참고문헌
- Ghobaei-Arani, M., Souri, A., & Rahmanian, A. A. ( 2020 ). Resource management approaches in fog computing: a comprehensive review. Journal of Grid Computing, 18 ( 1 ), 1-42. https://doi.org/10.1007/s10723-019-09491-1
- 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 ) https://doi.org/10.1080/0952813X.2017.1310308
- 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 ) https://doi.org/10.1007/s12927-017-0011-5
- 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 ) https://doi.org/10.1007/s00500-017-2783-4
- 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 ) https://doi.org/10.1002/dac.3537
- 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 ) https://doi.org/10.1007/s10586-017-1559-z
- Mouradian, C., et al.: A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun. Surv. Tutorials. ( 2017 )
- Hong, C.-H. and B. Varghese, Resource Management in Fog/Edge Computing: A Survey. arXiv preprint arXiv: 1810.00305, ( 2018 ) https://doi.org/10.1145/3326066
- 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 ) https://doi.org/10.1016/j.future.2018.04.057
- 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 ) https://doi.org/10.1016/j.future.2017.09.036
- Tocze, K., Nadjm-Tehrani, S.: A taxonomy for management and optimization of multiple resources in edge computing. Wirel. Commun. Mob. Comput. 2018, 1-23 ( 2018 ) https://doi.org/10.1155/2018/7476201
- Dias de Assuncao, 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 ) https://doi.org/10.1016/j.jnca.2017.12.001
- Hong, C.-H. and B. Varghese, Resource Management in Fog/Edge Computing: A Survey. arXiv preprint arXiv: 1810.00305, ( 2018 ) https://doi.org/10.1145/3326066
- Naha, R.K.; Garg, S.; Georgakopoulos, D.; Jayaraman, P.P.; Gao, L.; Xiang, Y.; Ranjan, R. Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions. IEEE Access 2018, 6, 47980-48009 https://doi.org/10.1109/access.2018.2866491
- Bendechache, M.; Svorobej, S.; Takako Endo, P.; Lynn, T. Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions. Future Internet 2020, 12, 95 https://doi.org/10.3390/fi12060095
- Aslanpour, M.S.; Gill, S.S.; Toosi, A.N. Performance evaluation metrics for cloud, fog, and edge computing: A review, taxonomy, benchmarks and standards for future research. Internet Things 2020, 12, 100273 https://doi.org/10.1016/j.iot.2020.100273
- Salaht, F.A.; Desprez, F.; Lebre, A. An Overview of Service Placement Problem in Fog and Edge Computing. ACM Comput. Surv. 2020, 53, 1-35 https://doi.org/10.1145/3391196
- Agarwal, S.; Yadav, S.; Yadav, A.K. An efficient architecture and algorithm for resource provisioning in fog computing. Int. J. Inf. Eng. Electronic Bus. ( IJIEEB ) 2016, 8, 48-61 https://doi.org/10.5815/ijieeb.2016.01.06
- 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 ) https://doi.org/10.1007/s12927-019-0003-8
- 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 ) https://doi.org/10.1002/spe.2598
- Dastjerdi, A.V., et al., Fog computing: Principles, architectures, and applications, in Internet of Things. Elsevier. p. 61-75 ( 2016 )
- Mijuskovic, A., Chiumento, A., Bemthuis, R., Aldea, A., & Havinga, P. ( 2021 ). Resource management techniques for cloud/fog and edge computing: An evaluation framework and classification. Sensors, 21 ( 5 ), 1832. https://doi.org/10.3390/s21051832
- Javaid, S.; Javaid, N.; Saba, T.; Wadud, Z.; Rehman, A.; Haseeb, A. Intelligent resource allocation in residential buildings using consumer to fog to cloud based framework. Energies 2019, 12, 815 https://doi.org/10.3390/en12050815
- 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, 2018
- Taneja, M.; Davy, A. Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm. In Proceedings of the 2017 IFIP/IEEE Symposium on Integrated Network and Service Management ( IM ), Lisbon, Portugal, 8-12 May 2017; pp. 1222-1228
- Skarlat, O., Nardelli, M., Schulte, S., Borkowski, M., Leitner, P.: Optimized IoT service placement in the fog. SOCA. 11 ( 4 ), 427-443 ( 2017 ) https://doi.org/10.1007/s11761-017-0219-8
- Venticinque, S., Amato, A.: A methodology for deployment of IoT application in fog . J . Ambient. Intell. Humaniz. Comput. 10 ( 5 ), 1955-1976 ( 2019 ) https://doi.org/10.1007/s12652-018-0785-4
- 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 ) https://doi.org/10.1016/j.compeleceng.2018.02.047
- 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 )
- 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 )
- Minh, Q.T., et al. Toward service placement on fog computing landscape. In Information and Computer Science, 2017 4th NAFOSTED Conference on. IEEE ( 2017 )
- Saurez, E., et al., Incremental deployment and migration of geo distributed situation awareness applications in the fog. p. 258-269 ( 2016 )
