[1]  RedHat, “What is a virtual machine (vm)?” 2022, last accessed 20 October 2022. [Online].
Available: https://www.redhat.com/en/topics/virtualization/what-is-a-virtual-machine

[2]  Docker, “What is a container? - docker,” 2023, last accessed 15 September 2023. [Online].
Available: https://www.docker.com/resources/what-container/

[3]  O. Oleghe, “Container placement and migration in edge computing: Concept and scheduling
models,” *IEEE Access*, vol. 9, pp. 68 028–68 043, 2021.

[4]  C. Pahl, S. Helmer, L. Miori, J. Sanin, and B. Lee, “A container-based edge cloud paas
architecture based on raspberry pi clusters,” in *2016 IEEE 4th International Conference on
Future Internet of Things and Cloud Workshops (FiCloudW)*. IEEE, 2016, pp. 117–124.

[5]  Weaveworks, “Docker vs virtual machines (vms) : A practical guide to docker containers
and vms,” 2020, last accessed 15 September 2023. [Online]. Available: https://www.weave.
works/blog/a-practical-guide-to-choosing-between-docker-containers-and-vms

[6]  C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield,
“Live migration of virtual machines,” in *Proceedings of the 2nd Conference on Symposium
on Networked Systems Design and Implementation*, ser. NSDI’05, vol. 2. USA: USENIX
Association, 2005, p. 273–286.

[7]  M. R. Hines, U. Deshpande, and K. Gopalan, “Post-copy live migration of virtual
machines,” *SIGOPS Oper. Syst. Rev.*, vol. 43, no. 3, p. 14–26, jul 2009. [Online]. Available:
https://doi.org/10.1145/1618525.1618528

[8]  A. Araldo, A. D. Stefano, and A. D. Stefano, “Resource allocation for edge
computing with multiple tenant configurations,” in *Proceedings of the 35th Annual
ACM Symposium on Applied Computing*, ser. SAC ’20. New York, NY, USA:
Association for Computing Machinery, 2020, p. 1190–1199. [Online]. Available:
https://doi.org/10.1145/3341105.3374026

[9]  R. W. Ahmad, A. Gani, S. H. Ab. Hamid, M. Shiraz, F. Xia, and S. A. Madani, “Virtual
machine migration in cloud data centers: a review, taxonomy, and open research issues,” *The
Journal of Supercomputing*, vol. 71, pp. 2473–2515, 2015.

[10]  R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. De Rose, and R. Buyya, “Cloudsim: a
toolkit for modeling and simulation of cloud computing environments and evaluation of re-
source provisioning algorithms,” *Software: Practice and experience*, vol. 41, no. 1, pp. 23–
50, 2011.

[11]  T. C. Computing and U. o. M. Distributed Systems (CLOUDS) Laboratory, “Cloudsim: A
framework for modeling and simulation of cloud computing infrastructures and services,”
2023, last accessed 10 October 2023. [Online]. Available: http://www.cloudbus.org/
cloudsim/

[12]  P. Kumar and R. Kumar, “Issues and challenges of load balancing techniques in cloud
computing: A survey,” *ACM Comput. Surv.*, vol. 51, no. 6, feb 2019. [Online]. Available:
https://doi.org/10.1145/3281010

[13]  C. Pham, D. T. Nguyen, Y. Njah, N. H. Tran, K. K. Nguyen, and M. Cheriet, “Share-to-run
iot services in edge cloud computing,” *IEEE Internet of Things Journal*, vol. 9, no. 1, pp.
497–509, 2021.

[14]  T. Hirofuchi, H. Nakada, H. Ogawa, S. Itoh, and S. Sekiguchi, “A live storage
migration mechanism over wan and its performance evaluation,” in *Proceedings of the
3rd International Workshop on Virtualization Technologies in Distributed Computing*, ser.
VTDC ’09. New York, NY, USA: Association for Computing Machinery, 2009, p. 67–74.
[Online]. Available: https://doi.org/10.1145/1555336.1555348

[15]  U.Mandal,P.Chowdhury,M.Tornatore,C.U.Martel,andB.Mukherjee,“Bandwidthprovi-
sioning for virtual machine migration in cloud: Strategy and application,” *IEEE Transactions
on Cloud Computing*, vol. 6, no. 4, pp. 967–976, 2018.

