{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:20:04Z","timestamp":1772137204044,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T00:00:00Z","timestamp":1672617600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T00:00:00Z","timestamp":1672617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Netw Distrib Comput"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Edge computing aims at reducing computation and storage across the cloud and provides service with reduced latency. Edge devices can be mobile devices, routers, cameras, printers or any Internet of Things (IoT) devices that generate vast amounts of data. The processing of these data is done by virtual machines (VMs) present in the edge servers that are located within close proximity of the edge devices. This work proposes two models which predict resource contention at the edge servers, namely, a Dynamic Markov model for Resource Contention Prediction in Edge Cloud (DMRCP) and a Hybrid Cascade of Regression and Markov model for Resource Contention Prediction (CRMRCP). In DMRCP, a history matrix is updated based on the CPU utilization of a Virtual Machine (VM). This history matrix is used to update a transition probability matrix. This matrix is used to predict the future state of the VM. In the CRMRCP approach, the past CPU utilization values of the virtual machines in the edge servers are used for predicting a set of future CPU utilization values using linear regression, polynomial regression, lasso regression and ridge regression. Then, the predicted future CPU utilization values are used by the dynamic and the second-order Markov models to classify the state of the edge servers as overloaded, underloaded or normally loaded. In both the approaches, the VMs that may cause resource contention are predicted and are migrated to other edge servers such that the destination edge server does not get overloaded after the migration. The DMRCP method is compared with the first-order and the second-order Markov models and the number of VM migrations is analysed to evaluate the performance. The number of VM migrations in the CRMRCP method is compared with that in the second-order Markov model. The overall results prove that the number of VM migrations for the DMRCP is 52.9% less compared to the first-order Markov model and 21.1% less when compared to the second-order Markov model. The number of VM migrations in CRMRCP is reduced by 81.8% when ridge regression cascaded with the second-order Markov model is used.<\/jats:p>","DOI":"10.1007\/s44227-022-00007-0","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T16:02:41Z","timestamp":1672675361000},"page":"20-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Novel Approaches for Resource Management Across Edge Servers"],"prefix":"10.1007","volume":"11","author":[{"given":"K.","family":"Surya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V. Mary Anita","family":"Rajam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"issue":"3","key":"7_CR1","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1109\/LWC.2017.2696539","volume":"6","author":"A Al-Shuwaili","year":"2017","unstructured":"Al-Shuwaili A, Simeone O (2017) Energy-efficient resource allocation for mobile edge computing-based augmented reality applications. IEEE Wirel Commun Lett 6(3):398\u2013401","journal-title":"IEEE Wirel Commun Lett"},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/978-3-319-94340-4_6","volume-title":"Lecture Notes in Computer Science","author":"KR Alasmari","year":"2018","unstructured":"Alasmari KR, Green RC, Alam M (2018) Mobile edge offloading using markov decision processes. Lecture Notes in Computer Science. Springer, Berlin, pp 80\u201390. https:\/\/doi.org\/10.1007\/978-3-319-94340-4_6"},{"key":"7_CR3","first-page":"31","volume-title":"International Conference on Edge Computing","author":"T Bahreini","year":"2019","unstructured":"Bahreini T, Badri H, Grosu D (2019) Energy-aware capacity provisioning and resource allocation in edge computing systems. International Conference on Edge Computing. Springer, Berlin, pp 31\u201345"},{"issue":"5","key":"7_CR4","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1016\/j.future.2011.04.017","volume":"28","author":"A Beloglazov","year":"2012","unstructured":"Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future gener comput syst 