Scheduling Strategies for Mobile Devices in BOINC

Mariela Josefina Curiel Huérfano; Arturo Garcia

doi:10.5281/zenodo.4445243

Scheduling Strategies for Mobile Devices in BOINC

Mariela Josefina Curiel Huérfano
, Arturo Garcia

Department of Systems Engineering

Universidad Javeriana

Research output: Contribution to journal › Article › peer-review

Abstract

Executing tasks on mobile devices brings some challenges that
must be addressed, such as device heterogeneity, limited CPU power,
limited memory, short battery life, mobility, and intermittent disconnections.
BOINC (Berkeley Open Infrastructure for Network Computing), the
platform chosen in our research project that aims to process medical
images using mobile phones, does not completely solve these challenges. In
this sense, this article's objective is to present the modifications to BOINC's
scheduling strategy to consider the characteristics of mobile devices and
possible disconnections. The article describes BOINC-MGE (BOINC -
Mobile Grid Extension), a BOINC extension that incorporates two new task
scheduling algorithms to ensure the completion of tasks while efficiently
using the mobile devices' energy. The strategies are tested using an image
segmentation algorithm.

Original language	English
Journal	Revista Eletr̂onica Argentina-Brasil de Tecnologias da Informação e da Comunicação
Volume	1
Issue number	13
DOIs	https://doi.org/10.5281/zenodo.4445243
State	Published - 16 Jan 2021

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title = "Scheduling Strategies for Mobile Devices in BOINC",

abstract = "Executing tasks on mobile devices brings some challenges that must be addressed, such as device heterogeneity, limited CPU power, limited memory, short battery life, mobility, and intermittent disconnections. BOINC (Berkeley Open Infrastructure for Network Computing), the platform chosen in our research project that aims to process medical images using mobile phones, does not completely solve these challenges. In this sense, this article's objective is to present the modifications to BOINC's scheduling strategy to consider the characteristics of mobile devices and possible disconnections. The article describes BOINC-MGE (BOINC - Mobile Grid Extension), a BOINC extension that incorporates two new task scheduling algorithms to ensure the completion of tasks while efficiently using the mobile devices' energy. The strategies are tested using an image segmentation algorithm.",

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N2 - Executing tasks on mobile devices brings some challenges that must be addressed, such as device heterogeneity, limited CPU power, limited memory, short battery life, mobility, and intermittent disconnections. BOINC (Berkeley Open Infrastructure for Network Computing), the platform chosen in our research project that aims to process medical images using mobile phones, does not completely solve these challenges. In this sense, this article's objective is to present the modifications to BOINC's scheduling strategy to consider the characteristics of mobile devices and possible disconnections. The article describes BOINC-MGE (BOINC - Mobile Grid Extension), a BOINC extension that incorporates two new task scheduling algorithms to ensure the completion of tasks while efficiently using the mobile devices' energy. The strategies are tested using an image segmentation algorithm.

AB - Executing tasks on mobile devices brings some challenges that must be addressed, such as device heterogeneity, limited CPU power, limited memory, short battery life, mobility, and intermittent disconnections. BOINC (Berkeley Open Infrastructure for Network Computing), the platform chosen in our research project that aims to process medical images using mobile phones, does not completely solve these challenges. In this sense, this article's objective is to present the modifications to BOINC's scheduling strategy to consider the characteristics of mobile devices and possible disconnections. The article describes BOINC-MGE (BOINC - Mobile Grid Extension), a BOINC extension that incorporates two new task scheduling algorithms to ensure the completion of tasks while efficiently using the mobile devices' energy. The strategies are tested using an image segmentation algorithm.

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Scheduling Strategies for Mobile Devices in BOINC

Abstract

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