TY - GEN
T1 - Reinforcing the Edge
T2 - 2019 INFOCOM IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
AU - Balasubramanian, Venkatraman
AU - Zaman, Faisal
AU - Aloqaily, Moayad
AU - Alrabaee, Saed
AU - Gorlatova, Maria
AU - Reisslein, Martin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - The collaboration among mobile devices to form an edge cloud for sharing computation and data can drastically reduce the tasks that need to be transmitted to the cloud. Moreover, reinforcement learning (RL) research has recently begun to intersect with edge computing to reduce the amount of data (and tasks) that needs to be transmitted over the network. For battery-powered Internet of Things (IoT) devices, the energy consumption in collaborating edge devices emerges as an important problem. To address this problem, we propose an RL-based Droplet framework for autonomous energy management. Droplet learns the power-related statistics of the devices and forms a reliable group of resources for providing a computation environment on-the-fly. We compare the energy reductions achieved by two different state-of-the-art RL algorithms. Further, we model a reward strategy for edge devices that participate in the mobile device cloud service. The proposed strategy effectively achieves a 10% gain in the rewards earned compared to state-of-the-art strategies.
AB - The collaboration among mobile devices to form an edge cloud for sharing computation and data can drastically reduce the tasks that need to be transmitted to the cloud. Moreover, reinforcement learning (RL) research has recently begun to intersect with edge computing to reduce the amount of data (and tasks) that needs to be transmitted over the network. For battery-powered Internet of Things (IoT) devices, the energy consumption in collaborating edge devices emerges as an important problem. To address this problem, we propose an RL-based Droplet framework for autonomous energy management. Droplet learns the power-related statistics of the devices and forms a reliable group of resources for providing a computation environment on-the-fly. We compare the energy reductions achieved by two different state-of-the-art RL algorithms. Further, we model a reward strategy for edge devices that participate in the mobile device cloud service. The proposed strategy effectively achieves a 10% gain in the rewards earned compared to state-of-the-art strategies.
KW - Device Clouds
KW - Internet of Things
KW - Mobile Edge Computing
KW - Reinforcement Learning
UR - http://www.scopus.com/inward/record.url?scp=85073253580&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073253580&partnerID=8YFLogxK
U2 - 10.1109/INFCOMW.2019.8845263
DO - 10.1109/INFCOMW.2019.8845263
M3 - Conference contribution
AN - SCOPUS:85073253580
T3 - INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
SP - 44
EP - 49
BT - INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 April 2019 through 2 May 2019
ER -