TY - JOUR
T1 - Heterogeneous Secure Multi-level Remote Acceleration Service for Low-Power Integrated Systems and Devices
AU - López, Lara
AU - Nieto, Francisco Javier
AU - Velivassaki, Terpsichori-Helen
AU - Kosta, Sokol
AU - Hong, Cheol-Ho
AU - Montella, Raffaele
AU - Mavroidis, Iakovos
AU - Fernández, Carles
PY - 2016/10/17
Y1 - 2016/10/17
N2 - This position paper presents a novel heterogeneous CPU-GPU multi-level cloud acceleration focusing on applications running on embedded systems found on low-power devices. A runtime system performs energy and performance estimations in order to automatically select local CPU-based and GPU-based tasks that should be seamlessly executed on more powerful remote devices or cloud infrastructures. Moreover, it proposes, for the first time, a secure unified model where almost any device or infrastructure can operate as an accelerated entity and/or as an accelerator serving other less powerful devices in a secure way.
AB - This position paper presents a novel heterogeneous CPU-GPU multi-level cloud acceleration focusing on applications running on embedded systems found on low-power devices. A runtime system performs energy and performance estimations in order to automatically select local CPU-based and GPU-based tasks that should be seamlessly executed on more powerful remote devices or cloud infrastructures. Moreover, it proposes, for the first time, a secure unified model where almost any device or infrastructure can operate as an accelerated entity and/or as an accelerator serving other less powerful devices in a secure way.
U2 - 10.1016/j.procs.2016.08.287
DO - 10.1016/j.procs.2016.08.287
M3 - Article
VL - 97
SP - 118
EP - 121
JO - Procedia Computer Science
JF - Procedia Computer Science
SN - 1877-0509
T2 - Cloud Futures: From Distributed to Complete Computing, CF2016, 18-20 October 2016, Madrid, Spain
Y2 - 18 October 2016 through 20 February 2017
ER -