- Brogi, A., Forti, S.: QoS-aware deployment of IoT applications through the fog. IEEE Internet Things J. 4 ( 5 ), 1185-1192 ( 2017 ) https://doi.org/10.1109/JIOT.2017.2701408
- 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 ) https://doi.org/10.1002/cpe.3975
- Yousefpour, A., et al., QoS-aware Dynamic Fog Service Provisioning. arXiv preprint arXiv:1802.00800 ( 2018 )
- Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency aware application module Management for fog Computing Environments. ACM Trans. Internet Technol. 19 ( 1 ), 1-21 ( 2018 )
- 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 )
- 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 ) https://doi.org/10.1016/j.jpdc.2018.03.004
- Velasquez, K., et al.: Service placement for latency reduction in the internet of things. Ann. Telecommun. 72 ( 1-2 ), 105-115 ( 2016 ) https://doi.org/10.1007/s12243-016-0524-9
- Selimi, M., Cerda-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 ) https://doi.org/10.1007/s10723-018-9437-3
- Bitam, S., Zeadally, S., Mellouk, A.: Fog computing job scheduling optimization based on bees swarm. Enterprise Information Systems ( EIS ). 12 ( 4 ), 373-397 ( 2017 ) https://doi.org/10.1080/17517575.2017.1304579
- 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 ) https://doi.org/10.1007/s11277-017-5200-5
- De Benedetti, M., et al.: JarvSis: a distributed scheduler for IoT applications. Clust. Comput. 20 ( 2 ), 1775-1790 ( 2017 ) https://doi.org/10.1007/s10586-017-0836-1
- 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 )
- 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 )
- 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 ) https://doi.org/10.1109/TC.2016.2536019
- 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 )
- Fan, J., et al. Deadline-Aware Task Scheduling in a Tiered IoT Infrastructure. in GLOBECOM 2017-2017 IEEE Global Communications Conference. Singapore: IEEE ( 2017 )
- Sun, Y., Zhang, N.: A resource-sharing model based on a repeated game in fog computing. Saudi journal of biolog ical sciences ( SJBS ). 24 ( 3 ), 687-694 ( 2017 ) https://doi.org/10.1016/j.sjbs.2017.01.043
- 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 ) https://doi.org/10.1109/LCOMM.2016.2647595
- 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 ) https://doi.org/10.1109/JIOT.2016.2565516
- Hoang, D. and T.D. Dang, FBRC: Optimization of task Scheduling in Fog-Based Region and Cloud. 2017: p. 1109-1114
- 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 net- works. Computing. 99 ( 11 ), 1081-1104 ( 2017 ) https://doi.org/10.1007/s00607-017-0551-z
- 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 ) https://doi.org/10.1109/jiot.2017.2780236
- Mukherjee, A., Deb, P., de, D., Buyya, R.: C2OF2N: a low power cooperative code offloading method for femtolet- based fog network. J. Super comput. 74 ( 6 ), 2412-2448 ( 2018 ) https://doi.org/10.1007/s11227-018-2269-x
- Wang, X., Ning, Z., Wang, L.: Offloading in internet of vehicles: a fog-enabled real-time traffic management sys- tem. IEEE Trans. Ind. Inf. 14 ( 10 ), 4568-4578 ( 2018 ) https://doi.org/10.1109/tii.2018.2816590
- Xu, J. and S. Ren. Online learning for offloading and auto scaling in renewable- powered mobile edge computing. In Global Communications Conference ( GLOBECOM ), 2016 IEEE. IEEE ( 2016 )
- 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 )
- Ye, D., et al., Scalable Fog Computing with Service Offloading in Bus Networks. p. 247-251 ( 2016 )
- 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 )
- Nan, Y., Li, W., Bao, W., Delicato, F.C., Pires, P.F., Zomaya, A.Y.: A dynamic tradeoff data processing frame- work for delay-sensitive applications in cloud of things systems. J. Parallel Distrib. Comput. 112, 53-66 ( 2018 ) https://doi.org/10.1016/j.jpdc.2017.09.009
- Meng, X., Wang, W., Zhang, Z.: Delay-constrained hybrid computation offloading with cloud and fog computing. IEEE ( ACCESS ). 5, 21355-21367 ( 2017 ) https://doi.org/10.1109/ACCESS.2017.2748140
- 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 )
- 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 )
- 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 )
- 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 )
- 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 )
- Zhu, Q., Si, B., Yang, F., Ma, Y.: Task offloading decision in fog computing system. China Communications ( Chinacom ). 14 ( 11 ), 59-68 ( 2017 ) https://doi.org/10.1109/cc.2017.8233651
- 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 ) https://doi.org/10.1109/TNET.2015.2487344
- Chang, Z., et al. Energy Efficient Optimization for Computation Offloading in Fog Computing System. In GLOBECOM 2017-2017 IEEE Global Communications Conference. IEEE ( 2017 )
