Skip to main content

A Manifesto for Modern Fog and Edge Computing: Vision, New Paradigms, Opportunities, and Future Directions

  • Chapter
  • First Online:
Operationalizing Multi-Cloud Environments

Abstract

The advancements in the use of Internet of Things (IoT) devices is increasing continuously and generating huge amounts of data in a fast manner. Cloud computing is an important paradigm which processes and manages user data effectively. Further, fog and edge computing paradigms are introduced to improve user service by reducing latency and response time. This chapter presents a manifesto for modern fog and edge computing systems based on the current research trends. Further, architectures and applications of fog and edge computing are explained. Moreover, research opportunities and promising future directions are presented with respect to the new paradigms, which will be helpful for practitioners, researchers, and academicians to continue their research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gill, S. S., Tuli, S., Xu, M., Singh, I., Singh, K. V., Lindsay, D., Tuli, S., Smirnova, D., Singh, M., Jain, U., & Pervaiz, H. (2019). Transformative effects of IoT, Blockchain and artificial intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet of Things, 8, 100118.

    Article  Google Scholar 

  2. Aslanpour, M. S., Gill, S. S., & Toosi, A. N. (2020). Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research. Internet of Things, 11, 100273.

    Article  Google Scholar 

  3. Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing (pp. 13–16).

    Google Scholar 

  4. Yi, S., Li, C., & Li, Q. (2015). A survey of fog computing: Concepts, applications and issues. In Proceedings of the 2015 workshop on mobile big data (pp. 37–42).

    Google Scholar 

  5. Yi, S., Hao, Z., Qin, Z., & Li, Q. (2015). Fog computing: Platform and applications. In the Third IEEE workshop on hot topics in web systems and technologies (HotWeb) (pp. 73–78). IEEE.

    Google Scholar 

  6. Vaquero, L. M., & Rodero-Merino, L. (2014, October 10). Finding your way in the fog: Towards a comprehensive definition of fog computing. ACM SIGCOMM Computer Communication Review, 44(5), 27–32.

    Article  Google Scholar 

  7. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.

    Article  Google Scholar 

  8. Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30–39.

    Article  Google Scholar 

  9. Shi, W., & Dustdar, S. (2016). The promise of edge computing. Computer, 49(5), 78–81.

    Article  Google Scholar 

  10. Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2017). Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1), 450–465.

    Article  Google Scholar 

  11. Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys & Tutorials, 19(4), 2322–2358.

    Article  Google Scholar 

  12. Hu, Y. C., Patel, M., Sabella, D., Sprecher, N., & Young, V. (2015). Mobile edge computing—A key technology towards 5G. ETSI White Paper, 11(11), 1–6.

    Google Scholar 

  13. Khan, W. Z., Ahmed, E., Hakak, S., Yaqoob, I., & Ahmed, A. (2019). Edge computing: A survey. Future Generation Computer Systems, 97, 219–235.

    Article  Google Scholar 

  14. Tuli, S., Basumatary, N., Gill, S. S., Kahani, M., Arya, R. C., Wander, G. S., & Buyya, R. (2020). Healthfog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environments. Future Generation Computer Systems, 104, 187–200.

    Article  Google Scholar 

  15. Gill, S. S., Arya, R. C., Wander, G. S., & Buyya, R. (2018). Fog-based smart healthcare as a big data and cloud service for heart patients using IoT. In International Conference on Intelligent Data Communication Technologies and Internet of Things (pp. 1376–1383). Springer.

    Google Scholar 

  16. Tuli, S., Tuli, S., Wander, G., Wander, P., Gill, S. S., Dustdar, S., Sakellariou, R., & Rana, O. (2020). Next generation technologies for smart healthcare: Challenges, vision, model, trends and future directions. Internet Technology Letters, 3(2), e145.

    Article  Google Scholar 

  17. Singh, S., Chana, I., & Buyya, R. (2020). Agri-info: Cloud based autonomic system for delivering agriculture as a service. Internet of Things, 9, 100131.

    Article  Google Scholar 

  18. Gill, S. S., Chana, I., & Buyya, R. (2017). IoT based agriculture as a cloud and big data service: The beginning of digital India. Journal of Organizational and End User Computing (JOEUC), 29(4), 1–23.

    Article  Google Scholar 

  19. Gill, S. S., Garraghan, P., & Buyya, R. (2019). ROUTER: Fog enabled cloud based intelligent resource management approach for smart home IoT devices. Journal of Systems and Software, 154, 125–138.

    Article  Google Scholar 

  20. Bansal, K., Mittal, K., Ahuja, G., Singh, A., & Gill, S. S. (2020). DeepBus: Machine learning based real time pothole detection system for smart transportation using IoT. Internet Technology Letters, 3(3), e156.

