{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T07:26:47Z","timestamp":1782631607800,"version":"3.54.5"},"reference-count":143,"publisher":"Association for Computing Machinery (ACM)","issue":"12","license":[{"start":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T00:00:00Z","timestamp":1677715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2020R1A2C1012196"],"award-info":[{"award-number":["2020R1A2C1012196"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"name":"School of Computer Science and Engineering, Ministry of Education, Kyungpook National University, South Korea, through the BK21 Four Project, AI-Driven Convergence Software Education Research Program","award":["4199990214394"],"award-info":[{"award-number":["4199990214394"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2023,12,31]]},"abstract":"<jats:p>The trend of adopting Internet of Things (IoT) in healthcare, smart cities, Industry 4.0, and so on is increasing by means of cloud computing, which provides on-demand storage and computation facilities over the Internet. To meet specific requirements of IoT applications, the cloud has also shifted its service offering platform to its next-generation models, such as fog, mist, and dew computing. As a result, the cloud and IoT have become part and parcel of smart applications that play significant roles in improving the quality of human life. In addition to the inherent advantages of advanced cloud models, to improve the performance of IoT applications further, it is essential to understand how the resources in the cloud and cloud-influenced platforms are managed to support various phases in the end-to-end IoT deployment. Considering this importance, in this article, we provide a brief description, a systematic review, and possible research directions on every aspect of resource management tasks, such as workload modeling, resource provisioning, workload scheduling, resource allocation, load balancing, energy management, and resource heterogeneity in such advanced platforms, from a cloud perspective. The primary objective of this article is to help early researchers gain insight into the underlying concepts of resource management tasks in the cloud for IoT applications.<\/jats:p>","DOI":"10.1145\/3571729","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T13:20:40Z","timestamp":1671196840000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":79,"title":["Resource Management in Cloud and Cloud-influenced Technologies for Internet of Things Applications"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0165-181X","authenticated-orcid":false,"given":"Rathinaraja","family":"Jeyaraj","sequence":"first","affiliation":[{"name":"Kyungpook National University, Daegu, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5457-2010","authenticated-orcid":false,"given":"Anandkumar","family":"Balasubramaniam","sequence":"additional","affiliation":[{"name":"Kyungpook National University, Daegu, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9088-6479","authenticated-orcid":false,"given":"Ajay Kumara","family":"M.A.","sequence":"additional","affiliation":[{"name":"Lenoir-Rhyne University, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5332-2685","authenticated-orcid":false,"given":"Nadra","family":"Guizani","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington, Texas, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0737-2021","authenticated-orcid":false,"given":"Anand","family":"Paul","sequence":"additional","affiliation":[{"name":"Kyungpook National University, Daegu, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,3,2]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2015.12.016"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2015.09.021"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2018.10.021"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbe2.133"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2619369"},{"key":"e_1_3_2_7_2","unstructured":"Azure IoT Hub. 2022. Retrieved 11 October 2022 from https:\/\/azure.microsoft.com\/en-us\/services\/iot-hub\/#overview."},{"key":"e_1_3_2_8_2","unstructured":"Amazon IoT. 2022. Retrieved 11 October 2022 from https:\/\/aws.amazon.com\/iot\/."},{"key":"e_1_3_2_9_2","unstructured":"Cisco IoT. 2022. Retrieved 11 October 2022 from https:\/\/www.cisco.com\/c\/en\/us\/solutions\/internet-of-things\/iot-control-center.html."},{"key":"e_1_3_2_10_2","unstructured":"IBM IoT. 2022. Retrieved 11 October 2022 from https:\/\/internetofthings.ibmcloud.com\/."},{"key":"e_1_3_2_11_2","unstructured":"Google IoT. 2022. Retrieved 11 October 2022 from https:\/\/cloud.google.com\/solutions\/iot."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2947542"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2876088"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3004500"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2021.09.003"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2775042"},{"key":"e_1_3_2_17_2","doi-asserted-by":"crossref","unstructured":"J. Ren D. Zhang S. He Y. Zhang and T. Li. 2020. A survey on end-edge-cloud orchestrated network computing paradigms: Transparent computing mobile edge computing fog computing and cloudlet. ACM Comput. Surv . 