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Resource Management Approaches in Fog Computing: a Comprehensive Review

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Abstract

In recent years, the Internet of Things (IoT) has been one of the most popular technologies that facilitate new interactions among things and humans to enhance the quality of life. With the rapid development of IoT, the fog computing paradigm is emerging as an attractive solution for processing the data of IoT applications. In the fog environment, IoT applications are executed by the intermediate computing nodes in the fog, as well as the physical servers in cloud data centers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictability of fog environment, it necessitates the resource management issues as one of the challenging problems to be considered in the fog landscape. Despite the importance of resource management issues, to the best of our knowledge, there is not any systematic, comprehensive and detailed survey on the field of resource management approaches in the fog computing context. In this paper, we provide a systematic literature review (SLR) on the resource management approaches in fog environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and providing open issues as well. The presented taxonomy are classified into six main fields: application placement, resource scheduling, task offloading, load balancing, resource allocation, and resource provisioning. The resource management approaches are compared with each other according to the important factors such as the performance metrics, case studies, utilized techniques, and evaluation tools as well as their advantages and disadvantages are discussed.

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References

  1. Jo, D., Kim, G.J.: IoT+ AR: pervasive and augmented environments for “Digi-log” shopping experience. Human-centric Computing and Information Sciences (HCIS). 9(1), 1 (2019)

    Article  Google Scholar 

  2. Ghobaei-Arani, M., Souri, A., Baker, T., Hussien, A.: ControCity: an autonomous approach for controlling elasticity using buffer Management in Cloud Computing Environment. IEEE (ACCESS). 7, 106912–106924 (2019)

    Article  Google Scholar 

  3. Miah, M.S., Schukat, M., Barrett, E.: An enhanced sum rate in the cluster based cognitive radio relay network using the sequential approach for the future internet of things. Human-centric Computing and Information Sciences (HCIS). 8(1), 16 (2018)

    Article  Google Scholar 

  4. Deng, Y., Chen, Z., Zhang, D., Zhao, M.: Workload scheduling toward worst-case delay and optimal utility for single-hop fog-IoT architecture. IET Commun. 12, 2164–2173 (2018)

    Article  Google Scholar 

  5. Souri, A., Asghari, P., Rezaei, R.: Software as a service based CRM providers in the cloud computing: challenges and technical issues. J. Serv. Sci. Res. 9(2), 219–237 (2017)

    Article  Google Scholar 

  6. Ghobaei-Arani, M., Shamsi, M., Rahmanian, A.A.: An efficient approach for improving virtual machine placement in cloud computing environment. J. Exp. Theor. Artif. Intell. 29(6), 1149–1171 (2017)

    Article  Google Scholar 

  7. Bonomi, F., et al. Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing. ACM (2012)

  8. Ghobaei-Arani, M., Rahmanian, A.A., Shamsi, M., Rasouli-Kenari, A.: A learning-based approach for virtual machine placement in cloud data centers. Int. J. Commun. Syst. 31(8), e3537 (2018)

    Article  Google Scholar 

  9. Ghobaei-Arani, M., Rahmanian, A.A., Aslanpour, M.S., Dashti, S.E.: CSA-WSC: cuckoo search algorithm for web service composition in cloud environments. Soft. Comput. 22(24), 8353–8378 (2018)

    Article  Google Scholar 

  10. Manasrah, A.M., Gupta, B.: An optimized service broker routing policy based on differential evolution algorithm in fog/cloud environment. Clust. Comput. 22(Supplement 1), 1639–1653 (2017)

    Google Scholar 

  11. Rabie, A.H., Ali, S.H., Ali, H.A., Saleh, A.I.: A fog based load forecasting strategy for smart grids using big electrical data. Clust. Comput. 22(1), 241–270 (2019)

    Article  Google Scholar 

  12. Mouradian, C., et al.: A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun. Surv. Tutorials. (2017)

  13. Dastjerdi, A.V., et al., Fog computing: Principles, architectures, and applications, in Internet of Things. Elsevier. p. 61–75 (2016)