[16]  M.A.Altahat,A.Agarwal,N.Goel,andM.Zaman,“Analysisandcomparisonoflivevirtual
machine migration methods,” in *2018 IEEE 6th international Conference on Future internet
of Things and Cloud (FiCloud)*. IEEE, 2018, pp. 251–258.

[17]  M. A. Altahat, A. Agarwal, N. Goel, and J. Kozlowski, “Dynamic hybrid-copy live
virtual machine migration: Analysis and comparison,” *Procedia Computer Science*,
vol. 171, pp. 1459–1468, 2020, third International Conference on Computing and
Network Communications (CoCoNet’19). [Online]. Available: https://www.sciencedirect.
com/science/article/pii/S1877050920311352

[18]  M. Sharma and R. Garg, “An artificial neural network based approach for energy efficient
task scheduling in cloud data centers,” *Sustainable Computing: Informatics and Systems*,
vol. 26, p. 100373, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/
pii/S2210537918302798

[19]  S. Ouhame, Y. Hadi, and A. Ullah, “An efficient forecasting approach for resource utilization
in cloud data center using cnn-lstm model,” *Neural Computing and Applications*, vol. 33, pp.
10 043–10 055, 2021.

[20]  S. Kumar T, S. D. S. Mustapha, P. Gupta, and R. P. Tripathi, “Hybrid approach for resource
allocation in cloud infrastructure using random forest and genetic algorithm,” *Scientific Pro-
gramming*, vol. 2021, pp. 1–10, 2021.

[21]  G. Singh and P. Gupta, “A review on migration techniques and challenges in live virtual ma-
chine migration,” in *2016 5th International Conference on Reliability, Infocom Technologies
and Optimization (Trends and Future Directions)(ICRITO)*. IEEE, 2016, pp. 542–546.

[22]  M. Soni, “The cloudsim framework: Modelling and simulating the cloud environment,”
2014, last accessed 14 August 2021. [Online]. Available: https://www.opensourceforu.com/
2014/03/cloudsim-framework-modelling-simulating-cloud-environment/

[23]  I. C. Education, “What are neural networks?” 2020, last accessed 14 October 2021. [Online].
Available: https://www.ibm.com/topics/neural-networks

[24]  ——, “What are convolutional neural networks?” 2020, last accessed 14 October 2021.
[Online]. Available: https://www.ibm.com/topics/convolutional-neural-networks

[25]  ——, “What are recurrent neural networks?” 2020, last accessed 14 October 2021. [Online].
Available: https://www.ibm.com/topics/recurrent-neural-networks

[26]  R. Awadallah, A. Samsudin, J. S. Teh, and M. Almazrooie, “An integrated architecture for
maintaining security in cloud computing based on blockchain,” *IEEE Access*, vol. 9, pp.
69 513–69 526, 2021.

[27]  C. V. B. Murthy, M. L. Shri, S. Kadry, and S. Lim, “Blockchain based cloud computing:
Architecture and research challenges,” *IEEE access*, vol. 8, pp. 205 190–205 205, 2020.

[28]  P. Stetsenko and G. Khalimov, “Blockchain-based protocol for ensuring authenticity of data
origin in cloud environments.” in *ICST*, 2020, pp. 176–190.

[29]  MongoDB, “Mongodb,” 2023, last accessed April 2023. [Online]. Available: https:
//www.mongodb.com

[30]  Tendermint, “Tendermint,” 2023, last accessed April 2023. [Online]. Available:
https://tendermint.com/

[31]  B. GmbH, “Bigchaindb 2.0: the blockchain database,” 2018.

[32]  Kubernetes, “Kubernetes: Production-grade container orchestration,” 2023, last accessed 25 September 2023. [Online]. Available: https://kubernetes.io/

[33]  C. Rosen, “Docker swarm vs. kubernetes: A comparison,” IBM Blog, June 2022,
last accessed 25 September 2023. [Online]. Available: https://www.ibm.com/blog/
docker-swarm-vs-kubernetes-a-comparison/

[34]  F. Zhang, G. Liu, X. Fu, and R. Yahyapour, “A survey on virtual machine migration: Chal-
lenges, techniques, and open issues,” *IEEE Communications Surveys & Tutorials*, vol. 20,
no. 2, pp. 1206–1243, 2018.

[35]  A. Mirkin, A. Kuznetsov, and K. Kolyshkin, “Containers checkpointing and live migration,”
in *Proceedings of the Linux Symposium*, vol. 2, 2008, pp. 85–90.

