Abstract
The new era of computation has given new directions for advancement and sophistication. The meaning of computation has made a paradigm shift from device and location orientation to the distributed level. Cloud has working models with a service-oriented delivery mechanism as well as a deployment oriented infrastructure mechanism. Data centers are the backbone of cloud computing. Multi-cloud exchanges allow organizations to establish a single connection to multiple cloud providers at the same time through an Ethernet switching platform, rather than wrestling with multiple individual connections to cloud providers. The massive participation of the public has also increased the load on the cloud servers. Proper scheduling of resources is always needed. There are many load balancing algorithms are there and all of them have their limitations. Different circumstances need different load balancing approaches. One single algorithm would not be able to provide a proper scheduling mechanism in all the needed directions. Therefore ‘hybrid algorithms’ would be best suited to provide improved services to the needs of the present conditions. This present paper analyzed a few and this can be carry forwarded to the next level with some other algorithms and parameters with vigorous efforts to analyze.











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Abbas I, Ahmad M, Faizan M, Arshed W and Khalid J (2020) Issues and challenges of cloud computing in performance augmentation for pervasive computing. In: 2020 international conference on electrical, communication, and computer engineering (ICECCE), Istanbul, Turkey, pp 1–7
Aceto G, Persico V, Pescape A (2020) Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0. J Indust Inform Integrat 18(2):100–129
Ahmed EA, Ali Ahmed H (2018) A proposed model for education system using cloud computing. In: 2018 3rd international conference on emerging trends in engineering, sciences and technology (ICEEST), Karachi, Pakistan, pp 1–4
Ali D, Gupta MK (2019) Deadline sensitive lease scheduling using hungarian genetic algorithm in cloud computing environment. Internat J Comput Sci Eng 7(12):7–15
Amanatullah Y, Lim C, Ipung HP, Juliandri A (2013) Toward cloud computing reference architecture: Cloud service management perspective. In: 2013 International Conference on ICT for Smart Society, Jakarta, pp 1–4
Ayadi I, Simoni N, Aubonnet T (2013) SLA approach for cloud as a service. In: 2013 IEEE Sixth International Conference on Cloud Computing, Santa Clara, CA, pp 966–967
Banan A, Nasiri A, Taheri-Garavand A (2020) Deep learning-based appearance features extraction for automated carp species identification. Aquacult Eng 89(2):1–10
Chang C, Yang J, Chang K (2012) An efficient and flexible mobile payment protocol. In: 2012 sixth international conference on genetic and evolutionary computing, Kitakushu, pp 63–66
Dongxing H (2012) Informationn network security situation aware technology based on artificial intelligence[J]. J Inform Comm 6(2012):80–81
Drakopoulos G, Kafeza E, Al Katheeri H (2019) Proof systems in blockchains: a survey. In: 2019 4th South-East Europe design automation, computer engineering, computer networks and social media conference, PiraeusGreece, pp 1–6
Duan L, Zhan D, Hohnerlein J (2015) Optimizing cloud data center energy efficiency via dynamic prediction of CPU Idle Intervals.In: 2015 IEEE 8th international conference on cloud computing, New York, NY, pp 985–988
Fan YJ (2020) Spatiotemporal modeling for nonlinear distributed thermal processes based on KL decomposition. MLP and LSTM Network IEEE Access 8(1):25111–25121
Fang S (2017) An integrated system for land resources supervision based on the IoT and cloud computing. Enterprise Inform Syst 11(1):105–121
Ganjkhani M (2019) A novel detection algorithm to identify false data injection attack on power system state estimation. Energies 12(2209):1–9
He W (2014) Developing vehicular data cloud services in the IoT environment. IEEE Trans Industr Inf 10(2):1587–1595
Henry R, Herzberg A, Kate A (2018) Blockchain access privacy: challenges and directions. IEEE Secur Priv 16(4):38–45
Hossan MT, Chowdhury MZ, Shahjalal M, Jang YM (2019) Human bond communication with head-mounted displays: scope, challenges, solutions, and applications. IEEE Commun Mag 57(2):26–32
Kim JH (2017) A review of cyber-physical system research relevant to the emerging IT trends: industry 4.0, IoT, big data, and cloud computing. J Ind Integr Manag 2(3):1750011