  14. Jian, C., Li, M., Kuang, X.: Edge cloud computing service composition based on modified bird swarm optimization in the internet of things. Clust. Comput. (2018)

  15. Kertesz, A., T. Pflanzner, and T. Gyimothy, A Mobile IoT Device Simulator for IoT-Fog-Cloud Systems. Journal of Grid Computing. EarlyCite: p. 1–23 (2018). https://doi.org/10.1007/s10723-018-9468-9

  16. Souri, A., Norouzi, M.: A state-of-the-art survey on formal verification of the internet of things applications. J. Serv. Sci. Res. 11(1), 47–67 (2019)

    Article  Google Scholar 

  17. Ghobaei-Arani, M., et al.: A moth-flame optimization algorithm for web service composition in cloud computing: simulation and verification. Software: Practice and Experience (SPE). 48(10), 1865–1892 (2018)

    Google Scholar 

  18. Aazam, M., Zeadally, S., Harras, K.A.: Offloading in fog computing for IoT: review, enabling technologies, and research opportunities. Futur. Gener. Comput. Syst. 87, 278–289 (2018)

    Article  Google Scholar 

  19. Hong, C.-H. and B. Varghese, Resource Management in Fog/Edge Computing: A Survey. arXiv preprint arXiv:1810.00305, (2018)

  20. Masip-Bruin, X., Marin-Tordera, E., Jukan, A., Ren, G.J.: Managing resources continuity from the edge to the cloud: architecture and performance. Futur. Gener. Comput. Syst. 79, 777–785 (2018)

    Article  Google Scholar 

  21. Dias de Assunção, M., da Silva Veith, A., Buyya, R.: Distributed data stream processing and edge computing: A survey on resource elasticity and future directions. J. Netw. Comput. Appl. 103, 1–17 (2018)

    Article  Google Scholar 

  22. Toczé, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wirel. Commun. Mob. Comput. 2018, 1–23 (2018)

    Article  Google Scholar 

  23. Jatoth, C., Gangadharan, G.R., Buyya, R.: Computational intelligence based QoS-aware web service composition: a systematic literature review. IEEE Trans. Serv. Comput. 10(3), 475–492 (2017)

    Article  Google Scholar 

  24. Jafarnejad Ghomi, E., Rahmani, A.M., Qader, N.N.: Load-balancing algorithms in cloud computing: A survey. J. Netw. Comput. Appl. 88(Supplement C), 50–71 (2017)

    Article  Google Scholar 

  25. Effatparvar, M., Dehghan, M., Rahmani, A.M.: A comprehensive survey of energy-aware routing protocols in wireless body area sensor networks. J. Med. Syst. 40(9), 201 (2016)

    Article  Google Scholar 

  26. Souri, A., Rahmani, A.M.: A survey for replica placement techniques in data grid environment. International Journal of Modern Education and Computer Science (IJMECS). 6(5), 46–51 (2014)

    Article  Google Scholar 

  27. Kitchenham, B., Pretorius, R., Budgen, D., Pearl Brereton, O., Turner, M., Niazi, M., Linkman, S.: Systematic literature reviews in software engineering - a tertiary study. Inf. Softw. Technol. 52(8), 792–805 (2010)

    Article  Google Scholar 

  28. Mahmud, R., Srirama, S.N., Ramamohanarao, K., Buyya, R.: Quality of experience (QoE)-aware placement of applications in fog computing environments. J. Parallel Distrib. Comput. 132, 190–203 (2019)

    Article  Google Scholar 

  29. Skarlat, O., Nardelli, M., Schulte, S., Borkowski, M., Leitner, P.: Optimized IoT service placement in the fog. SOCA. 11(4), 427–443 (2017)

