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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\u2010powered computing resources, typically found in Dew and Edge computing environments. This paper proposes new algorithms aware of devices\u2019 power source for scheduling tasks in hybrid environments, i.e., where the battery\u2010 and non\u2010battery\u2010powered devices cooperate. We simulated hybrid Dew\/Edge environments by extending DewSim, a simulator that models battery\u2010driven devices\u2019 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\u2010dependent devices called Enhanced Simple Energy\u2010Aware Schedule (E\u2010SEAS). 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\u2010SEAS. 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.<\/jats:p>","DOI":"10.1155\/2021\/8899660","type":"journal-article","created":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T18:35:09Z","timestamp":1614882909000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["New Heuristics for Scheduling and Distributing Jobs under Hybrid Dew Computing Environments"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6493-3895","authenticated-orcid":false,"given":"Pablo","family":"Sanabria","sequence":"first","affiliation":[]},{"given":"Tom\u00e1s Felipe","family":"Tapia","sequence":"additional","affiliation":[]},{"given":"Andres","family":"Neyem","sequence":"additional","affiliation":[]},{"given":"Jose Ignacio","family":"Benedetto","sequence":"additional","affiliation":[]},{"given":"Mat\u00edas","family":"Hirsch","sequence":"additional","affiliation":[]},{"given":"Cristian","family":"Mateos","sequence":"additional","affiliation":[]},{"given":"Alejandro","family":"Zunino","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"e_1_2_10_1_2","unstructured":"Cisco Cisco Visual Networking Index: global mobile data traffic forecast update 2017\u20132022 white paper - Cisco 2019.https:\/\/www.cisco.com\/c\/en\/us\/solutions\/collateral\/service-provider\/visual-networking-index-vni\/white-paper-c11-738429.html (accessed Mar. 04 2019)."},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/10438599.2019.1628509"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2696"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/info10030086"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.09.056"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/en13215706"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2013.062613.00160"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-014-2102-7"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2010.98"},{"key":"e_1_2_10_10_2","unstructured":"TsipisA. 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