52 6 Article 125 (November 2020) 36.","DOI":"10.1145\/3362031"},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","unstructured":"D. R. Vasconcelos R. M. C. Andrade V. Severino and J. N. De Souza. 2019. Cloud fog or mist in IoT? That is the qestion. ACM Trans. Internet Technol . 19 2 Article 25 (May 2019) 20.","DOI":"10.1145\/3309709"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-018-2493-4"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2020.03.006"},{"key":"e_1_3_2_21_2","doi-asserted-by":"crossref","unstructured":"G. A. S. Cassel V. F. Rodrigues R. da Rosa Righi M. R. Bez A. C. Nepomuceno and C. Andr\u00e9 da Costa. 2022. Serverless computing for internet of things: A systematic literature review. Futur. Gener. Comput. Syst . 128 (2022) 299\u2013316. DOI:10.1016\/j.future.2021.10.020","DOI":"10.1016\/j.future.2021.10.020"},{"key":"e_1_3_2_22_2","doi-asserted-by":"crossref","unstructured":"B. Jennings and R. Stadler. 2015. Resource management in clouds: Survey and research challenges. J. Netw. Syst. Manag . 23 3 (2015) 567\u2013619. DOI:10.1007\/s10922-014-9307-7","DOI":"10.1007\/s10922-014-9307-7"},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Maggi Bansal Inderveer Chana and Siobh\u00e1n Clarke. 2021. A survey on IoT big data: Current status 13 V\u2019s Challenges and future directions. ACM Comput. Surv . 53 6 Article 131 (November 2021) 59.","DOI":"10.1145\/3419634"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.06.042"},{"key":"e_1_3_2_25_2","unstructured":"S. K. Lo Q. Lu C. Wang H. Y. Paik and L. Zhu. 2019. A systematic literature review on federated machine learning: From a sofware engineering perspective. ACM Comput. Surv . 54 5 Article 95 (May 2019) 39."},{"key":"e_1_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Jie Zhang Zhihao Qu Chenxi Chen Haozhao Wang Yufeng Zhan Baoliu Ye and Song Guo. 2022. Edge learning: The enabling technology for distributed big data analytics in the edge. ACM Comput. Surv . 54 7 Article 151 (September 2022) 36.","DOI":"10.1145\/3464419"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100303"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-018-2277-x"},{"key":"e_1_3_2_29_2","unstructured":"Hadoop. 2022. Retrieved 11 October 2022 from https:\/\/hadoop.apache.org\/."},{"key":"e_1_3_2_30_2","unstructured":"Spark. 2022. Retrieved 11 October 2022 from https:\/\/spark.apache.org\/."},{"key":"e_1_3_2_31_2","unstructured":"Storm. 2022. Retrieved 11 October 2022 from https:\/\/storm.apache.org\/."},{"key":"e_1_3_2_32_2","unstructured":"Kafka. 2022. Retrieved 11 October 2022 from https:\/\/kafka.apache.org\/."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-13705-2_20"},{"key":"e_1_3_2_34_2","doi-asserted-by":"crossref","unstructured":"Muhammad H. Hilman Maria A. Rodriguez and Rajkumar Buyya. 2021. Multiple workflows scheduling in multi-tenant distributed systems: A taxonomy and future directions. ACM Comput. Surv . 53 1 Article 10 (January 2021) 39.","DOI":"10.1145\/3368036"},{"key":"e_1_3_2_35_2","doi-asserted-by":"crossref","unstructured":"T. Ben-Nun and T. Hoefler. 2020. Demystifying parallel and distributed deep learning. ACM Comput. Surv . 52 4 (2020) 1\u201343. DOI:10.1145\/3320060","DOI":"10.1145\/3320060"},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","unstructured":"R. Kang A. Guo G. Laput Y. Li and X. A. Chen. 2019. Minuet: Multimodal interaction with an internet of things. Proc. SUI. ACM Conf. Spat. User Interact . Article 2 (2019) 1\u201310. DOI:10.1145\/3357251.3357581","DOI":"10.1145\/3357251.3357581"},{"key":"e_1_3_2_37_2","doi-asserted-by":"crossref","unstructured":"Redowan Mahmud Kotagiri Ramamohanarao and Rajkumar Buyya. 2021. Application management in fog computing environments: A taxonomy review and future directions. ACM Comput. Surv . 53 4 Article 88 (July 2021) 43.","DOI":"10.1145\/3403955"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3325097"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1201\/9781003001188-14"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2015.2453966"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.08.040"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/2856127"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3151847"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460197"},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","unstructured":"Y. Wen G. Cheng S. Deng and J. Yin. 2022. Characterizing and synthesizing the workflow structure of microservices in bytedance cloud. J. Softw. Evol. Process (2022) 1\u201318. DOI:10.1002\/smr.2467","DOI":"10.1002\/smr.2467"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMSCS.2017.2749228"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2017.2758781"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2013.187"},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","unstructured":"A. Mahgoub et\u00a0al. 2022. WiseFuse: Workload characterization and DAG transformation for serverless workflows. Proc. ACM Meas. Anal. Comput. Syst . 