Skip to main content
SpringerLink
Log in

Improving Architectural Reusability for Resource Allocation Framework in Futuristic Cloud Computing Using Decision Tree Based Multi-objective Automated Approach

  • Conference paper
  • First Online:
Advancements in Smart Computing and Information Security (ASCIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1759))

Abstract

Cloud computing is a highly popular computing technique. Cloud combined with IoT, fog, edge, and mist computing in 5G networks gives us realtime and highly predictive responses leading to a better and smart life. It requires a highly robust and integrated cloud administration, especially cloud resource allocation. Artificial intelligence and machine learning can be easily implemented along cloud design patterns for efficient resource allocation. In this paper we discuss multi-tenant cloud resource allocation problem. We propose to use a rule-based analysis pattern to dynamically reconfigure resource allocation processes. The pattern uses various attributes of clouds, resources, subscribers and requests along with heuristic data like configurations, policies, strategies, and methods to efficiently identify and apply rule of allocation. We implemented a decision tree to assist pattern to have automated decisions, which rule to follow. The pattern caters for multi-objectivity, simplifies architecture, enables the extension of the cloud framework and makes it possible to interact easily with cloud. This paper describes the architectural framework pattern, which learns from itself. This paper presents CK’s object-oriented metrics comparisons of pattern-based object-oriented code. The comparison shows that object-oriented code improves code quality, making pattern-based code more maintainable, flexible, extendable and secure.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Singh, J., Dhiman, G.: A survey on cloud computing approaches. In: Materials Today: Proceedings. Science Direct (2021). https://doi.org/10.1016/j.matpr.2021.05.334

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

    Article  Google Scholar 

  3. Gamma, E., et al.: Elements of Reusable Object-oriented Software. Pearson (2015)

    Google Scholar 

  4. Chidamber, S.R., Kemerer, C.F.: A metrics suite for object oriented design. IEEE Trans. Software Eng. 20(6), 476–493 (1994)

    Article  Google Scholar 

  5. Fard, M.V., et al.: Resource allocation mechanisms in cloud computing: a systematic literature review. IET Software 14(6), 638–653 (2020)

    Article  Google Scholar 

  6. Benali, A.E., Asri, B.: Towards rigorous selection and configuration of cloud services: research methodology. Int. J. Comput. Sci. Issues 17(6), 77–81 (2020)

    Google Scholar 

  7. Fehling, C.: Cloud computing patterns: identification, design application (2015)

    Google Scholar 

  8. Sousa, T.B., Ferreira, H.S., Correia, F.F.: A survey on the adoption of patterns for engineering software for the cloud. IEEE Trans. Software Eng. 48(6), 2128–2140 (2022). https://doi.org/10.1109/TSE.2021.3052177

    Article  Google Scholar 

  9. Gill, S.S., Chana, I.: QoS based workload design patterns in cloud computing: a literature review. Int. J. Cloud-Comput. Super-Comput. 2, 37–46 (2015). https://doi.org/10.21742/ijcs.2015.2.2.05

  10. Abdul, A.O., et al.. Multi-tenancy design patterns in SAAS applications: a performance evaluation case study. Int. J. Digit. Soc. 17–20 (2018)

    Google Scholar 

  11. Kalra, S.: Implementation patterns for multi-tenancy. In: Proceedings of the 24th Conference on Pattern Languages of Programs (2017)

    Google Scholar 

  12. Alshudukhi, J. (2021). Pattern-based solution for architecting cloud-enabled software. Int. J. Adv. Appl. Sci. 8, 9–19. https://doi.org/10.21833/ijaas.2021.08.002

  13. Shi, J., et al.: Fast multi-resource allocation with patterns in large scale cloud data center. Journal of Computational Science 26, 389–401 (2018)

    Article  Google Scholar 

  14. Ezugwu, A.E., Eduard Frincu, M. Balarabe Junaidu, S.: Architectural pattern for scheduling multi-component applications in distributed systems. Int. J. Grid High Perf. Comput. 8(1), 1–22 (2016)

    Google Scholar 

  15. Reina, A., et al.: A design pattern for decentralised decision making. PLoS ONE 10, e0140950 (2015)

    Article  Google Scholar 

  16. Banijamali, A., et al.: Software architectures of the convergence of cloud computing and the Internet of Things: A systematic literature review. Inf. Softw. Technol. 122, 106271 (2020)