    Article  Google Scholar 

  30. Selimi, M., Cerdà-Alabern, L., Freitag, F., Veiga, L., Sathiaseelan, A., Crowcroft, J.: A lightweight service placement approach for community network micro-clouds. Journal of Grid Computing (GRID). 17(1), 169–189 (2019)

    Article  Google Scholar 

  31. Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module Management for fog Computing Environments. ACM Trans. Internet Technol. 19(1), 1–21 (2018)

    Article  Google Scholar 

  32. Velasquez, K., et al.: Service placement for latency reduction in the internet of things. Ann. Telecommun. 72(1–2), 105–115 (2016)

    Google Scholar 

  33. Naranjo, P.G.V., et al., FOCAN: A Fog-supported Smart City Network Architecture for Management of Applications in the Internet of Everything Environments. arXiv preprint arXiv:1710.01801, (2017)

  34. Yao, H., Bai, C., Xiong, M., Zeng, D., Fu, Z.: Heterogeneous cloudlet deployment and user-cloudlet association toward cost effective fog computing. Concurrency and Computation: Practice and Experience (CCPE). 29(16), e3975 (2017)

    Article  Google Scholar 

  35. Taneja, M. and A. Davy. Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm. In Integrated Network and Service Management (IM), 2017 IFIP/IEEE Symposium on. IEEE (2017)

  36. Yousefpour, A., et al., QoS-aware Dynamic Fog Service Provisioning. arXiv preprint arXiv:1802.00800 (2018)

  37. Brogi, A., Forti, S.: QoS-aware deployment of IoT applications through the fog. IEEE Internet Things J. 4(5), 1185–1192 (2017)

    Article  Google Scholar 

  38. Saurez, E., et al., Incremental deployment and migration of geo-distributed situation awareness applications in the fog. p. 258–269 (2016)

  39. Minh, Q.T., et al. Toward service placement on fog computing landscape. In Information and Computer Science, 2017 4th NAFOSTED Conference on. IEEE (2017)

  40. Yigitoglu, E., et al. Foggy: A Framework for Continuous Automated IoT Application Deployment in Fog Computing. In AI & Mobile Services (AIMS), 2017 IEEE International Conference on. IEEE (2017)

  41. Yangui, S., et al. A platform as-a-service for hybrid cloud/fog environments. In Local and Metropolitan Area Networks (LANMAN), 2016 IEEE International Symposium on. IEEE (2016)

  42. Mahmoud, M.M.E., Rodrigues, J.J.P.C., Saleem, K., al-Muhtadi, J., Kumar, N., Korotaev, V.: Towards energy-aware fog-enabled cloud of things for healthcare. Comput. Electr. Eng. 67, 58–69 (2018)

    Article  Google Scholar 

  43. Zeng, D., Gu, L., Yao, H.: Towards energy efficient service composition in green energy powered cyber–physical fog systems. Futur. Gener. Comput. Syst. (2018)

  44. Venticinque, S., Amato, A.: A methodology for deployment of IoT application in fog. J. Ambient. Intell. Humaniz. Comput. 10(5), 1955–1976 (2019)

    Article  Google Scholar 

  45. Souza, V.B., Masip-Bruin, X., Marín-Tordera, E., Sànchez-López, S., Garcia, J., Ren, G.J., Jukan, A., Juan Ferrer, A.: Towards a proper service placement in combined fog-to-cloud (F2C) architectures. Futur. Gener. Comput. Syst. 87, 1–15 (2018)

    Article  Google Scholar 

  46. Lin, C.-C., Yang, J.-W.: Cost-efficient deployment of fog computing Systems at Logistics Centers in industry 4.0. IEEE Trans. Ind. Inf. 14(10), 4603–4611 (2018)

    Article  Google Scholar 

  47. Gupta, H., et al.: 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 (SPE). 47(9), 1275–1296 (2017)

  48. Mahmud, R. and R. Buyya, Modelling and simulation of fog and edge computing environments using iFogSim toolkit. Fog and Edge Computing: Principles and Paradigms. p. 1–35 (2019)

