New Heuristics for Scheduling and Distributing Jobs under Hybrid Dew Computing Environments
Mobile grid computing has been a popular topic for researchers due to mobile and IoT devices’ ubiquity and their evergrowing processing potential. While many scheduling algorithms for harnessing these resources exist in the literature for standard grid computing scenarios, surprisingly, there is lit...
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Series: | Wireless Communications and Mobile Computing |
Online Access: | http://dx.doi.org/10.1155/2021/8899660 |
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doaj-509d05b32035418795c50a8feee7c2ee2021-03-15T00:00:29ZengHindawi-WileyWireless Communications and Mobile Computing1530-86772021-01-01202110.1155/2021/8899660New Heuristics for Scheduling and Distributing Jobs under Hybrid Dew Computing EnvironmentsPablo Sanabria0Tomás Felipe Tapia1Andres Neyem2Jose Ignacio Benedetto3Matías Hirsch4Cristian Mateos5Alejandro Zunino6Department of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceISISTAN Research InstituteISISTAN Research InstituteISISTAN Research InstituteMobile grid computing has been a popular topic for researchers due to mobile and IoT devices’ ubiquity and their evergrowing processing potential. While many scheduling algorithms for harnessing these resources exist in the literature for standard grid computing scenarios, surprisingly, there is little insight into this matter in the context of hybrid-powered computing resources, typically found in Dew and Edge computing environments. This paper proposes new algorithms aware of devices’ power source for scheduling tasks in hybrid environments, i.e., where the battery- and non-battery-powered devices cooperate. We simulated hybrid Dew/Edge environments by extending DewSim, a simulator that models battery-driven devices’ battery behavior using battery traces profiled from real mobile devices. We compared the throughput and job completion achieved by algorithms proposed in this paper using as a baseline a previously developed algorithm that considers computing resources but only from battery-dependent devices called Enhanced Simple Energy-Aware Schedule (E-SEAS). The obtained results in the simulation reveal that our proposed algorithms can obtain up to a 90% increment in overall throughput and around 95% of completed jobs in hybrid environments compared to E-SEAS. Finally, we show that incorporating these characteristics gives more awareness of the type of resources present and can enable the algorithms to manage resources more efficiently in more hybrid environments than other algorithms found in the literature.http://dx.doi.org/10.1155/2021/8899660 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pablo Sanabria Tomás Felipe Tapia Andres Neyem Jose Ignacio Benedetto Matías Hirsch Cristian Mateos Alejandro Zunino |
spellingShingle |
Pablo Sanabria Tomás Felipe Tapia Andres Neyem Jose Ignacio Benedetto Matías Hirsch Cristian Mateos Alejandro Zunino New Heuristics for Scheduling and Distributing Jobs under Hybrid Dew Computing Environments Wireless Communications and Mobile Computing |
author_facet |
Pablo Sanabria Tomás Felipe Tapia Andres Neyem Jose Ignacio Benedetto Matías Hirsch Cristian Mateos Alejandro Zunino |
author_sort |
Pablo Sanabria |
title |
New Heuristics for Scheduling and Distributing Jobs under Hybrid Dew Computing Environments |
title_short |
New Heuristics for Scheduling and Distributing Jobs under Hybrid Dew Computing Environments |
title_full |
New Heuristics for Scheduling and Distributing Jobs under Hybrid Dew Computing Environments |
title_fullStr |
New Heuristics for Scheduling and Distributing Jobs under Hybrid Dew Computing Environments |
title_full_unstemmed |
New Heuristics for Scheduling and Distributing Jobs under Hybrid Dew Computing Environments |
title_sort |
new heuristics for scheduling and distributing jobs under hybrid dew computing environments |
publisher |
Hindawi-Wiley |
series |
Wireless Communications and Mobile Computing |
issn |
1530-8677 |
publishDate |
2021-01-01 |
description |
Mobile grid computing has been a popular topic for researchers due to mobile and IoT devices’ ubiquity and their evergrowing processing potential. While many scheduling algorithms for harnessing these resources exist in the literature for standard grid computing scenarios, surprisingly, there is little insight into this matter in the context of hybrid-powered computing resources, typically found in Dew and Edge computing environments. This paper proposes new algorithms aware of devices’ power source for scheduling tasks in hybrid environments, i.e., where the battery- and non-battery-powered devices cooperate. We simulated hybrid Dew/Edge environments by extending DewSim, a simulator that models battery-driven devices’ battery behavior using battery traces profiled from real mobile devices. We compared the throughput and job completion achieved by algorithms proposed in this paper using as a baseline a previously developed algorithm that considers computing resources but only from battery-dependent devices called Enhanced Simple Energy-Aware Schedule (E-SEAS). The obtained results in the simulation reveal that our proposed algorithms can obtain up to a 90% increment in overall throughput and around 95% of completed jobs in hybrid environments compared to E-SEAS. Finally, we show that incorporating these characteristics gives more awareness of the type of resources present and can enable the algorithms to manage resources more efficiently in more hybrid environments than other algorithms found in the literature. |
url |
http://dx.doi.org/10.1155/2021/8899660 |
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