6 2 (2022) 1\u201328. DOI:10.1145\/3530892","DOI":"10.1145\/3530892"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcc.2014.2314661"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2016.2603476"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13677-020-00190-x"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2015.2398438"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2016.2560158"},{"key":"e_1_3_2_55_2","doi-asserted-by":"crossref","unstructured":"Hosein Mohamamdi Makrani Hossein Sayadi Najmeh Nazari Sai Mnoj Pudukotai Dinakarrao Avesta Sasan Tinoosh Mohsenin Setareh Rafatirad and Houman Homayoun. 2020. Adaptive performance modeling of data-intensive workloads for resource provisioning in virtualized environment. ACM Trans. Model. Perform. Eval. Comput. Syst . 5 4 Article 18 (December 2020) 24.","DOI":"10.1145\/3442696"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3081705"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2019.106136"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3029847"},{"key":"e_1_3_2_59_2","doi-asserted-by":"crossref","unstructured":"Cheol-Ho Hong and Blesson Varghese. 2020. Resource management in fog\/edge computing. ACM Comput. Surv . 52 5 Article 97 (September 2020) 37.","DOI":"10.1145\/3326066"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10723-019-09491-1"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2018.07.020"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3029583"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2019.02.021"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2016.2558203"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2016.10.008"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.05.087"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2999658"},{"key":"e_1_3_2_68_2","doi-asserted-by":"crossref","unstructured":"Giovanni Merlino Rustem Dautov Salvatore Distefano and Dario Bruneo. 2019. Enabling workload engineering in edge fog and cloud computing through openstack-based middleware. ACM Trans. Internet Technol . 19 2 Article 28 (May 2019) 22.","DOI":"10.1145\/3309705"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.04.037"},{"key":"e_1_3_2_70_2","unstructured":"Azure resource type. 2022. Retrieved 11 October 2022 from https:\/\/docs.microsoft.com\/en-us\/azure\/virtual-machines\/sizes."},{"key":"e_1_3_2_71_2","unstructured":"Azure service plan. 2022. Retrieved 11 October 2022 from https:\/\/azure.microsoft.com\/en-us\/pricing\/details\/virtual-machines\/windows\/."},{"key":"e_1_3_2_72_2","doi-asserted-by":"crossref","unstructured":"P. Ta-Shma A. Akbar G. Gerson-Golan G. Hadash F. Carrez and K. Moessner. 2018. An ingestion and analytics architecture for IoT applied to smart city use cases. IEEE Internet Things J . 5 2 (2018) 765\u2013774. DOI:10.1109\/JIOT.2017.2722378","DOI":"10.1109\/JIOT.2017.2722378"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-021-00924-y"},{"key":"e_1_3_2_74_2","doi-asserted-by":"crossref","unstructured":"L. Lin L. Pan and S. Liu. 2020. Backup or not: An online cost optimal algorithm for data analysis jobs using spot instances. IEEE Access 8 (2020) 144945\u2013144956. DOI:10.1109\/ACCESS.2020.3014978","DOI":"10.1109\/ACCESS.2020.3014978"},{"key":"e_1_3_2_75_2","unstructured":"Spot instances in AWS. 2022. Retrieved 11 October 2022 from https:\/\/aws.amazon.com\/blogs\/compute\/running-high-scale-web-on-spot-instances\/."},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107895"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2020.07.028"},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2017.12.015"},{"key":"e_1_3_2_79_2","doi-asserted-by":"crossref","unstructured":"Ilia Pietri and Rizos Sakellariou. 2017. Mapping virtual machines onto physical machines in cloud computing: A survey. ACM Comput. Surv . 49 3 Article 49 (September 2017) 30.","DOI":"10.1145\/2983575"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2021.101996"},{"key":"e_1_3_2_81_2","unstructured":"Guangyao Zhou Wenhong Tian and Rajkumar Buyya. 2021. Deep reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions. Association for Computing Machinery . arXiv:2105.04086. Retrieved from http:\/\/arxiv.org\/abs\/2105.04086."},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2016.04.016"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2016.01.018"},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-03885-3"},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-021-00935-9"},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2020.2967041"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107348"},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2936116"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2924958"},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2021.05.026"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-03894-2"},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2020.2977843"},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-021-00930-0"},{"key":"e_1_3_2_94_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2020.2994015"},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-021-03291-7"},{"key":"e_1_3_2_96_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.07.015"},{"key":"e_1_3_2_97_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.12.054"},{"key":"e_1_3_2_98_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.09.039"},{"key":"e_1_3_2_99_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2872674"},{"key":"e_1_