobile apps. We used the EdgeBench benchmark in our experimental evaluation, namely the audio, image, and scalar applications, over three smartphones. The results highlight different performance and energy consumption depending on the type of device and manufacturer, considering the same application, showing that the offloading decision is not linear, thus it is not always the best solution to minimize battery consumption.
KW - Computation Offloading
KW - EdgeBench
KW - Energy consumption
UR - http://www.scopus.com/inward/record.url?scp=85177886129&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85177886129&partnerID=8YFLogxK
U2 - 10.1109/ICCWorkshops57953.2023.10283704
DO - 10.1109/ICCWorkshops57953.2023.10283704
M3 - Conference contribution
AN - SCOPUS:85177886129
T3 - 2023 IEEE International Conference on Communications Workshops: Sustainable Communications for Renaissance, ICC Workshops 2023
SP - 1836
EP - 1841
BT - 2023 IEEE International Conference on Communications Workshops
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Communications Workshops, ICC Workshops 2023
Y2 - 28 May 2023 through 1 June 2023
ER -