Skip to main content

Abstract

Cloud computing provides users with access to system resources on demand. Before the advent of Cloud computing, users could run applications or programs from software downloaded to their computer or server. Cloud computing allows access to the same applications through the Internet. cloud computing has brought down the cost involved in computing. Users can avail resources and services according to their need. This ability to access information anywhere, anyhow, and at any time has positively impacted migration to the Cloud by organizations. With the advancement of cloud computing, there is a paradigm shift is in data storage and usage of remote applications. Adding mobility to computing, cloud has made data and applications to move out of physical buildings. So, data are no more confined. It is available on the go. By adding these features, the cloud also facilitates reliability, efficiency, and scalability. The demand of the cloud is exponentially growing with invention of the high-end and sophisticated devices. The cloud services are transparent and easy. Hence, it addresses the increasing demand easily. Cloud computing architecture is responsible for the distribution of cloud computing services that involve numerous cloud computing constituents, which communicate between them using a technique such as messaging line. The problem of managing the resources in the cloud still needs more innovations as the problems still persist. The cloud computing architecture of a cloud solution is the structure of the system, which involves premise and cloud resources, services, middleware, and software. In cloud computing, resource management comprises of provisioning, allocation, and monitoring. Cloud resources consist of the servers, memory, storage, network, CPU, application servers, and cybernetic systems otherwise called virtual machines. These machines are the processing units in cloud. Virtualization provides solutions for managing the cloud resources. The performance of any system depends on the effective management of resources. The resource management in cloud computing systems encompasses to manage the large number of virtual machines and physical machines (Vashistha, A., Kumar, S., Verma, P., Porwal, R. A self-adaptive view on resource management in cloud data center. IEEE Computer Society, 2018). Cloud architectures are constructed as software applications that use Internet accessible on-demand services. The applications in cloud architectures make use of the computing infrastructure when it is needed. It requests the necessary resources on demand, accomplishes a prescribed job, then releases the unneeded resources, and often disposes them after the job is done.

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 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
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. Alex, Y. A. G. (2017). Comparison of resource optimization algorithms in cloud computing. International Journal of Pure and Applied Mathematics, 847–854.

    Google Scholar 

  2. An architectural blueprint for autonomic computing. (2005). Autonomic computing white paper. Third Edition: IBM Press.

    Google Scholar 

  3. Vashistha, A., Kumar, S., Verma, P., Porwal, R. (2018). A self-adaptive view on resource management in cloud data center. IEEE Computer Society.

    Google Scholar 

  4. Singh, B. K., Alemu, D. P. S. M., & Adane, A. (2020). Cloud-based outsourcing framework for efficient IT project management practices. (IJACSA) International Journal of Advanced Computer Science and Applications, 11(9), 114–152.

    Google Scholar 

  5. Tomar, R., Khanna, A., Bansal, A., Fore, V. (2018). An architectural view towards autonomic cloud computing. Data engineering and intelligent computing (pp. 573–582).

  6. Yadav, A. K., Tomar, R., Kumar, D., Gupta, H. (2012). Security and privacy concerns in cloud computing. Computer Science and Software Engineering.

  7. Lee, Y. C., & Zomaya, A. Y. (2012). Energy efficient utilization of resources in cloud computing systems. The Journal of Supercomputing, 60(2), 268–280.

    Article  Google Scholar 

  8. Biography of Leonard Kleinrock. IEEE Computer Society (2019).

    Google Scholar 

  9. Sheshasaayee, A., & Megala, R. (2017). A study on resource provisioning approaches in autonomic cloud computing. In International conference on I-SMAC (IoT in social, mobile, analytics and cloud) (I-SMAC 2017) (pp. 141–147). IEEE Publications.

    Chapter  Google Scholar 

  10. Mell, P., Grance, T. (2011). The NIST definition of cloud computing. Computer security. NIST Special Publication 800–145.

    Google Scholar 

  11. Sukhpal Singh Gill, Peter Garraghan, Vlado Stankovski, Giuliano Casale, Ruppa K. Thulasiram , Soumya K. Ghosh, Ramamohanarao, K., Buyya, R. (2019). Holistic resource management for sustainable and reliable cloud computing: An innovative solution to global challenge. The Journal of Systems and Software, Elsevier Publications. 102–127.

    Google Scholar 

  12. Dehraj, P., & Sharma, A. (2021). A review on architecture and models for autonomic software systems. The Journal of Supercomputing, Springer Nature, 77, 388–417.

    Article  Google Scholar 

  13. Pompili, D., Hajisami, A., & Tran, T. X. (2016). Elastic resource utilization framework for high capacity and energy efficiency in cloud RAN. IEEE Communications Magazine, 54(1), 26–32.