  49. Ghobaei-Arani, M., Khorsand, R., Ramezanpour, M.: An autonomous resource provisioning framework for massively multiplayer online games in cloud environment. J. Netw. Comput. Appl. 142(18), 76–97 (2019)

    Article  Google Scholar 

  50. Sun, Y., Lin, F., Xu, H.: Multi-objective optimization of resource scheduling in fog computing using an improved NSGA-II. Wirel. Pers. Commun. 102(2), 1369–1385 (2018)

    Article  Google Scholar 

  51. Bitam, S., Zeadally, S., Mellouk, A.: Fog computing job scheduling optimization based on bees swarm. Enterprise Information Systems (EIS). 12(4), 373–397 (2017)

    Article  Google Scholar 

  52. Cardellini, V., et al. On QoS-aware scheduling of data stream applications over fog computing infrastructures. In Computers and Communication (ISCC), 2015 IEEE Symposium on. IEEE (2015)

  53. De Benedetti, M., et al.: JarvSis: a distributed scheduler for IoT applications. Clust. Comput. 20(2), 1775–1790 (2017)

    Article  MathSciNet  Google Scholar 

  54. Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S.: Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Trans. Comput. 65(12), 3702–3712 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  55. Fan, J., et al. Deadline-Aware Task Scheduling in a Tiered IoT Infrastructure. in GLOBECOM 2017–2017 IEEE Global Communications Conference. Singapore: IEEE (2017)

  56. Rahbari, D. and M. Nickray. Scheduling of Fog Networks with Optimized Knapsack by Symbiotic Organisms Search. In 2017 21st Conference of Open Innovations Association (FRUCT). Finland: IEEE (2017)

  57. Pham, X.-Q. and E.-N. Huh. Towards task scheduling in a cloud-fog computing system. In Network Operations and Management Symposium (APNOMS), 2016 18th Asia-Pacific. IEEE (2016)

  58. Kabirzadeh, S., D. Rahbari, and M. Nickray, A Hyper Heuristic Algorithm for Scheduling of Fog Networks. algorithms. 19: p. 20 (2017)

  59. Sun, Y., Zhang, N.: A resource-sharing model based on a repeated game in fog computing. Saudi journal of biological sciences (SJBS). 24(3), 687–694 (2017)

    Article  Google Scholar 

  60. Hoang, D. and T.D. Dang, FBRC: Optimization of task Scheduling in Fog-Based Region and Cloud. 2017: p. 1109–1114

  61. Chen, X., Wang, L.: Exploring fog computing-based adaptive vehicular data scheduling policies through a compositional formal method—PEPA. IEEE Commun. Lett. 21(4), 745–748 (2017)

    Article  Google Scholar 

  62. Urgaonkar, R., Wang, S., He, T., Zafer, M., Chan, K., Leung, K.K.: Dynamic service migration and workload scheduling in edge-clouds. Perform. Eval. 91, 205–228 (2015)

    Article  Google Scholar 

  63. Bittencourt, L.F., Diaz-Montes, J., Buyya, R., Rana, O.F., Parashar, M.: Mobility-aware application scheduling in fog computing. IEEE Technical Committee on Cloud Computing (TCCLD). 4(2), 26–35 (2017)

  64. Deng, R., et al.: Optimal workload allocation in fog-cloud computing towards balanced delay and power consumption. IEEE Internet Things J. 3(6), 1171–1181 (2016)

    Google Scholar 

  65. Tran, D.H., Tran, N.H., Pham, C., Kazmi, S.M.A., Huh, E.N., Hong, C.S.: OaaS: offload as a service in fog networks. Computing. 99(11), 1081–1104 (2017)

    Article  MathSciNet  Google Scholar 

  66. Mukherjee, A., Deb, P., de, D., Buyya, R.: C2OF2N: a low power cooperative code offloading method for femtolet-based fog network. J. Supercomput. 74(6), 2412–2448 (2018)