    Article  Google Scholar 

  21. Olivares-Rojas, J. C., Reyes-Archundia, E., Gutiérrez-Gnecchi, J. A., Molina-Moreno, I., Méndez-Patiño, A., & Cerda-Jacobo, J. (2020). Forecasting electricity consumption using weather data in an edge-fog-cloud data analytics architecture. In International conference on P2P, parallel, grid, cloud and internet computing (pp. 410–419). Springer.

    Google Scholar 

  22. Krishnan, R., Singh, M., Vellore, R., & Mujumdar, M. (2020). Progress and prospects in weather and climate modelling. arXiv preprint arXiv:2011.11353. 2020 November 23.

    Google Scholar 

  23. Hong, C. H., & Varghese, B. (2019). Resource management in fog/edge computing: A survey on architectures, infrastructure, and algorithms. ACM Computing Surveys (CSUR), 52(5), 1–37.

    Article  Google Scholar 

  24. Varshney, P., & Simmhan, Y. (2017). Demystifying fog computing: Characterizing architectures, applications and abstractions. In IEEE 1st international conference on fog and edge computing (ICFEC) (pp. 115–124). IEEE.

    Google Scholar 

  25. Omoniwa, B., Hussain, R., Javed, M. A., Bouk, S. H., & Malik, S. A. (2018). Fog/edge computing-based IoT (FECIoT): Architecture, applications, and research issues. IEEE Internet of Things Journal, 6(3), 4118–4149.

    Article  Google Scholar 

  26. Gill, S. S., Chana, I., Singh, M., & Buyya, R. (2018). Chopper: An intelligent QoS-aware autonomic resource management approach for cloud computing. Cluster Computing, 21(2), 1203–1241.

    Article  Google Scholar 

  27. Singh, S., Chana, I., & Singh, M. (2017). The journey of QoS-aware autonomic cloud computing. IT Professional, 19(2), 42–49.

    Article  Google Scholar 

  28. Singh, S., & Chana, I. (2016). QoS-aware autonomic resource management in cloud computing: A systematic review. ACM Computing Surveys (CSUR), 48(3), 1–46.

    Article  Google Scholar 

  29. Singh, S., & Chana, I. (2015). Q-aware: Quality of service based cloud resource provisioning. Computers & Electrical Engineering, 47, 138–160.

    Article  Google Scholar 

  30. Zhou, Q., Xu, M., Gill, S. S., Gao, C., Tian, W., Xu, C., & Buyya, R. (2020). Energy efficient algorithms based on VM consolidation for cloud computing: comparisons and evaluations. Proceedings of the 20th IEEE/ACM international symposium on cluster, cloud, and internet computing (CCGrid 2020, IEEE CS Press, USA), Melbourne, Australia, May 11–14, 2020.

    Google Scholar 

  31. Malik SU, Akram H, Gill SS, Pervaiz H, Malik H. (2020) Effort: Energy efficient framework for offload communication in mobile cloud computing. Software: Practice and Experience.

    Google Scholar 

  32. Gill, S. S., Garraghan, P., Stankovski, V., Casale, G., Thulasiram, R. K., Ghosh, S. K., Ramamohanarao, K., & Buyya, R. (2019). Holistic resource management for sustainable and reliable cloud computing: An innovative solution to global challenge. Journal of Systems and Software, 155, 104–129.

    Article  Google Scholar 

  33. Gill, S. S., & Buyya, R. (2018). A taxonomy and future directions for sustainable cloud computing: 360 degree view. ACM Computing Surveys (CSUR), 51(5), 1–33.

    Article  Google Scholar 

  34. Puthal, D., Obaidat, M. S., Nanda, P., Prasad, M., Mohanty, S. P., & Zomaya, A. Y. (2018). Secure and sustainable load balancing of edge data centers in fog computing. IEEE Communications Magazine, 56(5), 60–65.

    Article  Google Scholar 

  35. Singh, S., & Chana, I. (2016). A survey on resource scheduling in cloud computing: Issues and challenges. Journal of Grid Computing, 14(2), 217–264.

    Article  Google Scholar 

  36. Gill, S. S., & Buyya, R. (2019). Resource provisioning based scheduling framework for execution of heterogeneous and clustered workloads in clouds: From fundamental to autonomic offering. Journal of Grid Computing, 17(3), 385–417.

    Article  Google Scholar 

  37. Gill, S. S., Chana, I., Singh, M., & Buyya, R. (2019). RADAR: Self-configuring and self-healing in resource management for enhancing quality of cloud services. Concurrency and Computation: Practice and Experience, 31(1), –e4834.

    Google Scholar 

  38. Gill, S. S., Buyya, R., Chana, I., Singh, M., & Abraham, A. (2018). Bullet: Particle swarm optimization based scheduling technique for provisioned cloud resources. Journal of Network and Systems Management, 26(2), 361–400.

    Article  Google Scholar 

  39. Gill, S. S., & Buyya, R. (2018). Failure management for reliable cloud computing: A taxonomy, model and future directions. Computing in Science & Engineering.

    Google Scholar 

  40. Sharif, A., Nickray, M., & Shahidinejad, A. (2020). Fault-tolerant with load balancing scheduling in a fog-based IoT application. IET Communications, 14(16), 2646–2657.

    Article  Google Scholar 

  41. Grover, J, & Garimella, R. M. (2018). Reliable and fault-tolerant IoT-edge architecture. In IEEE sensors (pp. 1–4). IEEE.

    Google Scholar 

  42. Díaz-de-Arcaya, J., Miñon, R., & Torre-Bastida, A. I. (2019). Towards an architecture for big data analytics leveraging edge/fog paradigms. In Proceedings of the 13th European conference on software architecture (Vol. 2, pp. 173–176).

    Google Scholar 

  43. Krishnan, P., Duttagupta, S., & Achuthan, K. (2020). SDN/NFV security framework for fog-to-things computing infrastructure. Software: Practice and Experience, 50(5), 757–800.

    Google Scholar 

  44. Golec, M., Gill, S. S., Bahsoon, R., & Rana, O. (2020). BioSec: A biometric authentication framework for secure and private communication among edge devices in IoT and industry 4.0. IEEE Consumer Electronics Magazine.

    Google Scholar 

  45. Gill, S. S., & Shaghaghi, A. (2020). Security-aware autonomic allocation of cloud resources: A model, research trends, and future directions. Journal of Organizational and End User Computing (JOEUC), 32(3), 15–22.

    Article  Google Scholar 

  46. Gill, S. S., & Buyya, R. (2018). Secure: Self-protection approach in cloud resource management. IEEE Cloud Computing, 5(1), 60–72.

    Article  Google Scholar 

  47. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017). A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet of Things Journal, 4(5), 1125–1142.

    Article  Google Scholar 

  48. Yi, S., Qin, Z., & Li, Q. (2015). Security and privacy issues of fog computing: A survey. In International conference on wireless algorithms, systems, and applications (pp. 685–695). Springer.

    Chapter  Google Scholar 

  49. Gill, S. S., & Buyya, R. (2019). Bio-inspired algorithms for big data analytics: A survey, taxonomy, and open challenges. In Big data analytics for intelligent healthcare management (pp. 1–17). Academic.

    Google Scholar 

  50. Badidi, E., Mahrez, Z., & Sabir, E. (2020). Fog computing for smart cities’ big data management and analytics: A review. Future Internet, 12(11), 190.

    Article  Google Scholar 

  51. Hussain, M. M., Beg, M. S., & Alam, M. S. (2020). Fog computing for big data analytics in IoT aided smart grid networks. Wireless Personal Communications, 114(4), 3395–3418.

    Article  Google Scholar 

  52. Tuli, S., Gill, S. S., Casale, G., & Jennings, N. R. (2020). iThermoFog: IoT-fog based automatic thermal profile creation for cloud data centers using artificial intelligence techniques. Internet Technology Letters, 3(5), e198.

    Article  Google Scholar 

  53. Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing (pp. 13–16).

    Google Scholar 

  54. Rahmani, A. M., Liljeberg, P., Preden, J. S., & Jantsch, A. (Eds.). (2017). Fog computing in the internet of things: Intelligence at the edge. Springer.

    Google Scholar 

  55. Morabito, R. (2017). Virtualization on internet of things edge devices with container technologies: A performance evaluation. IEEE Access, 5, 8835–8850.

    Article  Google Scholar 

  56. Soo, S., Chang, C., Loke, S. W., & Srirama, S. N. (2018). Proactive mobile fog computing using work stealing: Data processing at the edge. In Fog computing: breakthroughs in research and practice (pp. 264–283). IGI global.

    Google Scholar 

  57. Bierzynski, K., Escobar, A., & Eberl, M. (2017). Cloud, fog and edge: Cooperation for the future?. In Second international conference on fog and mobile edge computing (FMEC) (pp. 62–67). IEEE.

    Google Scholar 

  58. Gill, S. S., & Buyya, R. (2019). Sustainable cloud computing realization for different applications: A manifesto. In Digital business (pp. 95–117). Springer.

    Chapter  Google Scholar 

  59. Pore, M., Chakati, V., Banerjee, A., & Gupta, S. K. (2019). Middleware for fog and edge computing: Design issues. In Fog and edge computing: principles and paradigms. Wiley.

    Google Scholar 

  60. Baldini, I., Castro, P., Chang, K., Cheng, P., Fink, S., Ishakian, V., Mitchell, N., Muthusamy, V., Rabbah, R., Slominski, A., & Suter, P. (2017). Serverless computing: Current trends and open problems. In Research advances in cloud computing (pp. 1–20). Springer.

