Heterogeneous Secure Multi-level Remote Acceleration Service for Low-power Integrated Systems and Devices

Lara López; Francisco Javier Nieto; Terpsichori-Helen Velivassaki; Sokol Kosta; Cheol-Ho Hong; Raffaele Montella; Iakovos Mavroidis; Carles Fernández

doi:10.1016/j.procs.2016.08.287

Heterogeneous Secure Multi-level Remote Acceleration Service for Low-power Integrated Systems and Devices

Lara López, Francisco Javier Nieto, Terpsichori-Helen Velivassaki, Sokol Kosta, Cheol-Ho Hong, Raffaele Montella, Iakovos Mavroidis, Carles Fernández

Department of Electronic Systems

Research output: Contribution to journal › Journal article › Research › peer-review

8 Citations (Scopus)

177 Downloads (Pure)

Abstract

Abstract 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.

Original language	English
Journal	Procedia Computer Science
Volume	97
Pages (from-to)	118 - 121
Number of pages	4
ISSN	1877-0509
DOIs	https://doi.org/10.1016/j.procs.2016.08.287
Publication status	Published - Oct 2016

Bibliographical note

2nd International Conference on Cloud Forward: From Distributed to Complete Computing

Keywords

Cloud computing

Access to Document

10.1016/j.procs.2016.08.287

Open access articleFinal published version, 131 KBLicence: CC BY-NC-ND 4.0

http://www.sciencedirect.com/science/article/pii/S1877050916321032

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Best Paper Award, Procedia Computer Science, 2016
López, Lara (Recipient), Nieto, Francisco Javier (Recipient), Kosta, Sokol (Recipient), Velivassaki, Terpsichori-Helen (Recipient), Hong, Cheol-Ho (Recipient) & Montella, Raffaele (Recipient), Oct 2016
Prize: Conference prizes

Cite this

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title = "Heterogeneous Secure Multi-level Remote Acceleration Service for Low-power Integrated Systems and Devices",

abstract = "Abstract 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.",

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note = "2nd International Conference on Cloud Forward: From Distributed to Complete Computing",

year = "2016",

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doi = "10.1016/j.procs.2016.08.287",

language = "English",

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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

N1 - 2nd International Conference on Cloud Forward: From Distributed to Complete Computing

PY - 2016/10

Y1 - 2016/10

N2 - Abstract 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 - Abstract 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.

KW - Cloud computing

U2 - 10.1016/j.procs.2016.08.287

DO - 10.1016/j.procs.2016.08.287

M3 - Journal article

SN - 1877-0509

VL - 97

SP - 118

EP - 121

JO - Procedia Computer Science

JF - Procedia Computer Science

ER -

Heterogeneous Secure Multi-level Remote Acceleration Service for Low-power Integrated Systems and Devices

Abstract

Bibliographical note

Keywords

Access to Document

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Prizes

Best Paper Award, Procedia Computer Science, 2016

Cite this