[36]  J. Pecholt, M. Huber, and S. Wessel, “Live migration of operating system containers in en-
crypted virtual machines,” in *Proceedings of the 2021 on Cloud Computing Security Work-
shop*, 2021, pp. 125–137.

[37]  F. Brasser, P. Jauernig, F. Pustelnik, A.-R. Sadeghi, and E. Stapf, “Trusted container exten-
sions for container-based confidential computing,” *arXiv preprint arXiv:2205.05747*, 2022.

[38]  T. Benjaponpitak, M. Karakate, and K. Sripanidkulchai, “Enabling live migration of con-
tainerized applications across clouds,” in *IEEE INFOCOM 2020 - IEEE Conference on Com-
puter Communications*, 2020, pp. 2529–2538.

[39]  G.Singh,P.Singh,A.Motii,andM.Hedabou,“Asecureandlightweightcontainermigration
technique in cloud computing,” *Journal of King Saud University-Computer and Information
Sciences*, p. 101887, 2023.

[40]  R. L. Rivest, A. Shamir, and L. Adleman, “A method for obtaining digital signatures and
public-key cryptosystems,” *Communications of the ACM*, vol. 21, no. 2, pp. 120–126, 1978.

[41]  V. Rijmen and J. Daemen, “Advanced encryption standard,” *Proceedings of federal infor-
mation processing standards publications, national institute of standards and technology*,
vol. 19, p. 22, 2001.

[42]  N.Koblitz,“Ellipticcurvecryptosystems,”*Mathematicsofcomputation*,vol.48,no.177,pp.
203–209, 1987.

[43]  B. Elaine, “Recommendation for key management, part 1: General,” *NIST Special Publica-
tion 800-57 Part 1 Revision 5*, 2020.

[44]  D. Patel, B. Patel, J. Vasa, and M. Patel, “A comparison of the key size and security level of
the ecc and rsa algorithms with a focus on cloud/fog computing,” in *International Conference
on Information and Communication Technology for Intelligent Systems*. Springer, 2023, pp.
43–53.

[45]  T. Wood, K. Ramakrishnan, P. Shenoy, J. Van der Merwe, J. Hwang, G. Liu, and L. Chau-
fournier, “Cloudnet: Dynamic pooling of cloud resources by live wan migration of virtual
machines,” *IEEE/ACM Transactions On Networking*, vol. 23, no. 5, pp. 1568–1583, 2014.

[46]  A. J. Mashtizadeh, M. Cai, G. Tarasuk-Levin, R. Koller, T. Garfinkel, and S. Setty,
“{XvMotion}:{Unified} virtual machine migration over long distance,” in *2014 usenix an-
nual technical conference (usenix atc 14)*, 2014, pp. 97–108.

[47]  R. Bradford, E. Kotsovinos, A. Feldmann, and H. Schio ̈berg, “Live wide-area migration of
virtual machines including local persistent state,” in *Proceedings of the 3rd international
conference on Virtual execution environments*, 2007, pp. 169–179.

[48]  S. He, C. Hu, B. Shi, T. Wo, and B. Li, “Optimizing virtual machine live migration
without shared storage in hybrid clouds,” in *2016 IEEE 18th International Conference
on High Performance Computing and Communications; IEEE 14th International Confer-
ence on Smart City; IEEE 2nd International Conference on Data Science and Systems
(HPCC/SmartCity/DSS)*. IEEE, 2016, pp. 921–928.

[49]  U. Deshpande and K. Keahey, “Traffic-sensitive live migration of virtual machines,” *Future
Generation Computer Systems*, vol. 72, pp. 118–128, 2017.

[50]  H. Wang, F. Li, W. Mo, P. Tao, H. Shen, Y. Wu, Y. Zhang, and F. Deng, “Novel cloud-edge
collaborative detection technique for detecting defects in pv components, based on transfer
learning,” *Energies*, vol. 15, no. 21, p. 7924, 2022.

[51]  D.Wang,W.Zhang,X.Han,J.Lin,andY.-C.Tian,“Amulti-objectivevirtualnetworkmigra-
tion algorithm based on reinforcement learning,” *IEEE Transactions on Cloud Computing*,
2022.

[52]  Z. Yang, R. Gu, H. Li, and Y. Ji, “Approximately lossless model compression-based mul-
tilayer virtual network embedding for edge-cloud collaborative services,” *IEEE Internet of
Things Journal*, 2023.