Li S (2012) Integration of hybrid wireless networks in cloud services oriented enterprise information systems. Enterp Inform Syst 6(2):165–187
Liu X, He W, Xu L, Yan G (2014) Enhancing the security of cloud manufacturing by restricting resource access. J Homel Secur Emerg Manage 11(4):533–554
Lopes Ferreira GA (2015) Internet of things and the credit card market: how companies can deal with the exponential increase of transactions with connected devices and can also be efficient to prevent frauds. In: 2015 12th international conference on information technology-new generations, Las Vegas,NV, pp 107–111
Lu Y (2020) Pricing the cloud: a QoS-based auction approach. Enterp Inform Syst 14(3):334–351
Maenhaut P, Moens H, Volckaert B, Ongenae V, De Turck F (2017) Resource allocation in the cloud: from simulation to experimental validation. In: 2017 IEEE 10th international conference on cloud computing (CLOUD), Honolulu, CA, pp 701–704
Mitra A, Kundu A, Chattopadhyay M, Chattopadhyay S (2017) A cost-efficient one time password-based authentication in cloud environment using equal lengthcellular automata. J Ind Inf Integr 5(1):17–25
Piao C, Zuo Y, Zhang C (2016) Research on hybrid-cloud-based user privacy protection of O2O platform. In: 2016 IEEE 13th international conference on e-business engineering (ICEBE), Macau, pp 214–219
Salapura.V (2012) Cloud computing: virtualization and resiliency for data center computing. In: 2012 IEEE 30th international conference on computer design (ICCD), Montreal, pp 1–2
Shamshirband S (2019) A survey of deep learning techniques: application in wind and solar energy resources. IEEE Access 7(1):164650–164666
Srinivasa Rao G, Anuradha T (2018a) Improved hybrid approach for load balancing in virtual machine. Internat J Comput Sci Eng 6(10):730–733
Srinivasa Rao G, Anuradha T (2018b) Improved implementation of hybrid approach in cloud environment. Internat J Comput Sci Eng 6(10):254–260
Subramanian V, Wang L, Lee E, Chen P (2010) Rapid processing of synthetic seismograms using windows azure cloud.In:2010 IEEE second international conference on cloud computing technology and science, Indianapolis, pp 193–200
Tripathi R, Vignesh S, Tamarapalli V, Medhi D (2017) Cost efficient design of fault tolerant geo-distributed data centers. IEEE Trans Netw Serv Manag 14(2):289–301
Verma A, Malla D, Choudhary AK, Arora V (2019) A detailed study of azure platform and its cognitive services. 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon). Faridabad, pp 29–134
Wang Z, Zeng J, Lv T, Shi B, Li B (2016) Cloud auditor: a cloud auditing framework based on nested virtualization. In: 2016 IEEE 3rd international conference on cyber security and cloud computing (CSCloud), Beijing, pp 50–53
Wang Y, Xia Y, Chen S (2017) Using integer programming for workflow scheduling in the cloud. In: 2017 IEEE 10th international conference on cloud computing (CLOUD), Honolulu, CA, pp 138–146
Xie C (2017) Linked semantic model for information resource toward cloud manufacturing. IEEE Trans Industr Inf 13(6):3338–3349
Xu L, Xu E, Li L (2018) Industry 40: state of the art and future trends. Internat J Prod Res 56(8):2941–2962
Yang J (2019) Cloud computing for storing and analyzing petabytes of genomic data. J Ind Inform Integr 15(7):50–57
Zheng X (2014) Cloud service negotiation in internet of things environment: a mixed approach. IEEE Trans Industr Inf 10(2):1506–1515
Acknowledgements
I wish to thank various Data centers in Hyderabad Cloud4C & CtrlS-CTRLS, NettLinx, Ricoh Data Center, and National Informatics Centre (NIC)—Hyderabad who has guided me to collect relevant information.I sincerely thank my Research Supervisor and Ex-Vice-Chancellor (i/c) of Dravidian University, Prof. T. Anuradha for her elderly contribution to this paper and for her guiding through her teaching, research guidance which made me explore more into the qualitative content about the cloud computing environment. I wish to thank Late Mr. Panem Nadipi Chennaih for his continues support for the development of this research paper and it is dedicated to him.
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Gundu, S.R., Panem, C.A., Thimmapuram, A. et al. Emerging computational challenges in cloud computing and RTEAH algorithm based solution. J Ambient Intell Human Comput 13, 4249–4263 (2022). https://doi.org/10.1007/s12652-021-03380-w
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DOI: https://doi.org/10.1007/s12652-021-03380-w