    Article  Google Scholar 

  67. Liu, L., Chang, Z., Guo, X., Mao, S., Ristaniemi, T.: Multiobjective optimization for computation offloading in fog computing. IEEE Internet Things J. 5(1), 283–294 (2018)

    Article  Google Scholar 

  68. Wang, X., Ning, Z., Wang, L.: Offloading in internet of vehicles: a fog-enabled real-time traffic management system. IEEE Trans. Ind. Inf. 14(10), 4568–4578 (2018)

    Article  Google Scholar 

  69. Liu, L., Z. Chang, and X. Guo, Socially-aware Dynamic Computation Offloading Scheme for Fog Computing System with Energy Harvesting Devices. IEEE Internet Things J.. p. 1–1 (2018)

  70. Xu, J. and S. Ren. Online learning for offloading and autoscaling in renewable-powered mobile edge computing. In Global Communications Conference (GLOBECOM), 2016 IEEE. IEEE (2016)

  71. Zhao, X., L. Zhao, and K. Liang. An Energy Consumption Oriented Offloading Algorithm for Fog Computing. In International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness. Springer (2016)

  72. Ye, D., et al., Scalable Fog Computing with Service Offloading in Bus Networks. p. 247–251 (2016)

  73. Meng, X., Wang, W., Zhang, Z.: Delay-constrained hybrid computation offloading with cloud and fog computing. IEEE (ACCESS). 5, 21355–21367 (2017)

    Article  Google Scholar 

  74. Nan, Y., Li, W., Bao, W., Delicato, F.C., Pires, P.F., Zomaya, A.Y.: A dynamic tradeoff data processing framework for delay-sensitive applications in cloud of things systems. J. Parallel Distrib. Comput. 112, 53–66 (2018)

    Article  Google Scholar 

  75. Chamola, V., C.-K. Tham, and G.S. Chalapathi. Latency aware mobile task assignment and load balancing for edge cloudlets. In Pervasive Computing and Communications Workshops (PerCom Workshops), 2017 IEEE International Conference on. IEEE (2017)

  76. Alam, M.G.R., Y.K. Tun, and C.S. Hong. Multi-agent and reinforcement learning based code offloading in mobile fog. In Information Networking (ICOIN), 2016 International Conference on. IEEE (2016)

  77. Khan, J.A., C. Westphal, and Y. Ghamri-Doudane. Offloading Content with Self-organizing Mobile Fogs. In Teletraffic Congress (ITC 29), 2017 29th International. IEEE (2017)

  78. Ahn, S., M. Gorlatova, and M. Chiang. Leveraging fog and cloud computing for efficient computational offloading. In Undergraduate Research Technology Conference (URTC), 2017 IEEE MIT. IEEE (2017)

  79. Bozorgchenani, A., D. Tarchi, and G.E. Corazza. An Energy-Aware Offloading Clustering Approach (EAOCA) in fog computing. In Wireless Communication Systems (ISWCS), 2017 International Symposium on. IEEE (2017)

  80. Zhu, Q., Si, B., Yang, F., Ma, Y.: Task offloading decision in fog computing system. China Communications (Chinacom). 14(11), 59–68 (2017)

    Article  Google Scholar 

  81. Chang, Z., et al. Energy Efficient Optimization for Computation Offloading in Fog Computing System. In GLOBECOM 2017-2017 IEEE Global Communications Conference. IEEE (2017)

  82. Bozorgchenani, A., D. Tarchi, and G.E. Corazza. An Energy and Delay-Efficient Partial Offloading Technique for Fog Computing Architectures. In GLOBECOM 2017-2017 IEEE Global Communications Conference. IEEE (2017)

  83. Bao, W., et al. Cost-Effective Processing in Fog-Integrated Internet of Things Ecosystems. In Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems. ACM (2017)

  84. Liang, K., Zhao, L., Zhao, X., Wang, Y., Ou, S.: Joint resource allocation and coordinated computation offloading for fog radio access networks. China Communications (Chinacom). 13(2), 131–139 (2016)