28(5):755\u2013768","journal-title":"Future gener comput syst"},{"key":"7_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2991734","author":"K Cao","year":"2020","unstructured":"Cao K, Liu Y, Meng G et al (2020) An overview on edge computing research. IEEE Access. https:\/\/doi.org\/10.1109\/access.2020.2991734","journal-title":"IEEE Access"},{"key":"7_CR6","doi-asserted-by":"publisher","unstructured":"Caprolu M, Di\u00a0Pietro R, Lombardi F, et. al (2019) Edge computing perspectives: Architectures, technologies, and open security issues. In: 2019 IEEE International Conference on Edge Computing (EDGE), pp 116\u2013123, Doi: https:\/\/doi.org\/10.1109\/EDGE.2019.00035","DOI":"10.1109\/EDGE.2019.00035"},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"53915","DOI":"10.1109\/ACCESS.2020.2981011","volume":"8","author":"J Chen","year":"2020","unstructured":"Chen J, Wang Y (2020) An adaptive short-term prediction algorithm for resource demands in cloud computing. IEEE Access 8:53915\u201353930","journal-title":"IEEE Access"},{"issue":"1","key":"7_CR8","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/MNET.2018.1700145","volume":"32","author":"X Chen","year":"2018","unstructured":"Chen X, Shi Q, Yang L et al (2018) Thriftyedge: Resource-efficient edge computing for intelligent iot applications. IEEE net 32(1):61\u201365","journal-title":"IEEE net"},{"issue":"1","key":"7_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13677-019-0130-2","volume":"8","author":"AA El-Moursy","year":"2019","unstructured":"El-Moursy AA, Abdelsamea A, Kamran R et al (2019) Multi-dimensional regression host utilization algorithm (mdrhu) for host overload detection in cloud computing. J Cloud Comput 8(1):1\u201317","journal-title":"J Cloud Comput"},{"key":"7_CR10","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1016\/j.future.2019.05.037","volume":"100","author":"IA Elgendy","year":"2019","unstructured":"Elgendy IA, Zhang W, Tian YC et al (2019) Resource allocation and computation offloading with data security for mobile edge computing. Future Gener Comput Syst 100:531\u2013541","journal-title":"Future Gener Comput Syst"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Farahnakian F, Liljeberg P, Plosila J (2013) Lircup: Linear regression based cpu usage prediction algorithm for live migration of virtual machines in data centers. In: 2013 39th Euromicro conference on software engineering and advanced applications, IEEE, pp 357\u2013364","DOI":"10.1109\/SEAA.2013.23"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Jia F, Zhang H, Ji H, et\u00a0al (2018) Distributed resource allocation and computation offloading scheme for cognitive mobile edge computing networks with noma. In: 2018 IEEE\/CIC International Conference on Communications in China (ICCC), pp 553\u2013557","DOI":"10.1109\/ICCChina.2018.8641192"},{"key":"7_CR13","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1016\/j.comcom.2020.01.004","volume":"151","author":"C Jiang","year":"2020","unstructured":"Jiang C, Fan T, Gao H et al (2020) Energy aware edge computing: A survey. Comput Commun 151:556\u2013580","journal-title":"Comput Commun"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Jo\u0161ilo S, D\u00e1n G (2019) Wireless and computing resource allocation for selfish computation offloading in edge computing. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, IEEE, pp 2467\u20132475","DOI":"10.1109\/INFOCOM.2019.8737480"},{"issue":"4","key":"7_CR15","doi-asserted-by":"publisher","first-page":"1507","DOI":"10.1109\/TCC.2019.2923768","volume":"9","author":"S Jo\u0161ilo","year":"2021","unstructured":"Jo\u0161ilo S, Dan G (2021) Joint management of wireless and computing resources for computation offloading in mobile edge clouds. IEEE Trans Cloud Comput 9(4):1507\u20131520. https:\/\/doi.org\/10.1109\/TCC.2019.2923768","journal-title":"IEEE Trans Cloud Comput"},{"key":"7_CR16","unstructured":"Kolosov O, Yadgar G, Maheshwari S, et. al (2020) Benchmarking in the dark: On the absence of comprehensive edge datasets. In: 3rd $$\\{USENIX\\}$$ Workshop on Hot Topics in Edge Computing (HotEdge 20)"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Liu X, Qin Z, Gao Y (2019) Resource allocation for edge computing in iot networks via reinforcement learning. In: ICC 2019-2019 IEEE International Conference on Communications (ICC), IEEE, pp 