{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T23:56:41Z","timestamp":1773964601310,"version":"3.50.1"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2021,11,30]]},"abstract":"<jats:p>\n            Volunteer computing is an Internet-based distributed computing in which volunteers share their extra available resources to manage large-scale tasks. However, computing devices in a\n            <jats:bold>Volunteer Computing System (VCS)<\/jats:bold>\n            are highly dynamic and heterogeneous in terms of their processing power, monetary cost, and data transferring latency. To ensure both of the high\n            <jats:bold>Quality of Service (QoS)<\/jats:bold>\n            and low cost for different requests, all of the available computing resources must be used efficiently. Task scheduling is an NP-hard problem that is considered as one of the main critical challenges in a heterogeneous VCS. Due to this, in this article, we design two task scheduling algorithms for VCSs, named\n            <jats:italic>Min-CCV<\/jats:italic>\n            and\n            <jats:italic>Min-V<\/jats:italic>\n            . The main goal of the proposed algorithms is jointly minimizing the computation, communication, and delay violation cost for the\n            <jats:bold>Internet of Things (IoT)<\/jats:bold>\n            requests. Our extensive simulation results show that proposed algorithms are able to allocate tasks to volunteer fog\/cloud resources more efficiently than the state-of-the-art. Specifically, our algorithms improve the deadline satisfaction task rates around 99.5% and decrease the total cost between 15 to 53% in comparison with the genetic-based algorithm.\n          <\/jats:p>","DOI":"10.1145\/3418501","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T15:01:57Z","timestamp":1626447717000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":67,"title":["Joint QoS-aware and Cost-efficient Task Scheduling for Fog-cloud Resources in a Volunteer Computing System"],"prefix":"10.1145","volume":"21","author":[{"given":"Farooq","family":"Hoseiny","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and IT, University of Kurdistan, Sanandaj, Iran"}]},{"given":"Sadoon","family":"Azizi","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and IT, University of Kurdistan, Sanandaj, Iran"}]},{"given":"Mohammad","family":"Shojafar","sequence":"additional","affiliation":[{"name":"6GIC\/5GIC, University of Surrey, Guildford, United Kingdom"}]},{"given":"Rahim","family":"Tafazolli","sequence":"additional","affiliation":[{"name":"6GIC\/5GIC, University of Surrey, Guildford, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2021,7,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.09.039"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/E-SCIENCE.2005.51"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2019.2944360"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2012.090512.00043"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC.2019.8766437"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/17517575.2017.1304579"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1600885"},{"key":"e_1_2_1_8_1","volume-title":"IEEE International Conference on Communications (ICC\u201913)","author":"Xu Yang","unstructured":"Kuan-yin Chen, Yang Xu , Kang Xi , and H. Jonathan Chao . 2013. Intelligent virtual machine placement for cost efficiency in geo-distributed cloud systems . In IEEE International Conference on Communications (ICC\u201913) . IEEE, 3498\u20133503. Kuan-yin Chen, Yang Xu, Kang Xi, and H. Jonathan Chao. 2013. Intelligent virtual machine placement for cost efficiency in geo-distributed cloud systems. In IEEE International Conference on Communications (ICC\u201913). IEEE, 3498\u20133503."