TY - GEN
T1 - EDOS
T2 - 26th International Conference on Computer Communications and Networks, ICCCN 2017
AU - Harvey, Hank H.
AU - Mao, Ying
AU - Hou, Yantian
AU - Sheng, Bo
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/14
Y1 - 2017/9/14
N2 - Offloading resource-intensive jobs to the cloud and nearby users is a promising approach to enhance mobile devices. This paper investigates a hybrid offloading system that takes both infrastructure-based networks and Ad-hoc networks into the scope. Specifically, we propose EDOS, an edge assisted offloading system that consists of two major components, an Edge Assistant (EA) and Offload Agent (OA). EA runs on the routers/towers to manage registered remote cloud servers and local service providers and OA operates on the users' devices to discover the services in proximity. We present the system with a suite of protocols to collect the potential service providers and algorithms to allocate tasks according to user-specified constraints. To evaluate EDOS, we prototype it on commercial mobile devices and evaluate it with both experiments on a small-scale testbed and simulations. The results show that EDOS is effective and efficient for offloading jobs.
AB - Offloading resource-intensive jobs to the cloud and nearby users is a promising approach to enhance mobile devices. This paper investigates a hybrid offloading system that takes both infrastructure-based networks and Ad-hoc networks into the scope. Specifically, we propose EDOS, an edge assisted offloading system that consists of two major components, an Edge Assistant (EA) and Offload Agent (OA). EA runs on the routers/towers to manage registered remote cloud servers and local service providers and OA operates on the users' devices to discover the services in proximity. We present the system with a suite of protocols to collect the potential service providers and algorithms to allocate tasks according to user-specified constraints. To evaluate EDOS, we prototype it on commercial mobile devices and evaluate it with both experiments on a small-scale testbed and simulations. The results show that EDOS is effective and efficient for offloading jobs.
UR - https://www.scopus.com/pages/publications/85032287226
UR - https://www.scopus.com/pages/publications/85032287226#tab=citedBy
U2 - 10.1109/ICCCN.2017.8038421
DO - 10.1109/ICCCN.2017.8038421
M3 - Conference contribution
AN - SCOPUS:85032287226
T3 - 2017 26th International Conference on Computer Communications and Networks, ICCCN 2017
BT - 2017 26th International Conference on Computer Communications and Networks, ICCCN 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 31 July 2017 through 3 August 2017
ER -