1\u20136","DOI":"10.1109\/ICC.2019.8761385"},{"issue":"4","key":"7_CR18","doi-asserted-by":"publisher","first-page":"3415","DOI":"10.1109\/JIOT.2020.2970110","volume":"7","author":"X Liu","year":"2020","unstructured":"Liu X, Yu J, Wang J et al (2020) Resource allocation with edge computing in iot networks via machine learning. IEEE Internet Thin J 7(4):3415\u20133426","journal-title":"IEEE Internet Thin J"},{"key":"7_CR19","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/978-981-15-1041-0_7","volume-title":"Algorithms in machine learning paradigms","author":"G Mandal","year":"2020","unstructured":"Mandal G, Dam S, Dasgupta K et al (2020) A linear regression-based resource utilization prediction policy for live migration in cloud computing. Algorithms in machine learning paradigms. Springer, Berlin, pp 109\u2013128"},{"key":"7_CR20","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1080\/00031305.1975.10479105","volume":"29","author":"D Marquardt","year":"1975","unstructured":"Marquardt D, Snee R (1975) Ridge regression in practice. Am Stat - Amer Statist 29:3\u201320. https:\/\/doi.org\/10.1080\/00031305.1975.10479105","journal-title":"Am Stat - Amer Statist"},{"key":"7_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/ICACA.2016.7887916","author":"R Muthukrishnan","year":"2016","unstructured":"Muthukrishnan R, Rohini R (2016) Lasso: A feature selection technique in predictive modeling for machine learning. IEEE Int Con Adv Comput Appl (ICACA). https:\/\/doi.org\/10.1109\/ICACA.2016.7887916","journal-title":"IEEE Int Con Adv Comput Appl (ICACA)"},{"issue":"1","key":"7_CR22","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1145\/1113361.1113374","volume":"40","author":"K Park","year":"2006","unstructured":"Park K, Pai VS (2006) Comon: a mostly-scalable monitoring system for planetlab. ACM SIGOPS Operat Syst Rev 40(1):65\u201374","journal-title":"ACM SIGOPS Operat Syst Rev"},{"key":"7_CR23","doi-asserted-by":"publisher","first-page":"273","DOI":"10.22271\/allresearch.2021.v7.i8d.8876","volume":"7","author":"S Patil","year":"2021","unstructured":"Patil S, Patil S (2021) Linear with polynomial regression: Overview. Int J Appl Res 7:273\u2013275. https:\/\/doi.org\/10.22271\/allresearch.2021.v7.i8d.8876","journal-title":"Int J Appl Res"},{"key":"7_CR24","doi-asserted-by":"publisher","unstructured":"Plachy J, Becvar Z, Strinati EC (2016) Dynamic resource allocation exploiting mobility prediction in mobile edge computing. In: 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp 1\u20136, Doi: https:\/\/doi.org\/10.1109\/PIMRC.2016.7794955","DOI":"10.1109\/PIMRC.2016.7794955"},{"key":"7_CR25","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.comcom.2020.05.037","volume":"160","author":"N Shan","year":"2020","unstructured":"Shan N, Cui X, Gao Z (2020) Drl+ fl-an intelligent resource allocation model based on deep reinforcement learning for mobile edge computing. Comput Commun 160:14\u201324","journal-title":"Comput Commun"},{"key":"7_CR26","doi-asserted-by":"crossref","unstructured":"Shan N, Li Y, Cui X (2020b) A multilevel optimization framework for computation offloading in mobile edge computing. Mathematical Problems in Engineering 2020","DOI":"10.1155\/2020\/4124791"},{"issue":"5","key":"7_CR27","first-page":"637","volume":"3","author":"W Shi","year":"2016","unstructured":"Shi W, Cao J, Zhang Q et al (2016) Edge computing: Vision and challenges. IEEE Int Thing 3(5):637\u2013646","journal-title":"IEEE Int Thing"},{"key":"7_CR28","first-page":"1740","volume":"12","author":"H Shingne","year":"2020","unstructured":"Shingne H, Sountharrajan S, Karthiga M et al (2020) Lasso and ridge regression for optimized resource allocation in cloud computing. J Adv Res Dynam Contr Syst 12:1740\u20131747","journal-title":"J Adv Res Dynam Contr Syst"},{"issue":"101","key":"7_CR29","first-page":"217","volume":"67","author":"D Spatharakis","year":"2020","unstructured":"Spatharakis D, Dimolitsas I, Dechouniotis D et al (2020) A scalable edge computing architecture enabling smart offloading for location based services. Pervasive Mobile Comput 67(101):217","journal-title":"Pervasive Mobile Comput"},{"key":"7_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-021-00967-1","author":"K Surya","year":"2021","unstructured":"Surya K, Rajam VMA (2021) Prediction of resource contention in cloud using