- Kattepur, A., et al. Resource constrained offloading in fog computing. In Proceedings of the 1st Workshop on Middleware for Edge Clouds & Cloudlets. ACM ( 2016 )
- 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 )
- Xiong, Z., et al.: Cloud/fog computing resource manage- ment and pricing for blockchain networks. IEEE Internet Things J. 6 ( 3 ), 4585-4600 ( 2018 ) https://doi.org/10.1109/jiot.2018.2871706
- 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 ) https://doi.org/10.1007/s13369-018-3169-3
- 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 )
- Manasrah, A.M., A.a. Aldomi, and B.B. Gupta, An opti- mized service broker routing policy based on differential evolution algorithm in fog/cloud environment. Cluster Computing, ( 2017 )
- 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 ) https://doi.org/10.1109/CC.2016.7405730
- 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 )
- 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 ) https://doi.org/10.1109/CC.2016.7445510
- Oueis, J., E.C. Strinati, and S. Barbarossa. The fog balancing: Load distribution for small cell cloud comput- ing. In Vehicular Technology Conference ( VTC Spring ), 2015 IEEE 81st. IEEE ( 2015 )
- 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 )
- 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 )
- Gu, L., Zeng, D., Guo, S., Barnawi, A., Xiang, Y.: Cost efficient resource management in fog computing supported medical cyberphysical system. IEEE Trans. Emerg. Top. Comput. 5 ( 1 ), 108-119 ( 2017 ) https://doi.org/10.1109/TETC.2015.2508382
- 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 ) https://doi.org/10.1109/MCC.2017.25
- 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 )
- 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 )
- 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 ) https://doi.org/10.1109/JIOT.2017.2709814
- Zhang, Y., et al., Resource Allocation in Software Defined Fog Vehicular Networks. 2017: p. 71-76
- Zhang, H., Xiao, Y., Bu, S., Niyato, D., Yu, F.R., Han, Z.: Computing resource allocation in three-tier IoT fog net- works: a joint optimization approach combining Stackelberg game and matching. IEEE Internet Things J. 4 ( 5 ), 1204-1215 ( 2017 ) https://doi.org/10.1109/JIOT.2017.2688925
- Do, C.T., et al. A proximal algorithm for joint re- source allocation and minimizing carbon footprint in geo-distributed fog computing. In Information Networking ( ICOIN ), 2015 International Conference on. IEEE ( 2015 )
- Alsaffar,A.A.,Pham,H.P.,Hong,C.S.,Huh,E.N.,Aazam, M.: An architecture of IoTservice delegation and resource allocation based on collaboration between fog and cloud computing. Mob. Inf. Syst. 2016, 1-15 ( 2016 )
- 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 ) https://doi.org/10.1109/MCOM.2017.1600896
- 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 )
- 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 ) https://doi.org/10.1016/j.osn.2017.12.007
- Naranjo, P.G., et al.: Fog over virtualized IoT: new oppor- tunity for context-aware networked applications and a case study. Appl. Sci. 7 ( 12 ), 1325 ( 2017 ) https://doi.org/10.3390/app7121325
- 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 )
- Jiao,Y.,etal.:Auction mechanisms in cloud / fog computing resource allocation for public Block chain networks. IEEE Trans. Parallel Distrib. Syst. 30 ( 9 ), 1975-1989 ( 2018 ) https://doi.org/10.1109/tpds.2019.2900238
- El Kafhali, S., Salah, K.: Efficient and dynamic scaling of fog nodes for IoT devices. J. Super comput. 73 ( 12 ), 5261-5284 ( 2017 ) https://doi.org/10.1007/s11227-017-2083-x
- Wang, N., et al., ENORM: A Framework For Edge Node Resource Management. IEEE Transactions on Services Computing. Early access: p. 1-1 ( 2017 )
- 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 https://doi.org/10.1109/tii.2018.2799230
- Dos Santos, X., et al. Resource provisioning for IoT appli- cation services in Smart Cities. in CNSM2017, the 13e International Conference on Network and Service Management. ( 2017 )
- 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 ) https://doi.org/10.1016/j.jnca.2017.01.012
- 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. Super comput., 2018. 74 ( 6 ): p. 2470-2507 https://doi.org/10.1007/s11227-018-2274-0
- Ostberg, 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 )
- 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 )
- Skarlat, O., et al. Resource provisioning for IoTservices in the fog. In Service-Oriented Computing and Applications ( SOCA ), 2016 IEEE 9th International Conference on. IEEE ( 2016 )
- 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 ) https://doi.org/10.3390/a11090134