    Article  Google Scholar 

  85. Perala, S.S.N., I. Galanis, and I. Anagnostopoulos. Fog Computing and Efficient Resource Management in the era of Internet-of-Video Things (IoVT). In Circuits and Systems (ISCAS), 2018 IEEE International Symposium on. IEEE (2018)

  86. Chen, X., Jiao, L., Li, W., Fu, X.: Efficient multi-user computation offloading for Mobile-edge cloud computing. IEEE/ACM Trans. Networking. 24(5), 2795–2808 (2016)

    Article  Google Scholar 

  87. Kattepur, A., et al. Resource constrained offloading in fog computing. In Proceedings of the 1st Workshop on Middleware for Edge Clouds & Cloudlets. ACM (2016)

  88. Xiong, Z., et al.: Cloud/fog computing resource management and pricing for blockchain networks. IEEE Internet Things J. 6(3), 4585–4600 (2018)

    Article  Google Scholar 

  89. Li, C., Zhuang, H., Wang, Q., Zhou, X.: SSLB: self-similarity-based load balancing for large-scale fog computing. Arab. J. Sci. Eng. 43(12), 7487–7498 (2018)

    Article  Google Scholar 

  90. Manasrah, A.M., A.a. Aldomi, and B.B. Gupta, An optimized service broker routing policy based on differential evolution algorithm in fog/cloud environment. Cluster Computing, (2017)

  91. Beraldi, R., A. Mtibaa, and H. Alnuweiri. Cooperative load balancing scheme for edge computing resources. In Fog and Mobile Edge Computing (FMEC), 2017 Second International Conference on. IEEE (2017)

  92. Shi, C., Z. Ren, and X. He, Research on Load Balancing for Software Defined Cloud-Fog Network in Real-Time Mobile Face Recognition. 210: p. 121–131 (2018)

  93. He, X., Ren, Z., Shi, C., Fang, J.: A novel load balancing strategy of software-defined cloud/fog networking in the internet of vehicles. China Communications (Chinacom). 13(2), 140–149 (2016)

    Article  Google Scholar 

  94. Ningning, S., Chao, G., Xingshuo, A., Qiang, Z.: Fog computing dynamic load balancing mechanism based on graph repartitioning. China Communications (Chinacom). 13(3), 156–164 (2016)

    Article  Google Scholar 

  95. Yu, Y., X. Li, and C. Qian. SDLB: A Scalable and Dynamic Software Load Balancer for Fog and Mobile Edge Computing. In Proceedings of the Workshop on Mobile Edge Communications. ACM (2017)

  96. Oueis, J., E.C. Strinati, and S. Barbarossa. The fog balancing: Load distribution for small cell cloud computing. In Vehicular Technology Conference (VTC Spring), 2015 IEEE 81st. IEEE (2015)

  97. Neto, E.C.P., G. Callou, and F. Aires. An algorithm to optimise the load distribution of fog environments. In Systems, Man, and Cybernetics (SMC), 2017 IEEE International Conference on. . IEEE (2017)

  98. Kapsalis, A., Kasnesis, P., Venieris, I.S., Kaklamani, D.I., Patrikakis, C.Z.: A cooperative fog approach for effective workload balancing. IEEE Cloud Computing. 4(2), 36–45 (2017)

    Article  Google Scholar 

  99. Verma, S., et al. An efficient data replication and load balancing technique for fog computing environment. In Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on. IEEE (2016)

  100. Gu, L., Zeng, D., Guo, S., Barnawi, A., Xiang, Y.: Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Trans. Emerg. Top. Comput. 5(1), 108–119 (2017)

    Article  Google Scholar 

  101. Xu, X., Fu, S., Cai, Q., Tian, W., Liu, W., Dou, W., Sun, X., Liu, A.X.: Dynamic resource allocation for load balancing in fog environment. Wirel. Commun. Mob. Comput. 2018, 1–15 (2018)