[53]  E. F. Maleki, L. Mashayekhy, and S. M. Nabavinejad, “Mobility-aware computation offload-
ing in edge computing using machine learning,” *IEEE Transactions on Mobile Computing*,
vol. 22, no. 1, pp. 328–340, 2021.

[54]  J. Wang, J. Hu, G. Min, Q. Ni, and T. El-Ghazawi, “Online service migration in mobile edge
with incomplete system information: A deep recurrent actor-critic learning approach,” *IEEE
Transactions on Mobile Computing*, vol. 22, no. 11, pp. 6663–6675, 2023.

[55]  S. Padhy and J. Chou, “Mirage: A consolidation aware migration avoidance
genetic job scheduling algorithm for virtualized data centers,” *Journal of Parallel
and Distributed Computing*, vol. 154, pp. 106–118, 2021. [Online]. Available:
https://www.sciencedirect.com/science/article/pii/S0743731521000575

[56]  Z. Tong, J. Wang, Y. Wang, B. Liu, and Q. Li, “Energy and performance-efficient dynamic
consolidate vms using deep-q neural network,” *IEEE Transactions on Industrial Informatics*,
pp. 1–11, 2023.

[57]  M. H. Sayadnavard, A. T. Haghighat, and A. M. Rahmani, “A multi-objective approach for
energy-efficient and reliable dynamic vm consolidation in cloud data centers,” *Engineering
science and technology, an International Journal*, vol. 26, p. 100995, 2022.

[58]  J. Zhu, J. Wang, Y. Zhang, and Y. Jiang, “Virtual machine migration method based on load
cognition,” *Soft Computing*, vol. 23, no. 19, pp. 9439–9448, 2019.

[59]  H. Zhao, N. Feng, J. Li, G. Zhang, J. Wang, Q. Wang, and B. Wan, “Vm performance-aware
virtual machine migration method based on ant colony optimization in cloud environment,”
*Journal of Parallel and Distributed Computing*, vol. 176, pp. 17–27, 2023. [Online].
Available: https://www.sciencedirect.com/science/article/pii/S0743731523000187

[60]  S. E. Motaki, A. Yahyaouy, and H. Gualous, “A prediction-based model for virtual machine
live migration monitoring in a cloud datacenter,” *Computing*, vol. 103, no. 11, pp. 2711–2735,
2021.

[61]  C. Jo, Y. Cho, and B. Egger, “A machine learning approach to live migration modeling,” in
*Proceedings of the 2017 Symposium on Cloud Computing*, 2017, pp. 351–364.

[62]  C. Systems, P. L. D. of Computer Science, and S. N. U. Engineering, “Csap virtual
machine live migration dataset,” 2017, last accessed 22 December 2023. [Online]. Available:
https://csap.snu.ac.kr/software/lmdataset

[63]  M. E. Elsaid, H. M. Abbas, and C. Meinel, “Live migration timing optimization for vmware
environments using machine learning techniques.” in *CLOSER*, 2020, pp. 91–102.

[64]  M. Duggan, R. Shaw, J. Duggan, E. Howley, and E. Barrett, “A multitime-steps-ahead pre-
diction approach for scheduling live migration in cloud data centers,” *Software: Practice and
Experience*, vol. 49, no. 4, pp. 617–639, 2019.

[65]  K. Mason, M. Duggan, E. Barrett, J. Duggan, and E. Howley, “Predicting host cpu utiliza-
tion in the cloud using evolutionary neural networks,” *Future Generation Computer Systems*,
vol. 86, pp. 162–173, 2018.

[66]  M. Hassan, A. Babiker, M. Amien, and M. Hamad, “Sla management for virtual machine
live migration using machine learning with modified kernel and statistical approach,” *Engi-
neering, Technology & Applied Science Research*, vol. 8, no. 1, pp. 2459–2463, 2018.

[67]  X. Sui, D. Liu, L. Li, H. Wang, and H. Yang, “Virtual machine scheduling strategy based on
machine learning algorithms for load balancing,” *EURASIP Journal on Wireless Communi-
cations and Networking*, vol. 2019, pp. 1–16, 2019.

[68]  N. Garg, D. Singh, and M. S. Goraya, “Optimal virtual machine scheduling in virtualized
cloud environment using vikor method,” *The Journal of Supercomputing*, pp. 1–29, 2022.

[69]  Q. Gao, J. Xiao, Y. Cao, S. Deng, C. Ouyang, and Z. Feng, “Blockchain-based collaborative edge computing: efficiency, incentive and trust,” *Journal of Cloud Computing*, vol. 12, no. 1,
p. 72, 2023.