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.1145\/3344341.3368800"},{"key":"e_1_3_2_101_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.12.063"},{"key":"e_1_3_2_102_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2018.2889482"},{"key":"e_1_3_2_103_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2843802"},{"key":"e_1_3_2_104_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.10.003"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-019-02910-8"},{"key":"e_1_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.1145\/3410992.3411015"},{"key":"e_1_3_2_107_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3054420"},{"key":"e_1_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2019.2912077"},{"key":"e_1_3_2_109_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.10.018"},{"key":"e_1_3_2_110_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2021.04.008"},{"key":"e_1_3_2_111_2","doi-asserted-by":"publisher","DOI":"10.1109\/IIKI.2016.87"},{"key":"e_1_3_2_112_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2015.2481400"},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.11.011"},{"key":"e_1_3_2_114_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3065597"},{"key":"e_1_3_2_115_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2018.09.011"},{"key":"e_1_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2975741"},{"key":"e_1_3_2_117_2","doi-asserted-by":"crossref","unstructured":"Pawan Kumar and Rakesh Kumar. 2019. Issues and challenges of load balancing techniques in cloud computing: A survey. ACM Comput. Surv . 51 6 Article 120 (November 2019) 35.","DOI":"10.1145\/3281010"},{"key":"e_1_3_2_118_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-020-03600-8"},{"key":"e_1_3_2_119_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-03941-y"},{"key":"e_1_3_2_120_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-03760-1"},{"key":"e_1_3_2_121_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.12.019"},{"key":"e_1_3_2_122_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2818932"},{"key":"e_1_3_2_123_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2021.10.003"},{"key":"e_1_3_2_124_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107511"},{"key":"e_1_3_2_125_2","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5558"},{"key":"e_1_3_2_126_2","doi-asserted-by":"crossref","unstructured":"A. H. T. Dias L. H. A. Correia and N. Malheiros. 2022. A systematic literature review on virtual machine consolidation. ACM Comput. Surv . 54 8 Article 176 (November 2022) 38.","DOI":"10.1145\/3470972"},{"key":"e_1_3_2_127_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2884028"},{"key":"e_1_3_2_128_2","unstructured":"Forbes. 2022. Retrieved 11 October 2022 from https:\/\/www.forbes.com\/sites\/forbestechcouncil\/2021\/05\/03\/renewable-energy-alone-cant-address-data-centers-adverse-environmental-impact\/?sh=729ab68e5ddc."},{"key":"e_1_3_2_129_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2961952"},{"key":"e_1_3_2_130_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.02.042"},{"key":"e_1_3_2_131_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2021.01.022"},{"key":"e_1_3_2_132_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.2986614"},{"key":"e_1_3_2_133_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2016.10.003"},{"key":"e_1_3_2_134_2","doi-asserted-by":"publisher","DOI":"10.1145\/3418501"},{"key":"e_1_3_2_135_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2017.2693149"},{"key":"e_1_3_2_136_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-021-01014-9"},{"key":"e_1_3_2_137_2","doi-asserted-by":"crossref","unstructured":"R. Jeyaraj and A. Paul. 2022. Optimizing mapreduce task scheduling on virtualized heterogeneous environments using ant colony optimization. IEEE Access 10 (2022) 55842\u201355855. DOI:10.1109\/access.2022.3176729","DOI":"10.1109\/ACCESS.2022.3176729"},{"key":"e_1_3_2_138_2","doi-asserted-by":"publisher","DOI":"10.1002\/9781119525080.ch17"},{"key":"e_1_3_2_139_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-89856-6_19"},{"key":"e_1_3_2_140_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-020-03310-1"},{"key":"e_1_3_2_141_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-020-00273-1"},{"key":"e_1_3_2_142_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2020.102144"},{"key":"e_1_3_2_143_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2877696"},{"key":"e_1_3_2_144_2","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2509"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3571729","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3571729","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:33Z","timestamp":1750182573000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3571729"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,2]]},"references-count":143,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12,31]]}},"alternative-id":["10.1145\/3571729"],"URL":"https:\/\/doi.org\/10.1145\/3571729","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,2]]},"assertion":[{"value":"2022-02-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-10-17","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-03-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}