    Google Scholar 

  102. Ni, L., Zhang, J., Jiang, C., Yan, C., Yu, K.: Resource allocation strategy in fog computing based on priced timed petri nets. IEEE Internet Things J. 4(5), 1216–1228 (2017)

    Article  Google Scholar 

  103. Zhang, H., Xiao, Y., Bu, S., Niyato, D., Yu, F.R., Han, Z.: Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining Stackelberg game and matching. IEEE Internet Things J. 4(5), 1204–1215 (2017)

    Article  Google Scholar 

  104. Alsaffar, A.A., Pham, H.P., Hong, C.S., Huh, E.N., Aazam, M.: An architecture of IoT service delegation and resource allocation based on collaboration between fog and cloud computing. Mob. Inf. Syst. 2016, 1–15 (2016)

    Google Scholar 

  105. Zhang, Y., et al., Resource Allocation in Software Defined Fog Vehicular Networks. 2017: p. 71–76

  106. Do, C.T., et al. A proximal algorithm for joint resource allocation and minimizing carbon footprint in geo-distributed fog computing. In Information Networking (ICOIN), 2015 International Conference on. IEEE (2015)

  107. Xu, J., et al. Zenith: Utility-aware resource allocation for edge computing. In Edge Computing (EDGE), 2017 IEEE International Conference on. IEEE (2017)

  108. Aazam, M., et al., IoT resource estimation challenges and modeling in fog, in Fog Computing in the Internet of Things, Springer. p. 17–31 (2018)

  109. Zhang, H., Zhang, Y., Gu, Y., Niyato, D., Han, Z.: A hierarchical game framework for resource management in fog computing. IEEE Commun. Mag. 55(8), 52–57 (2017)

    Article  Google Scholar 

  110. Sood, S.K., Singh, K.D.: SNA based resource optimization in optical network using fog and cloud computing. Opt. Switch. Netw. 33(July), 114–121 (2017)

    Google Scholar 

  111. Kochar, V. and A. Sarkar. Real time resource allocation on a dynamic two level symbiotic fog architecture. In Embedded Computing and System Design (ISED), 2016 Sixth International Symposium on. IEEE (2016)

  112. Naranjo, P.G., et al.: Fog over virtualized IoT: new opportunity for context-aware networked applications and a case study. Appl. Sci. 7(12), 1325 (2017)

    Article  Google Scholar 

  113. Jiao, Y., et al.: Auction mechanisms in cloud/fog computing resource allocation for public Blockchain networks. IEEE Trans. Parallel Distrib. Syst. 30(9), 1975–1989 (2018)

    Article  Google Scholar 

  114. Ali, M., Riaz, N., Ashraf, M.I., Qaisar, S., Naeem, M.: Joint cloudlet selection and latency minimization in fog networks. IEEE Trans. Ind. Inf. 14(9), 4055–4063 (2018)

    Article  Google Scholar 

  115. Nguyen, D.T., L.B. Le, and V. Bhargava, Price-based Resource Allocation for Edge Computing: A Market Equilibrium Approach. arXiv preprint arXiv:1805.02982, (2018)

  116. Zhang, W., Zhang, Z., Chao, H.-C.: Cooperative fog computing for dealing with big data in the internet of vehicles: architecture and hierarchical resource management. IEEE Commun. Mag. 55(12), 60–67 (2017)

    Article  Google Scholar 

  117. Anglano, C., M. Canonico, and M. Guazzone. Profit-aware resource management for edge computing systems. In Proceedings of the 1st International Workshop on Edge Systems, Analytics and Networking. ACM (2018)

  118. El Kafhali, S., Salah, K.: Efficient and dynamic scaling of fog nodes for IoT devices. J. Supercomput. 73(12), 5261–5284 (2017)

