[2] A. Botta, W. De Donato, V. Persico, and A. Pescapé, "Integration of cloud computing and internet of things: a survey," Future generation computer systems, vol. 56, pp. 684-700, 2016.
[3] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, "Fog computing and its role in the internet of things," in Proceedings of the first edition of the MCC workshop on Mobile cloud computing, 2012, pp. 13-16.
[4] P. Hosseinioun, M. Kheirabadi, S. R. Kamel Tabbakh, and R. Ghaemi, "aTask scheduling approaches in fog computing: A survey," Transactions on Emerging Telecommunications Technologies, p. e3792, 2020.
[5] M. R. Alizadeh, V. Khajehvand, A. M. Rahmani, and E. Akbari, "Task scheduling approaches in fog computing: A systematic review," International Journal of Communication Systems, vol. 33, no. 16, p. e4583, 2020.
[6] S. K. Mishra, D. Puthal, J. J. Rodrigues, B. Sahoo, and E. Dutkiewicz, "Sustainable service allocation using a metaheuristic technique in a fog server for industrial applications," IEEE Transactions on Industrial Informatics, vol. 14, no. 10, pp. 4497-4506, 2018.
[7] B. M. Nguyen, H. Thi Thanh Binh, and B. Do Son, "Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud–fog computing environment," Applied Sciences, vol. 9, no. 9, p. 1730, 2019.
[8] M. Abdel-Basset, D. El-shahat, M. Elhoseny, and H. Song, "Energy-Aware Metaheuristic algorithm for Industrial Internet of Things task scheduling problems in fog computing applications," IEEE Internet of Things Journal, 2020.
[9] F. Hoseiny, S. Azizi, and S. Dabiri, "Using the Power of Two Choices for Real-Time Task Scheduling in Fog-Cloud Computing," in 2020 4th International Conference on Smart City, Internet of Things and Applications (SCIOT), 2020: IEEE, pp. 18-23.
[10] S. Ghanavati, J. H. Abawajy, and D. Izadi, "An Energy Aware Task Scheduling Model Using Ant-Mating Optimization in Fog Computing Environment," IEEE Transactions on Services Computing, 2020.
[11] S. Bitam, S. Zeadally, and A. Mellouk, "Fog computing job scheduling optimization based on bees swarm," Enterprise Information Systems, vol. 12, no. 4, pp. 373-397, 2018.
[12] N. Auluck, A. Azim, and K. Fizza, "Improving the schedulability of real-time tasks using fog computing," IEEE Transactions on Services Computing, 2019.
[13] R. O. Aburukba, M. AliKarrar, T. Landolsi, and K. El-Fakih, "Scheduling Internet of Things requests to minimize latency in hybrid Fog–Cloud computing," Future Generation Computer Systems, vol. 111, pp. 539-551, 2020.
[14] S. Javanmardi, M. Shojafar, V. Persico, and A. Pescape, "FPFTS: A Joint Fuzzy PSO Mobility-aware Approach to Fog Task Scheduling Algorithm for IoT Devices," Software Practice and Experience, 2020.
[15] Z. Zhou, H. Xie, and F. Li, "A novel task scheduling algorithm integrated with priority and greedy strategy in cloud computing," Journal of Intelligent & Fuzzy Systems, vol. 37, no. 4, pp. 4647-4655, 2019.
[16] A. Moaddeli, I. N. Ahmadi, and N. Abhar, "The Power of d Choices in Scheduling for Data Centers with Heterogeneous Servers," arXiv preprint arXiv:1904.00447, 2019.
[17] F. Hoseiny, S. Azizi, M. Shojafar, and R. Tafazolli, "Joint QoS-aware and Cost-efficient Task Scheduling for Fog-Cloud Resources in a Volunteer Computing System," ACM Transaction on Internet Technology, vol. 21, no. 4, pp. 1-21, 2021.
[18] R. Deng, R. Lu, C. Lai, T. H. Luan, and H. Liang, "Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption," IEEE internet of things journal, vol. 3, no. 6, pp. 1171-1181, 2016.
[19] B. Wang, C. Wang, Y. Song, J. Cao, X. Cui, and L. Zhang, "A survey and taxonomy on workload scheduling and resource provisioning in hybrid clouds," Cluster Computing, pp. 1-26, 2020.
[20] J. Konečný, H. B. McMahan, D. Ramage, and P. Richtárik, "Federated optimization: Distributed machine learning for on-device intelligence," arXiv preprint arXiv:1610.02527, 2016.
[21] C. You, K. Huang, H. Chae, and B.-H. Kim, "Energy-efficient resource allocation for mobile-edge computation offloading," IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1397-1411, 2016.
[22] Y. Donoso and R. Fabregat, Multi-objective optimization in computer networks using metaheuristics. CRC Press, 2016.
[23] R. Beraldi, H. Alnuweiri, and A. Mtibaa, "A power-of-two choices based algorithm for fog computing," IEEE Transactions on Cloud Computing, vol. 8, no. 3, pp. 698-709, 2018.
[24] R. Beraldi and G. P. Mattia, "Power of random choices made efficient for fog computing," IEEE Transactions on Cloud Computing, 2020.
[25] A. M. Sampaio, J. G. Barbosa, and R. Prodan, "PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers," Simulation Modelling Practice and Theory, vol. 57, pp. 142-160, 2015.
[27] H. Gupta, A. Vahid Dastjerdi, S. K. Ghosh, and R. Buyya, "iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments," Software: Practice and Experience, vol. 47, no. 9, pp. 1275-1296, 2017.
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