[70] S. Rathod, R. Joshi, S. Gonge, S. Pandya, T. R. Gadekallu, and A. R. Javed, “Blockchain
based simulated virtual machine placement hybrid approach for decentralized cloud and edge
computing environments,” in *Security and Risk Analysis for Intelligent Edge Computing*.
Springer, 2023, pp. 223–236.

[71] A.Wilczyn ́skiandJ.Kołodziej,“Modellingandsimulationofsecurity-awaretaskscheduling
in cloud computing based on blockchain technology,” *Simulation Modelling Practice and
Theory*, vol. 99, p. 102038, 2020. [Online]. Available: https://www.sciencedirect.com/
science/article/pii/S1569190X19301698

[72] S. Lupaiescu, P. Cioata, C. E. Turcu, O. Gherman, C. O. Turcu, and G. Paslaru,
“Centralized vs. decentralized: Performance comparison between bigchaindb and
amazon qldb,” *Applied Sciences*, vol. 13, no. 1, 2023. [Online]. Available: https:
//www.mdpi.com/2076-3417/13/1/499

[73] R. Liu, P. Yang, H. Lv, and W. Li, “Multi-objective multi-factorial evolutionary algorithm
for container placement,” *IEEE Transactions on Cloud Computing*, vol. 11, no. 2, pp. 1430–
1445, 2023.

[74] Y. Mao, W. Yan, Y. Song, Y. Zeng, M. Chen, L. Cheng, and Q. Liu, “Differentiate quality
of experience scheduling for deep learning inferences with docker containers in the cloud,”
*IEEE Transactions on Cloud Computing*, vol. 11, no. 2, pp. 1667–1677, 2023.

[75] L. Deng, Z. Wang, H. Sun, B. Li, and X. Yang, “A deep reinforcement learning-based opti-
mization method for long-running applications container deployment,” *International Journal
of Computers Communications & Control*, vol. 18, no. 4, 2023.

[76] L. Zhu, K. Huang, K. Fu, Y. Hu, and Y. Wang, “A priority-aware scheduling framework for
heterogeneous workloads in container-based cloud,” *Applied Intelligence*, vol. 53, no. 12, p.
15222–15245, nov 2022. [Online]. Available: https://doi.org/10.1007/s10489-022-04164-1

[77]  Y. Han, S. Shen, X. Wang, S. Wang, and V. C. Leung, “Tailored learning-based scheduling
for kubernetes-oriented edge-cloud system,” in *IEEE INFOCOM 2021 - IEEE Conference on
Computer Communications*, 2021, pp. 1–10.

[78]  A. Katal, T. Choudhury, and S. Dahiya, “Energy optimized container placement for cloud
data centers: a meta-heuristic approach,” *The Journal of Supercomputing*, pp. 1–43, 2023.

[79]  A. Bouaouda, K. Afdel, and R. Abounacer, “Forecasting the energy consumption of cloud
data centers based on container placement with ant colony optimization and bin packing,” in
*2022 5th Conference on Cloud and Internet of Things (CIoT)*, 2022, pp. 150–157.

[80]  S. Long, W. Wen, Z. Li, K. Li, R. Yu, and J. Zhu, “A global cost-aware container schedul-
ing strategy in cloud data centers,” *IEEE Transactions on Parallel and Distributed Systems*,
vol. 33, no. 11, pp. 2752–2766, 2021.

[81]  Y. Chen, S. He, X. Jin, Z. Wang, F. Wang, and L. Chen, “Resource utilization and cost
optimization oriented container placement for edge computing in industrial internet,” *The
Journal of Supercomputing*, vol. 79, no. 4, pp. 3821–3849, 2023.

[82]  W. Zhang, L. Chen, J. Luo, and J. Liu, “A two-stage container management in the cloud for
optimizing the load balancing and migration cost,” *Future Generation Computer Systems*,
vol. 135, pp. 303–314, 2022. [Online]. Available: https://www.sciencedirect.com/science/
article/pii/S0167739X22001674

[83]  N. Zhou, F. Dufour, V. Bode, P. Zinterhof, N. J. Hammer, and D. Kranzlmu ̈ller, “Towards
confidential computing: A secure cloud architecture for big data analytics and ai,” *arXiv
preprint arXiv:2305.17761*, 2023.