    Article  Google Scholar 

  14. Felter, W., Ferreira, A., Rajamony, R., Rubio, J. (2015). An updated performance comparison of virtual machines and linux container. 2015 IEEE International Symposium on in performance analysis of systems and software, (ISPASS) (pp. 171–172).

    Google Scholar 

  15. Beloglazov, A., & Buyya, R. (2013). Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Transactions on Parallel and Distributed Systems, 24(7), 1366–1379.

    Article  Google Scholar 

  16. Coutinho, E. F., Gomes, D. G., & Neuman de Souza, J. (2015). An autonomic computing-based architecture for cloud computing elasticity. In network operations and management symposium (LANOMS) (pp. 111–112).

    Google Scholar 

  17. Tesfatsion, S. K., Wadbro, E., & Tordsson, J. (2018). PerfGreen: Performance and energy aware resource provisioning for heterogeneous cloud. IEEE international conference on autonomic computing, IEEE Computer Society.

    Google Scholar 

  18. Vieira, K., Koch, F. L., Sobral, J. B. M., Westphall, C. B., de Souza Leão, J. L. (2019). Autonomic intrusion detection and response using big data. IEEE Systems. https://doi.org/10.1109/JSYST.2019.2945555.

  19. Vuksanović, D., Ugarak, J., Korčok, D. (2016). Industry 4.0: The future concepts and new visions of factory of the future development. International scientific conference on ICT and E-business related research. Advanced Engineering Systems (pp. 293–298).

    Google Scholar 

  20. Rojko, A. (2017). Industry 4.0 concept: Background and overview. International Journal of Interactive Mobile Technologies (iJIM).

    Google Scholar 

  21. Alcácer, V., & Cruz-Machado, V. (2019). Scanning the industry 4.0: A literature review on Technologies for Manufacturing Systems. Engineering Science and Technology, 22, 899–919.

    Google Scholar 

  22. Zhou, K., Liu, T., Zhou, L. (2015). Industry 4.0: Towards future industrial opportunities and challenges. 12th international conference on fuzzy systems and knowledge discovery (FSKD) 2015 (pp. 2147–2152).

    Google Scholar 

  23. Boberg, C., Svensson, M., Kovács, B. (2018). Distributed cloud – A key enabler of automotive and industry 4.0 use cases. Charting the Future of Innovation, Ericcson Technology Review, No.11–2018.

    Google Scholar 

  24. Khan, A., Turowski, K. (2016). A perspective on industry 4.0: From challenges to opportunities in production systems. In proceedings of the international conference on internet of things and big data (IoTBD 2016), scitepress (pp. 441–448).

    Google Scholar 

  25. Nahar, K., & Chakraborty, P. (2020). Improved approach of rail fence for enhancing security. International Journal of Innovative Technology and Exploring Engineering, 9(9), 583–585. https://doi.org/10.35940/ijitee.i7637.079920.

    Article  Google Scholar 

  26. Nahar, K., & Chakraborty, P. (2020). A modified version of Vigenere cipher using 95 × 95 table. International Journal of Engineering and Advanced Technology (IJEAT), 9(5), 1144–1148. https://doi.org/10.35940/ijeat.E9941.069520.

    Article  Google Scholar 

  27. Sharma, M., Kumar, R., & Jain, A. (2019). Implementation of various load-balancing approaches for cloud computing using CloudSim. Journal of Computational and Theoretical Nanoscience, 16(9), 3974–3980.

    Article  Google Scholar 

  28. Jain, A., & Kumar, R. (2014). A taxonomy of cloud computing. International Journal of Scientific and Research Publications, 4(7), 1–5.

    Google Scholar 

  29. Compastié, M., Badonnel, R., Festor, O., He, R., & Kassi-Lahlou, M. (2016). A software-defined security strategy for supporting autonomic security enforcement in distributed cloud. IEEE 8th international conference on cloud computing technology and science, IEEE Computer Society.

    Google Scholar 

  30. John, W., Sargor, C., Szabo, R., Awan, A. J., Padala, C., Drake, E., Julien, M., Opsenica, M. (2020). The future of cloud computing - highly distributed with heterogeneous hardware. Ericcson Technology Review.

    Google Scholar 

  31. Eriksson, A. C., Forsman, M., Ronkainen, H., Willars, P., Östberg, C. (2020). 5G new radio RAN & transport – Choices that minimize CTO. Ericcson Technology Review.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sobers Smiles David, G., Ramkumar, K., Shanmugavadivu, P., Eliahim Jeevaraj, P.S. (2021). Introduction to Cloud Resource Management. In: Choudhury, T., Dewangan, B.K., Tomar, R., Singh, B.K., Toe, T.T., Nhu, N.G. (eds) Autonomic Computing in Cloud Resource Management in Industry 4.0. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-71756-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-71756-8_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71755-1

  • Online ISBN: 978-3-030-71756-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics