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Fog Computing. Группа авторовЧитать онлайн книгу.

Fog Computing - Группа авторов


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User mobility aware task assignment for mobile edge computing. Future Generation Computer Systems 85: 1–8.

      50 50 Nasrin, W. and Xie, J. (2018). SharedMEC: sharing clouds to support user mobility in mobile edge computing. In: 2018 IEEE International Conference on Communications (ICC), 1–6. IEEE.

      51 51 Yang, J., Wen, J., Jiang, B. et al. (2018). Marine depth mapping algorithm based on the edge computing in Internet of Things. Journal of Parallel and Distributed Computing 114: 95–103.

      52 52 Jeong, S., Simeone, O., and Kang, J. (2018). Mobile edge computing via a UAV-mounted cloudlet: optimization of bit allocation and path planning. IEEE Transactions on Vehicular Technology 67 (3): 2049–2063.

      53 53 Salem, A., Salonidis, T., Desai, N., and Nadeem, T. (2017). Kinaara: distributed discovery and allocation of mobile edge resources. In: 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), 153–161. IEEE.

      54 54 Liyanage, M., Chang, C., and Srirama, S.N. (2018). Adaptive mobile web server framework for mist computing in the Internet of Things. International Journal of Pervasive Computing and Communications.

      55 55 Sucipto, K., Chatzopoulos, D., Kosta±, S., and Hui, P. (2017). Keep your nice friends close, but your rich friends closer — computation offloading using nfc. In: IEEE INFOCOM 2017 IEEE Conference on Computer Communications, 1–9.

      56 56 Characteristics of VHF radio systems and equipment for the exchange of data and electronic mail in the maritime mobile service RR appendix 18 channels, Recommendation M.1842-1, International Telecommunication Union, 2008.

      57 57 Al-Zaidi, R., Woods, J., Al-Khalidi, M. et al. (2017). Next generation marine data networks in an iot environment. In: 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), 50–55. IEEE.

      58 58 Manoufali, M., Alshaer, H., Kong, P.-Y., and Jimaa, S. (2013). Technologies and networks supporting maritime wireless mesh communications. In: Wireless and Mobile Networking Conference (WMNC), 2013 6th Joint IFIP, 1–8. IEEE.

      59 59 Bardram, A.V.T., Larsen, M.D., Malarski, K.M. et al. (2018). Lorawan capacity simulation and field test in a harbour environment. In: 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), 193–198.

      60 60 Raza, U., Kulkarni, P., and Sooriyabandara, M. (2017). Low power wide area networks: an overview. IEEE Communication Surveys and Tutorials 19: 855–873.

      61 61 Martin, B.A., Michaud, F., Banks, D. et al. (2017). Openfog security requirements and approaches. In: 2017 IEEE Fog World Congress (FWC), 1–6.

      62 62 Sui, Y., Wang, X., Pengt, M., and An, N. (2017). Optimizing mobility and energy charging for mobile cloudlet. In: 2017 IEEE International Conference on Communications (ICC), 1–6. IEEE.

      63 63 Tang, F., Fadlullah, Z.M., Mao, B. et al. (2018). On a novel adaptive UAV-mounted cloudlet-aided recommendation system for LBSNs. IEEE Transactions on Emerging Topics in Computing 7 (4): 565–577.

      64 64 Truong-Huu, T., Tham, C.-K., and Niyato, D. (2014). To offload or to wait: An opportunistic offloading algorithm for parallel tasks in a mobile cloud. In: 2014 IEEE 6th International Conference on Cloud Computing Technology and Science (CloudCom), 182–189. IEEE.

      65 65 Chang, C., Srirama, S.N., and Buyya, R. (2016). Mobile cloud business process management system for the Internet of Things: a survey. ACM Computing Surveys 49, pp. 70:1–70:42.

      66 66 Li, M., Yu, F.R., Si, P. et al. (2018). Software-defined vehicular networks with caching and computing for delay-tolerant data traffic. In: 2018 IEEE International Conference on Communications (ICC), 1–6. IEEE.

      67 67 Kumar, N., Rodrigues, J.J.P.C., Guizani, M. et al. (2018). Achieving energy efficiency and sustainability in edge/fog deployment. IEEE Communications Magazine 56: 20–21.

      68 68 Ruan, Y., Durresi, A., and Uslu, S. (2018). Trust assessment for Internet of Things in multiaccess edge computing. In: 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), –1155, 1161. IEEE.

      69 69 Wang, S., Xu, J., Zhang, N., and Liu, Y. (2018). A survey on service migration in mobile edge computing. IEEE Access 6: 23511–23528.

      70 70 Zhang, P., Zhou, M., and Fortino, G. (2018). Security and trust issues in fog computing: a survey. Future Generation Computer Systems 88: 16–27.

      71 71 Lipp, M., Schwarz, M., Gruss, D. et al. (2018). Meltdown: reading kernel memory from user space. In: 27th Security Symposium, 973–990.

      72 72 Hussain, R., Son, J., Eun, H. et al. (2012). Rethinking vehicular communications: merging vanet with cloud computing. In: 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom), 606–609. IEEE.

      73 73 Nobre, J.C., de Souza, A.M., Rosário, D. et al. (2019). Vehicular software-defined networking and fog computing: integration and design principles. Ad Hoc Networks 82: 172–181.

      Note

      1 1 www.docker.com.

       Cosmin Avasalcai, Ilir Murturi, and Schahram Dustdar

       Distributed Systems Group, TU Wien, Vienna, Austria

      In the past couple of years, the cloud computing paradigm was at the center of the Internet of Things' (IoT) ever-growing network, where companies can move their control and computing capabilities, and store collected data in a medium with almost unlimited resources [1]. It was and continues to be the best solution to deploy demanding computational applications with the main focus on processing vast amounts of data. Data are generated from geo-distributed IoT devices, such as sensors, smartphones, laptops, and vehicles, just to name a few. However, today this paradigm is facing growing challenges in meeting the demanding constraints of new IoT applications.

      With the rapid adoption of IoT devices, new use cases have emerged to improve our daily lives. Some of these new use cases are the smart city, smart home, smart grid, and smart manufacturing with the power of changing industries (i.e. healthcare, oil and gas, automotive, etc.) by improving the working environment and optimizing workflow. Since most of the use cases consist of multiple applications that require fast response time (i.e. real-time or near real-time) and improved privacy, most of the time the cloud fails to fulfill these requirements (i.e. network congestion and ensuring privacy).

      Embracing the vision of these paradigms and focusing on the deployment of multiple applications in close proximity of users, researchers have suggested new fog/edge devices. Among these devices, the most notable are mini servers, such as cloudlets [4], portable edge computers [5], and edge-cloud [6], which enable an application to work in harsh environments; mobile edge computing (MEC) [7] and mobile cloud computing [8] improve user experience and enable higher computational applications to be deployed on smartphones by offloading parts of the application on the device locally.

      Many surveys are found in the literature that describe each paradigm in detail and its challenges [3, 9, 10]. However, there is no paper that compares the two; most of the time the terms fog and edge are both used


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