    Article  Google Scholar 

  17. Saboor, A., Mahmood, A.K., Hassan, M.F., Shah, S.N.M., Hassan, F., Siddiqui, M.A.: Design pattern based distribution of microservices in cloud computing environment. In: 2021 International Conference on Computer and Information Sciences (ICCOINS), pp. 396–400 (2021) https://doi.org/10.1109/ICCOINS49721.2021.9497188

  18. Ferguson, D., Méndez Muñoz, V., Pahl, C., Helfert, M. (eds.): CLOSER 2019. CCIS, vol. 1218. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49432-2

    Book  Google Scholar 

  19. Leitner, P., et al.: A mixed-method empirical study of function-as-a-Service software development in industrial practice. J. Syst. Softw. 149, 340–359 (2019)

    Article  Google Scholar 

  20. Krupitzer, C., et al.: An overview of design patterns for self-adaptive systems in the context of the internet of things. IEEE Access 8, 187384–187399 (2020)

    Article  Google Scholar 

  21. Washizaki, H., et al.: Landscape of architecture and design patterns for Iot systems. IEEE Internet Things J. 7(10), 10091–10101 (2020)

    Google Scholar 

  22. Zúñiga-Prieto, M., et al.: Dynamic reconfiguration of cloud application architectures. Softw. Pract. Exp. 48(2), 327–344 (2018)

    Article  Google Scholar 

  23. Mannava, V. T. Ramesh.: Design pattern for dynamic reconfiguration of component-based autonomic computing systems using RMI. Proc. Technol. 6, 590–597 (2012)

    Google Scholar 

  24. Durdik, Z.: Architectural Design Decision Documentation through Reuse of Design Patterns. Vol. 14. KIT Scientific Publishing (2016)

    Google Scholar 

  25. Sahly, Eiman M. Omar M. Sallabi. Design pattern selection: A solution strategy method. In: 2012 ICCSII, IEEE (2012)

    Google Scholar 

  26. Fortiş, T.-F., Ferry, N.: Cloud patterns. In: Di Nitto, E., Matthews, P., Petcu, D., Solberg, A. (eds.) Model-Driven Development and Operation of Multi-Cloud Applications. SAST, pp. 107–112. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-46031-4_11

    Chapter  Google Scholar 

  27. Liu, Y., Lu, Q., Paik, H.-Y., Xu, X., Chen, S., Zhu, L.: Design pattern as a service for blockchain-based self-Sovereign identity. IEEE Softw. 37(5), 30–36 (2020). https://doi.org/10.1109/MS.2020.2992783

    Article  Google Scholar 

  28. Gill, S.S., et al.: Transformative effects of IoT, blockchain and artificial intelligence on cloud computing: Evolution, vision, trends and open challenges. Intern. Things 8, 100118 (2019)

    Article  Google Scholar 

  29. Madni, S., Shafie Abd Latiff, M., Coulibaly, Y.: Recent advancements in resource allocation techniques for cloud computing environment: a systematic review. Clust. Comput. 20(3), 2489–2533 (2017)

    Google Scholar 

  30. Joloudari, J.H., et al.: Resource allocation optimization using artificial intelligence methods in various computing paradigms: A review. 12315 (2022)

    Google Scholar 

  31. Pirhoseinlo, A., Osati Eraghi, N. Akbari Torkestani, J.:. Artificial intelligence-based framework for scheduling distributed systems using a combination of neural networks and genetic algorithms. Mobile Inf. Syst. (2022)

    Google Scholar 

  32. Kumar, M., et al.: ARPS: An autonomic resource provisioning and scheduling framework for cloud platforms. IEEE Trans. Sustain. Comput. 7(2), 386–399 (2021)

    Article  Google Scholar 

  33. Liu, N., et al.: A hierarchical framework of cloud resource allocation and power management using deep reinforcement learning. In: 2017 IEEE 37th international conference on distributed computing systems (ICDCS). IEEE (2017)

    Google Scholar 

  34. Wang, J.-B., et al.: A machine learning framework for resource allocation assisted by cloud computing. IEEE Netw. 32(2), 144–151 (2018)

