New Heuristics for Scheduling and Distributing Jobs under Hybrid Dew Computing Environments. (4th March 2021)

• Record Type: Journal Article
• Authors: Sanabria, Pablo; Tapia, Tomás Felipe; Neyem, Andres; Benedetto, Jose Ignacio; Hirsch, Matías; Mateos, Cristian; Zunino, Alejandro
• Other Names: Yadav Rahul Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2021(2021)
• Publication Date: 2021-03-04
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2021/8899660
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : 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.