second order markov model. Springer Comput. https:\/\/doi.org\/10.1007\/s00607-021-00967-1","journal-title":"Springer Comput"},{"issue":"3","key":"7_CR31","doi-asserted-by":"publisher","first-page":"55","DOI":"10.3390\/fi11030055","volume":"11","author":"S Svorobej","year":"2019","unstructured":"Svorobej S, Takako Endo P, Bendechache M et al (2019) Simulating fog and edge computing scenarios: An overview and research challenges. Future Internet 11(3):55","journal-title":"Future Internet"},{"issue":"8","key":"7_CR32","doi-asserted-by":"publisher","first-page":"1482","DOI":"10.1109\/JPROC.2019.2927919","volume":"107","author":"Z Tao","year":"2019","unstructured":"Tao Z, Xia Q, Hao Z et al (2019) A survey of virtual machine management in edge computing. Proceed IEEE 107(8):1482\u20131499","journal-title":"Proceed IEEE"},{"issue":"6","key":"7_CR33","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1109\/JSAC.2020.2986615","volume":"38","author":"X Xiong","year":"2020","unstructured":"Xiong X, Zheng K, Lei L et al (2020) Resource allocation based on deep reinforcement learning in iot edge computing. IEEE J Selec Area Commun 38(6):1133\u20131146. https:\/\/doi.org\/10.1109\/JSAC.2020.2986615","journal-title":"IEEE J Selec Area Commun"},{"issue":"4","key":"7_CR34","doi-asserted-by":"publisher","first-page":"2483","DOI":"10.1109\/JIOT.2020.3033285","volume":"8","author":"G Yang","year":"2021","unstructured":"Yang G, Hou L, He X et al (2021) Offloading time optimization via markov decision process in mobile-edge computing. IEEE Internet Thin J 8(4):2483\u20132493. https:\/\/doi.org\/10.1109\/JIOT.2020.3033285","journal-title":"IEEE Internet Thin J"},{"issue":"3","key":"7_CR35","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1109\/TWC.2016.2633522","volume":"16","author":"C You","year":"2016","unstructured":"You C, Huang K, Chae H et al (2016) Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Transact Wirel Commun 16(3):1397\u20131411","journal-title":"IEEE Transact Wirel Commun"},{"key":"7_CR36","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.sysarc.2019.02.009","volume":"98","author":"A Yousefpour","year":"2019","unstructured":"Yousefpour A, Fung C, Nguyen T et al (2019) All one needs to know about fog computing and related edge computing paradigms: A complete survey. J Syst Architec 98:289\u2013330. https:\/\/doi.org\/10.1016\/j.sysarc.2019.02.009","journal-title":"J Syst Architec"},{"key":"7_CR37","doi-asserted-by":"publisher","first-page":"5896","DOI":"10.1109\/ACCESS.2016.2597169","volume":"4","author":"K Zhang","year":"2016","unstructured":"Zhang K, Mao Y, Leng S et al (2016) Energy-efficient offloading for mobile edge computing in 5g heterogeneous networks. IEEE access 4:5896\u20135907","journal-title":"IEEE access"},{"issue":"5","key":"7_CR38","doi-asserted-by":"publisher","first-page":"3606","DOI":"10.1109\/JIOT.2018.2823498","volume":"5","author":"Z Zhao","year":"2018","unstructured":"Zhao Z, Min G, Gao W et al (2018) Deploying edge computing nodes for large-scale iot: A diversity aware approach. IEEE Internet of Things Journal 5(5):3606\u20133614","journal-title":"IEEE Internet of Things Journal"},{"issue":"1","key":"7_CR39","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1109\/MCOM.001.1900103","volume":"58","author":"G Zhu","year":"2020","unstructured":"Zhu G, Liu D, Du Y et al (2020) Toward an intelligent edge: wireless communication meets machine learning. IEEE Commun Mag 58(1):19\u201325","journal-title":"IEEE Commun Mag"}],"container-title":["International Journal of Networked and Distributed Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44227-022-00007-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44227-022-00007-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44227-022-00007-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T10:03:28Z","timestamp":1684317808000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44227-022-00007-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,2]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["7"],"URL":"https:\/\/doi.org\/10.1007\/s44227-022-00007-0","relation":{},"ISSN":["2211-7938","2211-7946"],"issn-type":[{"value":"2211-7938","type":"print"},{"value":"2211-7946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,2]]},"assertion":[{"value":"25 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}