[84]  H. Song, J. Li, and H. Li, “A cloud secure storage mechanism based on data dispersion and
encryption,” *IEEE Access*, vol. 9, pp. 63 745–63 751, 2021.

[85]  J. Gabriel, S. Ankermann, M. Seidel, and F. H. Fitzek, “Transparent storage encryption in
kubernetes,” in *European Wireless 2022; 27th European Wireless Conference*. VDE, 2022,
pp. 1–6.

[86]  Q.Deng,X.Tan,J.Yang,C.Zheng,L.Wang,andZ.Xu,“Asecurecontainerplacementstrat-
egy using deep reinforcement learning in cloud,” in *2022 IEEE 25th International Conference
on Computer Supported Cooperative Work in Design (CSCWD)*, 2022, pp. 1299–1304.

[87]  T. Kong, L. Wang, D. Ma, Z. Xu, Q. Yang, and K. Chen, “A secure container deployment
strategy by genetic algorithm to defend against co-resident attacks in cloud computing,” in
*2019 IEEE 21st International Conference on High Performance Computing and Communica-
tions; IEEE 17th International Conference on Smart City; IEEE 5th International Conference
on Data Science and Systems (HPCC/SmartCity/DSS)*, 2019, pp. 1825–1832.

[88]  Z. Lei, E. Sun, S. Chen, J. Wu, and W. Shen, “A novel hybrid-copy algorithm for live migra-
tion of virtual machine,” *Future Internet*, vol. 9, no. 3, p. 37, 2017.

[89]  A. Beloglazov and R. Buyya, “Optimal online deterministic algorithms and adaptive heuris-
tics for energy and performance efficient dynamic consolidation of virtual machines in cloud
data centers,” *Concurrency and Computation: Practice and Experience*, vol. 24, no. 13, pp.
1397–1420, 2012.

[90]  Prometheus, “Prometheus - monitoring system & time series database,” 2024, last accessed 20 Jun 2024. [Online]. Available: https://prometheus.io/

[91]  G. Labs, “Grafana: The open observability platform — grafana labs,” 2024, last accessed 20
Jun 2024. [Online]. Available: https://grafana.com/

[92]  OpenStack, “Compute api - nova documentation,” 2023, last accessed 20 Jun 2024. [Online]. Available: https://docs.openstack.org/api-ref/compute/

[93]  ——, “Open source cloud computing platform software - openstack,” 2024, last accessed 20 Jun 2024. [Online]. Available: https://www.openstack.org/software/

[94]  G. Liu, J. Li, and J. Xu, “An improved min-min algorithm in cloud computing,” in *Proceed-
ings of the 2012 International Conference of Modern Computer Science and Applications*.
Springer, 2013, pp. 47–52.

[95] D. Gritto and P. Muthulakshmi, “Scheduling cloudlets in a cloud computing environment: A
priority-based cloudlet scheduling algorithm (pbcsa),” in *2022 11th International Conference
on System Modeling & Advancement in Research Trends (SMART)*. IEEE, 2022, pp. 80–86.

[96] Z. Liu and X. Wang, “A pso-based algorithm for load balancing in virtual machines of cloud
computing environment,” in *Advances in Swarm Intelligence: Third International Conference, ICSI 2012, Shenzhen, China, June 17-20, 2012 Proceedings, Part I 3*. Springer, 2012,
pp. 142–147.

[97] C.-C. Lin, P. Liu, and J.-J. Wu, “Energy-aware virtual machine dynamic provision and
scheduling for cloud computing,” in *2011 IEEE 4th International Conference on Cloud Computing*. IEEE, 2011, pp. 736–737.

[98] V.PolepallyandK.ShahuChatrapati,“Dragonflyoptimizationandconstraintmeasure-based
load balancing in cloud computing,” *Cluster Computing*, vol. 22, no. Suppl 1, pp. 1099–1111,
2019.

[99] N. Alaei and F. Safi-Esfahani, “Repro-active: a reactive–proactive scheduling method based
on simulation in cloud computing,” *The Journal of Supercomputing*, vol. 74, no. 2, pp. 801–
829, 2018.

[100] S. Supreeth, K. Patil, S. D. Patil, and S. Rohith, “Comparative approach for vm scheduling
using modified particle swarm optimization and genetic algorithm in cloud computing,” in
*2022 IEEE International Conference on Data Science and Information System (ICDSIS)*.
IEEE, 2022, pp. 1–6.

[101] Z. Xiao, X. Liu, and Z. Ming, “A deep reinforcement learning based vm scheduling strategy
decreasing data center communication costs,” in *2022 IEEE 24th Int Conf on High Perfor-
mance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int
Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems &
Application (HPCC/DSS/SmartCity/DependSys)*. IEEE, 2022, pp. 1173–1179.

[102] S. Kamat, S. Naik, S. Kanamadi, S. Alur, D. Narayan, and S. Patil, “Compute and network aware vm scheduling using reinforcement learning in cloud,” in *2023 IEEE 8th International
Conference for Convergence in Technology (I2CT)*. IEEE, 2023, pp. 1–7.

[103] I.Bhandary,K.Atul,A.Athani,S.Patil,andD.Narayan,“Energy-efficientvmschedulingin
the cloud environment using reinforcement learning,” in *2021 IEEE International Conference
on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)*. IEEE,
2021, pp. 46–51.

[104] X. Ma, H. Xu, H. Gao, M. Bian, and W. Hussain, “Real-time virtual machine scheduling
in industry iot network: a reinforcement learning method,” *IEEE Transactions on Industrial
Informatics*, vol. 19, no. 2, pp. 2129–2139, 2022.

[105] T. Daradkeh, A. Agarwal, N. Goel, and A. Kozlowski, “Point estimator log tracker for cloud
monitoring,” in *IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
Workshops (INFOCOM WKSHPS)*, 2019, pp. 62–67.

[106] Z. Lee, Y. Wang, and W. Zhou, “A dynamic priority scheduling algorithm on service request
scheduling in cloud computing,” in *Proceedings of 2011 International Conference on Elec-
tronic & Mechanical Engineering and Information Technology*, vol. 9, 2011, pp. 4665–4669.

[107] S. Network and M. HPC Group Gina Cody School, Concordia University, “Speed
hpc facility,” 2023, last accessed July 2023. [Online]. Available: https://github.com/
NAG-DevOps/speed-hpc

[108] T. C. S. Ltd., “Wanem the wide area network emulator,” 2014, last accessed April 2023.
[Online]. Available: https://wanem.sourceforge.net

[109] D. C. Montgomery and G. C. Runger, *Applied Statistics and Probability for Engineers, 6th
Edition*. Wiley, 2013.

[110] M.A.Altahat,A.Agarwal,N.Goel,andM.Zaman,“Neuralnetworkbasedregressionmodel
for virtual machines migration method selection,” in *2021 IEEE International Conference on
Communications Workshops (ICC Workshops)*. IEEE, 2021, pp. 1–6.

[111] F. Bellard, “Qemu, a fast and portable dynamic translator.” in *USENIX annual technical
conference, FREENIX Track*, vol. 41, no. 46. California, USA, 2005, pp. 10–5555.

[112] QEMU, “Qemu a generic and open source machine emulator and virtualizer,” 2023, last accessed 4 April 2024. [Online]. Available: https://www.qemu.org/

[113] J. Tang, T. Qin, Y. Xiang, Z. Zhou, and J. Gu, “Optimization search strategy for task offload-
ing from collaborative edge computing,” *IEEE Transactions on Services Computing*, vol. 16, no. 3, pp. 2044–2058, 2023.

[114] I. Attiya, M. A. Elaziz, L. Abualigah, T.N.Nguyen, and A. A. A. El-Latif, “An improved hybrid swarm intelligence for scheduling iot application tasks in the cloud,” *IEEE Transactions on Industrial Informatics*, vol. 18, no. 9, pp. 6264–6272, 2022.

[115] A. Satpathy, M. N. Sahoo, A. Mishra, B. Majhi, J. J. Rodrigues, and S. Bakshi,
“A service sustainable live migration strategy for multiple virtual machines in cloud data centers,” *Big Data Research*, vol. 25, p. 100213, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2214579621000307

[116] J. Tang, M. M. Jalalzai, C. Feng, Z. Xiong, and Y. Zhang, “Latency-aware task scheduling
in software-defined edge and cloud computing with erasure-coded storage systems,” *IEEE
Transactions on Cloud Computing*, vol. 11, no. 2, pp. 1575–1590, 2023.