    Article  Google Scholar 

  119. Tseng, F.-H., Tsai, M.S., Tseng, C.W., Yang, Y.T., Liu, C.C., Chou, L.D.: A lightweight auto-scaling mechanism for fog computing in industrial applications. IEEE Trans. Ind. Inf. 14(10), 4529–4537 (2018)

    Article  Google Scholar 

  120. Arkian, H.R., Diyanat, A., Pourkhalili, A.: MIST: fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications. J. Netw. Comput. Appl. 82, 152–165 (2017)

    Article  Google Scholar 

  121. Wang, N., et al., ENORM: A Framework For Edge NOde Resource Management. IEEE Transactions on Services Computing. Early access: p. 1–1 (2017)

  122. Dos Santos, X., et al. Resource provisioning for IoT application services in Smart Cities. in CNSM2017, the 13e International Conference on Network and Service Management. (2017)

  123. Skarlat, O., et al. Resource provisioning for IoT services in the fog. In Service-Oriented Computing and Applications (SOCA), 2016 IEEE 9th International Conference on. IEEE (2016)

  124. Östberg, P.-O., et al. Reliable capacity provisioning for distributed cloud/edge/fog computing applications. In Networks and Communications (EuCNC), 2017 European Conference on. IEEE (2017)

  125. Vinueza Naranjo, P.G., E. Baccarelli, and M. Scarpiniti, Design and energy-efficient resource management of virtualized networked Fog architectures for the real-time support of IoT applications. J. Supercomput., 2018. 74(6): p. 2470–2507

  126. Zanni, A., et al. Elastic Provisioning of Internet of Things Services Using Fog Computing: An Experience Report. In 2018 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud). IEEE (2018)

  127. Russo Russo, G., Nardelli, M., Cardellini, V., Lo Presti, F.: Multi-level elasticity for wide-area data streaming systems: a reinforcement learning approach. Algorithms. 11(9), 134 (2018)

    Article  MATH  Google Scholar 

  128. Pešić, S., et al. Context aware resource and service provisioning management in fog computing systems. In International Symposium on Intelligent and Distributed Computing. Springer (2017)

  129. Souri, A., Navimipour, N.J., Rahmani, A.M.: Formal verification approaches and standards in the cloud computing: a comprehensive and systematic review. Computer Standards & Interfaces (CSI). 58, 1–22 (2018)

  130. Keshanchi, B., Souri, A., Navimipour, N.J.: An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J. Syst. Softw. 124, 1–21 (2017)

    Article  Google Scholar 

  131. Souri, A., Navimipour, N.J.: Behavioral modeling and formal verification of a resource discovery approach in grid computing. Expert Syst. Appl. 41(8), 3831–3849 (2014)

    Article  Google Scholar 

  132. Souri, A. and M. Norouzi. A new probable decision making approach for verification of probabilistic real-time systems. In Software Engineering and Service Science (ICSESS), 2015 6th IEEE International Conference on. IEEE (2015)

  133. Arunkumar, G., Venkataraman, N.: A novel approach to address interoperability concern in cloud computing. Procedia Computer Science. 50, 554–559 (2015)

    Article  Google Scholar 

  134. Rezaei, R., Chiew, T.K., Lee, S.P., Shams Aliee, Z.: A semantic interoperability framework for software as a service systems in cloud computing environments. Expert Syst. Appl. 41(13), 5751–5770 (2014)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran

    Mostafa Ghobaei-Arani

  2. Young Researchers and Elite Club, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran

    Mostafa Ghobaei-Arani & Alireza Souri

  3. Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands

    Ali A. Rahmanian

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  1. Mostafa Ghobaei-Arani
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  2. Alireza Souri
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  3. Ali A. Rahmanian
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Correspondence to Mostafa Ghobaei-Arani.

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Ghobaei-Arani, M., Souri, A. & Rahmanian, A.A. Resource Management Approaches in Fog Computing: a Comprehensive Review. J Grid Computing 18, 1–42 (2020). https://doi.org/10.1007/s10723-019-09491-1

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