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3190645.3190699"},{"key":"e_1_2_1_10_1","first-page":"1171","article-title":"Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption","volume":"3","author":"Deng Ruilong","year":"2016","unstructured":"Ruilong Deng , Rongxing Lu , Chengzhe Lai , Tom H. Luan , and Hao Liang . 2016 . Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption . IEEE Internet Things J. 3 , 6 (2016), 1171 \u2013 1181 . Ruilong Deng, Rongxing Lu, Chengzhe Lai, Tom H. Luan, and Hao Liang. 2016. Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet Things J. 3, 6 (2016), 1171\u20131181.","journal-title":"IEEE Internet Things J."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2014.11.007"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/ett.3770"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2019.04.058"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-com.2020.0007"},{"key":"e_1_2_1_15_1","unstructured":"Farooq Hoseiny Sadoon Azizi Mohammad Shojafar and Rahim Tafazolli. 2020. Min-CCV Min-V Source Code. Retrieved from https:\/\/github.com\/mshojafar\/sourcecodes\/blob\/master\/Farooq2020MinvMinccv-ACMTOIT.zip.  Farooq Hoseiny Sadoon Azizi Mohammad Shojafar and Rahim Tafazolli. 2020. Min-CCV Min-V Source Code. Retrieved from https:\/\/github.com\/mshojafar\/sourcecodes\/blob\/master\/Farooq2020MinvMinccv-ACMTOIT.zip."},{"key":"e_1_2_1_16_1","volume-title":"Seyed Reza Kamel Tabbakh, and Reza Ghaemi","author":"Hosseinioun Pejman","year":"2020","unstructured":"Pejman Hosseinioun , Maryam Kheirabadi , Seyed Reza Kamel Tabbakh, and Reza Ghaemi . 2020 . A task scheduling approaches in fog computing: A survey. Trans. Emerg. Telecommun. Technol. Article e3792 (2020). Pejman Hosseinioun, Maryam Kheirabadi, Seyed Reza Kamel Tabbakh, and Reza Ghaemi. 2020. A task scheduling approaches in fog computing: A survey. Trans. Emerg. Telecommun. Technol. Article e3792 (2020)."},{"key":"e_1_2_1_17_1","volume-title":"FPFTS: A joint fuzzy PSO mobility-aware approach to fog task scheduling algorithm for IoT devices. Softw.: Pract. Exper. (to be appear) Article s2867","author":"Javanmardi Saeed","year":"2020","unstructured":"Saeed Javanmardi , Mohammad Shojafar , Valerio Prisco , and Antonio Pescape . 2020 . FPFTS: A joint fuzzy PSO mobility-aware approach to fog task scheduling algorithm for IoT devices. Softw.: Pract. Exper. (to be appear) Article s2867 (2020). Saeed Javanmardi, Mohammad Shojafar, Valerio Prisco, and Antonio Pescape. 2020. FPFTS: A joint fuzzy PSO mobility-aware approach to fog task scheduling algorithm for IoT devices. Softw.: Pract. Exper. (to be appear) Article s2867 (2020)."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2018.2880874"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-009-0326-1"},{"key":"e_1_2_1_20_1","volume-title":"A task scheduling algorithm based on classification mining in fog computing environment. Wirel. Commun. Mob. Comput","author":"Liu Lindong","year":"2018","unstructured":"Lindong Liu , Deyu Qi , Naqin Zhou , and Yilin Wu. 2018. A task scheduling algorithm based on classification mining in fog computing environment. Wirel. Commun. Mob. Comput . 2018 , Article 2102348 (2018), 11 pages. https:\/\/doi.org\/10.1155\/2018\/2102348 10.1155\/2018 Lindong Liu, Deyu Qi, Naqin Zhou, and Yilin Wu. 2018. A task scheduling algorithm based on classification mining in fog computing environment. Wirel. Commun. Mob. Comput. 2018, Article 2102348 (2018), 11 pages. https:\/\/doi.org\/10.1155\/2018\/2102348"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2884720"},{"key":"e_1_2_1_22_1","volume-title":"Fog computing: Focusing on mobile users at the edge. arXiv preprint arXiv:1502.01815","author":"Luan Tom H.","year":"2015","unstructured":"Tom H. Luan , Longxiang Gao , Zhi Li , Yang Xiang , Guiyi Wei , and Limin Sun . 2015. Fog computing: Focusing on mobile users at the edge. arXiv preprint arXiv:1502.01815 ( 2015 ). Tom H. Luan, Longxiang Gao, Zhi Li, Yang Xiang, Guiyi Wei, and Limin Sun. 2015. 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