[117] M. Maray, E. Mustafa, J. Shuja, and M. Bilal, “Dependent task offloading with deadline-
aware scheduling in mobile edge networks,” *Internet of Things*, vol. 23, p. 100868, 2023.
[Online]. Available: https://www.sciencedirect.com/science/article/pii/S2542660523001919

[118] J. Bi, K. Zhang, H. Yuan, and J. Zhang, “Energy-efficient computation offloading for static
and dynamic applications in hybrid mobile edge cloud system,” *IEEE Transactions on Sustainable Computing*, vol. 8, no. 2, pp. 232–244, 2023.

[119] R. W. Ahmad, A. Gani, S. H. Ab. Hamid, M. Shiraz, F. Xia, and S. A. Madani, “Virtual machine migration in cloud data centers: A review, taxonomy, and open research issues,” *J. Supercomput.*, vol. 71, no. 7, p. 2473–2515, jul 2015. [Online]. Available: https://doi.org/10.1007/s11227-015-1400-5

[120] R. Queiroz, T. Cruz, J. Mendes, P. Sousa, and P. Simo ̃es, “Container-based virtualization for
real-time industrial systems—a systematic review,” *ACM Comput. Surv.*, vol. 56, no. 3, oct
2023. [Online]. Available: https://doi.org/10.1145/3617591

	
[121] R. Gupta, P. Kanungo, and N. Dagdee, “A survey of state-of-the-art multi-authority attribute
based encryption schemes in cloud environment.” *KSII Transactions on Internet & Informa-
tion Systems*, vol. 17, no. 1, 2023.

[122] R. Imam, K. Kumar, S. M. Raza, R. Sadaf, F. Anwer, N. Fatima, M. Nadeem, M. Abbas, and O. Rahman, “A systematic literature review of attribute based encryption in health services,” *Journal of King Saud University - Computer and Information Sciences*, vol. 34, no. 9, pp. 6743–6774, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1319157822002269

[123] P. S. K. Oberko, V.-H. K. S. Obeng, and H. Xiong, “A survey on multi-authority and decen-
tralized attribute-based encryption,” *Journal of Ambient Intelligence and Humanized Computing*, pp. 1–19, 2022.

[124] M. A. Altahat, T. Daradkeh, and A. Agarwal, “Optimized encryption-integrated strategy for containers scheduling and secure migration in multi-cloud data centers,” *IEEE Access*, vol. 12, pp. 51 330–51 345, 2024.

[125] A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for
efficient management of data centers for cloud computing,” *Future Generation Computer Systems*, vol. 28, no. 5, pp. 755–768, 2012, special Section: Energy efficiency in large-scale
distributed systems. [Online]. Available: https://www.sciencedirect.com/science/article/pii/
S0167739X11000689

[126] J.Luo,Q.Liu,Y.Yang,X.Li,M.rongChen,andW.Cao,“Anartificialbeecolonyalgorithm
for multi-objective optimisation,” *Applied Soft Computing*, vol. 50, pp. 235–251, 2017.
[Online]. Available: https://www.sciencedirect.com/science/article/pii/S156849461630583X

	
[127] PyPI, “Pure python rsa implementation,” 2022, last accessed December 2023. [Online]. Available: https://pypi.org/project/rsa/

[128] B. Kaliski, “Rfc2313: Pkcs# 1: Rsa encryption version 1.5,” 1998, last accessed December 2023. [Online]. Available: https://www.rfc-editor.org/rfc/rfc2313

[129] PyCryptodome, “Aes - pycryptodome 3.210b0 documentation,” 2022, last accessed December 2023. [Online]. Available: https://pycryptodome.readthedocs.io/en/latest/src/ cipher/aes.html

[130] P. S. Foundation, “secrets — generate secure random numbers for managing secrets,” 2022, last accessed December 2023. [Online]. Available: https://docs.python.org/3/library/secrets. html

[131] PyPI, “tinyec 0.4.0,” 2021, last accessed December 2023. [Online]. Available: https: [//pypi.org/project/tinyec/](https://pypi.org/project/tinyec/)

[132] M. Tirmazi, A. Barker, N. Deng, M. E. Haque, Z. G. Qin, S. Hand, M. Harchol-Balter, and
J. Wilkes, “Borg: the next generation,” in *Proceedings of the fifteenth European conference
on computer systems*, 2020, pp. 1–14.

	
[133] Google, “Borg cluster traces from google,” 2020, last accessed 20 December 2023. [Online]. Available: https://github.com/google/cluster-data/tree/master

[134] G. OR-Tools, “Cp-sat solver,” 2023, last accessed November 2023. [Online]. Available: https://developers.google.com/optimization/cp/cp solver