    Google Scholar 

  61. McGrath, G., & Brenner, P. R. (2017). Serverless computing: Design, implementation, and performance. In IEEE 37th international conference on distributed computing systems workshops (ICDCSW) (pp. 405–410). IEEE.

    Google Scholar 

  62. Fox, G. C., Ishakian, V., Muthusamy, V., & Slominski, A. (2017). Status of serverless computing and function-as-a-service (faas) in industry and research. arXiv preprint arXiv:1708.08028.

    Google Scholar 

  63. Bouraga, S. (2020). A taxonomy of blockchain consensus protocols: A survey and classification framework. Expert Systems with Applications. 114384.

    Google Scholar 

  64. Deepa, N., Pham, Q. V., Nguyen, D. C., Bhattacharya, S., Gadekallu, T. R., Maddikunta, P. K., Fang, F, & Pathirana, P. N. (2020). A survey on Blockchain for big data: Approaches, opportunities, and future directions. arXiv preprint arXiv:2009.00858.

    Google Scholar 

  65. Ankenbrand, T., Bieri, D., Cortivo, R., Hoehener, J., & Hardjono, T. (2020). Proposal for a comprehensive (crypto) asset taxonomy. In2020 Crypto Valley conference on Blockchain technology (CVCBT) (pp. 16–26). IEEE.

    Google Scholar 

  66. Kreutz, D., Ramos, F. M., Verissimo, P. E., Rothenberg, C. E., Azodolmolky, S., & Uhlig, S. (2014). Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103(1), 14–76.

    Article  Google Scholar 

  67. Duan, Y., Li, W., Fu, X., Luo, Y., & Yang, L. (2017). A methodology for reliability of WSN based on software defined network in adaptive industrial environment. IEEE/CAA Journal of Automatica Sinica, 5(1), 74–82.

    Article  Google Scholar 

  68. Dhillon, A., Singh, A., Vohra, H., Ellis, C., Varghese, B., & Gill, S. S. (2020). IoTPulse: Machine learning-based enterprise health information system to predict alcohol addiction in Punjab (India) using IoT and fog computing. Enterprise Information Systems. 1–33.

    Google Scholar 

  69. Li, L., Ota, K., & Dong, M. (2018). Deep learning for smart industry: Efficient manufacture inspection system with fog computing. IEEE Transactions on Industrial Informatics, 14(10), 4665–4673.

    Article  Google Scholar 

  70. Bachiega, N. G., Souza, P. S., Bruschi, S. M., & De Souza, S. D. (2018) Container-based performance evaluation: A survey and challenges. In IEEE international conference on cloud engineering (IC2E) (pp. 398–403). IEEE.

    Google Scholar 

  71. Gill, S. S., Kumar, A., Singh, H., Singh, M., Kaur, K., Usman, M., & Buyya, R. (2020). Quantum computing: A taxonomy, systematic review and future directions. arXiv preprint arXiv:2010.15559.

    Google Scholar 

  72. Aslanpour, M. S., Toosi, A. N., Cicconetti, C., Javadi, B., Sbarski, P., Taibi, D., Assuncao, M., Gill, S. S., Gaire, R., & Dustdar, S. (2021). Serverless edge computing: Vision and challenges. Proceedings of the 19th Australasian Symposium on Parallel and Distributed Computing (AusPDC 2021), Dunedin, New Zealand.

    Google Scholar 

  73. Nawaz, F., Ibrahim, J., Awais, M., Junaid, M., Kousar, S., & Parveen, T. (2020). A review of vision and challenges of 6G technology. International Journal of Advanced Computer Science and Applications, 11(2).

    Google Scholar 

  74. Stergiou, C. L., Psannis, K. E., & Gupta, B. B. (2020). IoT-based big data secure management in the fog over a 6G wireless network. IEEE Internet of Things Journal.

    Google Scholar 

  75. Sengupta, J., Ruj, S., & Bit, S. D. (2020). A secure fog based architecture for industrial internet of things and industry 4.0. IEEE Transactions on Industrial Informatics.

    Google Scholar 

  76. Abdullah, M., Iqbal, W., Mahmood, A., Bukhari, F., & Erradi, A. (2020). Predictive autoscaling of microservices hosted in fog microdata center. IEEE Systems Journal.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sukhpal Singh Gill .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gill, S.S. (2022). A Manifesto for Modern Fog and Edge Computing: Vision, New Paradigms, Opportunities, and Future Directions. In: Nagarajan, R., Raj, P., Thirunavukarasu, R. (eds) Operationalizing Multi-Cloud Environments. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-74402-1_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-74402-1_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-74401-4

  • Online ISBN: 978-3-030-74402-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics