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6 results on '"JOB STEALING"'

1. A Two-Phase Energy-Aware Scheduling Approach for CPU-Intensive Jobs in Mobile Grids.

Author

Hirsch, Matías, Rodríguez, Juan, Mateos, Cristian, and Zunino, Alejandro

Abstract

The profusion of mobile devices over the world and their evolved computational capabilities promote their inclusion as resource providers in traditional Grid environments. However, their efficient exploitation requires adapting current schedulers to operate with computing capabilities limited by energy supply and mobile devices that cannot be assumed to be dedicated, among other concerns. We propose a two-phase scheduling approach for running CPU-intensive jobs on mobile devices that combines novel energy-aware criteria with job stealing techniques. The approach was evaluated through an event-based simulator that uses battery consumption profiles extracted from real mobile devices. CPU usage derived from non-Grid processes was also modelled. For evaluating the first phase we compared the number of finalized jobs by all energy-aware criteria, while for the second phase we analyzed the performance boost introduced by job stealing. While the best first phase criteria finalized up to 90 % of submitted jobs, job stealing increased this percentage by up to 9 %. [ABSTRACT FROM AUTHOR]

Published
2017
Full Text
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2. Battery-aware centralized schedulers for CPU-bound jobs in mobile Grids.

Author

Hirsch, Matías, Rodriguez, Juan Manuel, Zunino, Alejandro, and Mateos, Cristian

Subjects
GRID computing, MOBILE apps, COMPUTER systems, BEOWULF clusters (Computer systems), POWER resources
Abstract

Given the relentless growing number of mobile devices, researchers have pointed out that distributed computing environments, such as clusters or even computational Grids, could increase the available resources by scavenging devices’ capabilities. However, this negatively impacts on their batteries. We study centralized job schedulers that aim at exploiting clusters of mobile resources with rather stable devices in terms of connection time. These schedulers outperform a previous centralized scheduler, finishing up to 30% more jobs with the same amount of energy. We also show that the schedulers perform competitively compared to schedulers based on job stealing, which are more difficult to implement and consume more network resources and hence energy. [ABSTRACT FROM AUTHOR]

Published
2016
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3. Energy-efficient job stealing for CPU-intensive processing in mobile devices.

Author

Rodriguez, Juan, Mateos, Cristian, and Zunino, Alejandro

Subjects
*CENTRAL processing units, *PRODUCTION scheduling, *CELL phones, *GRID computing, *QUEUING theory, *ENERGY consumption
Abstract

Mobile devices have evolved from simple electronic agendas and mobile phones to small computers with great computational capabilities. In addition, there are more than 2 billion mobile devices around the world. Taking these facts into account, mobile devices are a potential source of computational resources for clusters and computational Grids. In this work, we present an analysis of different schedulers based on job stealing for mobile computational Grids. These job stealing techniques have been designed to consider energy consumption and battery status. As a result of this work, we present empirical evidence showing that energy-aware job stealing is more efficient than traditional random stealing in this context. In particular, our results show that mobile Grids using energy-aware job stealing might finish up to 11 % more jobs than when using random stealing, and up to 24 % more jobs than when not using any job stealing technique. This means that using energy-aware job stealing increases the energy efficiency of mobile computational Grids because it increases the number of jobs that can be executed using the same amount of energy. [ABSTRACT FROM AUTHOR]

Published
2014
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4. Battery-aware centralized schedulers for CPU-bound jobs in mobile Grids

Author

Matías Hirsch, Juan Manuel Rodriguez, Alejandro Zunino, and Cristian Mateos

Subjects
Job scheduler, Battery aware, Computer Networks and Communications, Computer science, Distributed computing, 02 engineering and technology, computer.software_genre, Mobile grid, 0202 electrical engineering, electronic engineering, information engineering, MOBILE GRID, CPU-bound, JOB STEALING, 020206 networking & telecommunications, MOBILE DEVICES, Connection time, Ciencias de la Computación, Computer Science Applications, Hardware and Architecture, SEAS, Ciencias de la Computación e Información, JOB SCHEDULING, 020201 artificial intelligence & image processing, Mobile device, computer, CIENCIAS NATURALES Y EXACTAS, Software, Information Systems
Abstract

Given the relentless growing number of mobile devices, researchers have pointed out that distributed computing environments, such as clusters or even computational Grids, could increase the available resources by scavenging devices’ capabilities. However, this negatively impacts on their batteries. We study centralized job schedulers that aim at exploiting clusters of mobile resources with rather stable devices in terms of connection time. These schedulers outperform a previous centralized scheduler, finishing up to 30% more jobs with the same amount of energy. We also show that the schedulers perform competitively compared to schedulers based on job stealing, which are more difficult to implement and consume more network resources and hence energy. Fil: Hirsch Jofré, Matías Eberardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Rodriguez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina

Published
2016

5. A Two-Phase Energy-Aware Scheduling Approach for CPU-Intensive Jobs in Mobile Grids

Author

Juan Manuel Rodriguez, Alejandro Zunino, Cristian Mateos, and Matías Hirsch

Subjects
Job scheduler, Computer Networks and Communications, Computer science, Distributed computing, CPU time, 02 engineering and technology, computer.software_genre, Mobile grid, Scheduling (computing), Energy aware scheduling, 0202 electrical engineering, electronic engineering, information engineering, Energy supply, CPU INTENSIVE APPLICATION, MOBILE GRID, JOB STEALING, 020206 networking & telecommunications, MOBILE DEVICES, Grid, Ciencias de la Computación, Hardware and Architecture, Ciencias de la Computación e Información, JOB SCHEDULING, 020201 artificial intelligence & image processing, Mobile device, computer, Software, CIENCIAS NATURALES Y EXACTAS, Information Systems
Abstract

The profusion of mobile devices over the world and their evolved computational capabilities promote their inclusion as resource providers in traditional Grid environments. However, their efficient exploitation requires adapting current schedulers to operate with computing capabilities limited by energy supply and mobile devices that cannot be assumed to be dedicated, among other concerns. We propose a two-phase scheduling approach for running CPU-intensive jobs on mobile devices that combines novel energy-aware criteria with job stealing techniques. The approach was evaluated through an event-based simulator that uses battery consumption profiles extracted from real mobile devices. CPU usage derived from non-Grid processes was also modelled. For evaluating the first phase we compared the number of finalized jobs by all energy-aware criteria, while for the second phase we analyzed the performance boost introduced by job stealing. While the best first phase criteria finalized up to 90 % of submitted jobs, job stealing increased this percentage by up to 9 %. Fil: Hirsch Jofré, Matías Eberardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Rodriguez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina

Published
2017

6. Energy-efficient job stealing for CPU-intensive processing in mobile devices

Author

Juan Manuel Rodriguez, Cristian Mateos, and Alejandro Zunino

Subjects
Job scheduler, Job stealing, Computer science, Distributed computing, computer.software_genre, Theoretical Computer Science, Scheduling (computing), Potential source, Empirical evidence, Mobile Grid, Numerical Analysis, Queueing theory, Job scheduling, Energy consumption, CPU intensive application, Ciencias de la Computación, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, Ciencias de la Computación e Información, Mobile devices, computer, Mobile device, CIENCIAS NATURALES Y EXACTAS, Software, Efficient energy use
Abstract

Mobile devices have evolved from simple electronic agendas and mobile phones to small computers with great computational capabilities. In addition, there are more than 2 billion mobile devices around the world. Taking these facts into account, mobile devices are a potential source of computational resources for clusters and computational Grids. In this work, we present an analysis of different schedulers based on job stealing for mobile computational Grids. These job stealing techniques have been designed to consider energy consumption and battery status. As a result of this work, we present empirical evidence showing that energy-aware job stealing is more efficient than traditional random stealing in this context. In particular, our results show that mobile Grids using energy-aware job stealing might finish up to 11% more jobs than when using random stealing, and up to 24% more jobs than when not using any job stealing technique. This means that using energy-aware job stealing increases the energy efficiency of mobile computational Grids because it increases the number of jobs that can be executed using the same amount of energy. Fil: Rodriguez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina

Published
2012

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