Abstract
Fog computing is an emerging paradigm that deals with distributing data and computation at intermediate layers between the cloud and the edge. Cloud computing was introduced to support the increasing computing requirements of users. Later, it was observed that end users experienced a delay involved in uploading the large amounts of data to the cloud for processing. Such a seemingly centralized approach did not provide a good user experience. To overcome this limitation, processing capability was incorporated in devices at the edge. This led to the rise of edge computing. This paradigm suffered because edge devices had limited capability in terms of computing resources and storage requirements. Relying on these edge devices alone was not sufficient. Thus, a paradigm was needed without the delay in uploading to the cloud and without the resource availability constraints. This is where fog computing came into existence. This abstract paradigm involves the establishment of fog nodes at different levels between the edge and the cloud. Fog nodes can be different entities, such as personal computers (PCs). There are different realms where fog computing may be applied, such as vehicular networks and the Internet of Things. In all realms, resource management decisions will vary based on the environmental conditions. This chapter attempts to classify the various approaches for managing resources in the fog environment based on their application realm, and to identify future research directions.
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Ahmed, A., and E. Ahmed. 2016. A survey on mobile edge computing. In IEEE 10th International Conference on Intelligent Systems and Control (ISCO), 1–8.
Arkian, H.R., A. Diyanat, and A. Pourkhalili. 2017. Mist: Fog-based data analytics scheme with cost-efficient resource provisioning for iot crowdsensing applications. Journal of Network and Computer Applications 82: 152–165.
Barcelo, M., A. Correa, J. Llorca, A.M. Tulino, J.L. Vicario, and A. Morell. 2016. Iot-cloud service optimization in next generation smart environments. IEEE Journal on Selected Areas in Communications 34 (12): 4077–4090.
Byers, C.C. 2015. Fog computing distributing data and intelligence for resiliency and scale necessary for iot the internet of things.
Chen, X., and L. Wang. 2017. Exploring fog computing-based adaptive vehicular data scheduling policies through a compositional formal methodpepa. IEEE Communications Letters 21 (4): 745–748.
Dao, N.N., J. Lee, D.N. Vu, J. Paek, J. Kim, S. Cho, K.S. Chung, and C. Keum. 2017. Adaptive resource balancing for serviceability maximization in fog radio access networks. IEEE Access 5: 14548–14559.
Dastjerdi, A.V., and R. Buyya. 2016. Fog computing: helping the internet of things realize its potential. Computer 49 (8): 112–116.
Datta, S.K., C. Bonnet, and J. Haerri. 2015. Fog computing architecture to enable consumer centric internet of things services. In IEEE International Symposium on Consumer Electronics (ISCE), 1–2.
Deng, R., R. Lu, C. Lai, T.H. Luan, and H. Liang. 2016. Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet of Things Journal 3 (6): 1171–1181.
Do, C.T., N.H. Tran, C. Pham, M.G.R. Alam, J.H. Son, and C.S. Hong. 2015. A proximal algorithm for joint resource allocation and minimizing carbon footprint in geo-distributed fog computing. In IEEE International Conference on Information Networking (ICOIN), 324–329.
Fernando, N., S.W. Loke, and W. Rahayu. 2013. Mobile cloud computing: a survey. Future Generation Computer Systems 29 (1): 84–106.
Gu, L., D. Zeng, S. Guo, A. Barnawi, and Y. Xiang. 2017. Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Transactions on Emerging Topics in Computing 5 (1): 108–119.
He, Z., Z. Cai, J. Yu, X. Wang, Y. Sun, and Y. Li. 2017. Cost-efficient strategies for restraining rumor spreading in mobile social networks. IEEE Transactions on Vehicular Technology 66 (3): 2789–2800.
Jingtao, S., L. Fuhong, Z. Xianwei, and L. Xing. 2015. Steiner tree based optimal resource caching scheme in fog computing. China Communications 12 (8): 161–168.
Khan, S., S. Parkinson, and Y. Qin. 2017. Fog computing security: a review of current applications and security solutions. Journal of Cloud Computing 6 (1): 19.
Networking, C.V. 2017. Ciscoglobal cloud index: forecast and methodology, 2015–2020. white paper
Ni, L., J. Zhang, C. Jiang, C. Yan, and K. Yu. 2017. Resource allocation strategy in fog computing based on priced timed petri nets. IEEE Internet of Things Journal.
Ning, H., H. Liu, J. Ma, L.T. Yang, and R. Huang. 2016. Cybermatics: cyber-physical-social-thinking hyperspace based science and technology. Future Generation Computer Systems 56: 504–522.
Peng, M., and K. Zhang. 2016. Recent advances in fog radio access networks: performance analysis and radio resource allocation. IEEE Access 4: 5003–5009.
Qiu, T., R. Qiao, and D. Wu. 2017. Eabs: An event-aware backpressure scheduling scheme for emergency internet of things. IEEE Transactions on Mobile Computing.
Tordera, E.M., X. Masip-Bruin, J. García-Almiñana, A. Jukan, G.J. Ren, and J. Zhu. 2017. Do we all really know what a fog node is? current trends towards an open definition. Computer Communications.
Wang, W., Q. Wang, and K. Sohraby. 2017. Multimedia sensing as a service (msaas): Exploring resource saving potentials of at cloud-edge iot and fogs. IEEE Internet of Things Journal 4 (2): 487–495.
Weinhardt, C., A. Anandasivam, B. Blau, N. Borissov, T. Meinl, W. Michalk, and J. Stößer. 2009. Cloud computing-a classification, business models, and research directions. Business and Information Systems Engineering 1 (5): 391–399.
Wen, Z., R. Yang, P. Garraghan, T. Lin, J. Xu, and M. Rovatsos. 2017. Fog orchestration for internet of things services. IEEE Internet Computing 21 (2): 16–24.
Zeng, D., L. Gu, S. Guo, Z. Cheng, and S. Yu. 2016. Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers 65 (12): 3702–3712.
Zhang, Y., D. Niyato, and P. Wang. 2015. Offloading in mobile cloudlet systems with intermittent connectivity. IEEE Transactions on Mobile Computing 14 (12): 2516–2529.
Zhu, J., D.S. Chan, M.S. Prabhu, P. Natarajan, H. Hu, and F. Bonomi. 2013. Improving web sites performance using edge servers in fog computing architecture. IEEE 7th International Symposium on Service Oriented System Engineering (SOSE), 320–323.
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Martin, J.P., Kandasamy, A., Chandrasekaran, K. (2019). Unraveling the Challenges for the Application of Fog Computing in Different Realms: A Multifaceted Study. In: Krishna, A., Srikantaiah, K., Naveena, C. (eds) Integrated Intelligent Computing, Communication and Security. Studies in Computational Intelligence, vol 771. Springer, Singapore. https://doi.org/10.1007/978-981-10-8797-4_49
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