    Article  Google Scholar 

  35. Daoud, W.B., et al.: Cloud-IoT resource management based on artificial intelligence for energy reduction. In: Wireless Communications and Mobile Computing 2022 (2022)

    Google Scholar 

  36. Zhang, J., et al.: Machine learning based resource allocation of cloud computing in auction. Comput. Mater. Contin. 56(1), 123–135 (2018)

    Google Scholar 

  37. Cheng, M., Li, J., Nazarian, S.: DRL-cloud: Deep reinforcement learningbased resource provisioning and task scheduling for cloud service providers. In: 2018 23rd Asia and South pacific design automation conference (ASP-DAC). IEEE (2018)

    Google Scholar 

  38. Ji, H., Alfarraj, O., Tolba, A.: Artificial intelligence-empowered edge of vehicles: architecture, enabling technologies applications. IEEE Access 8, 61020–61034 (2020)

    Article  Google Scholar 

  39. Xu, C., Wang, K., Guo, M.: Intelligent resource management in blockchain-based cloud datacenters. IEEE Cloud Comput. 4(6), 50–59 (2017)

    Article  Google Scholar 

  40. Alfakih, T., et al.: Task offloading and resource allocation for mobile edge computing by deep reinforcement learning based on SARSA. IEEE Access 8, 54074–54084 (2020)

    Article  Google Scholar 

  41. Hou, W., et al.: Multiagent deep reinforcement learning for task offloading and re-source allocation in Cybertwin-based networks. IEEE Internet Things J. 8(22), 16256–16268 (2021)

    Article  Google Scholar 

  42. Khayyat, M., et al.: Advanced deep learning-based computational offloading for multilevel vehicular edge-cloud computing networks. IEEE Access 8, 137052–137062 (2020)

    Article  Google Scholar 

  43. Genero, M., Piattini, M., Calero, C.: A survey of metrics for UML class diagrams. J. Object Technol. 4(9), 59–92 (2005)

    Article  Google Scholar 

  44. Chawla, Mandeep K. I. Chhabra: Implementation of an object oriented model to analyze relative progression of source code versions with respect to software quality. Int. J. Computer Applications 107, 10 (2014)

    Google Scholar 

  45. Ghareb, M. I., Allen, G.: Quality metrics measurement for hybrid systems (aspect oriented programming—object oriented programming). Technium: Roman. J. Appl. Sci. Technol. 3(3), 82–99 (2021)

    Google Scholar 

  46. Godhrawala, H. R. Sridaran: A dynamic Stackelberg game based multi-objective approach for effective resource allocation in cloud computing. Int. J. Inf. Technol. (2022) 1–16

    Google Scholar 

  47. Keery, S., Harber, C., Marcus , Y., Demiliani, O.S.: Implementing Azure Cloud Design Patterns: Implement Efficient Design Patterns for Data Management, High Availability, Monitoring And Other Popular Patterns On Your Azure Cloud. Packt Publishing Ltd (2018)

    Google Scholar 

  48. Erl, T., Cope, R., Naserpour, A.: Cloud Computing Design Patterns. Prentice Hall Press (2015)

    Google Scholar 

  49. Fernandez, E.B., Yuan, X.: An analysis pattern for reservation and use of reusable entities. In: Procs. of Pattern Languages of Programs Conference, PLoP99 (1999)

    Google Scholar 

  50. Sobajic, O., Moussavi Behrouz Far, M.: Parameterized strategy pattern. In: Proceedings of the 17th Conference on Pattern Languages of Programs (2010)

    Google Scholar 

  51. Mannava, V., Ramesh, T.: A novel adaptive re-configuration compliance design pattern for autonomic computing systems. Proc. Eng. 30, 1129-1137 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Applications, Marwadi University, Rajkot, India

    Husain Godhrawala & R. Sridaran

Authors
  1. Husain Godhrawala
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Sridaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Husain Godhrawala .

Editor information

Editors and Affiliations

  1. Marwadi University, Rajkot, India

    Sridaran Rajagopal

  2. AVPTI, Rajkot, India

    Parvez Faruki

  3. Marwadi University, Rajkot, India

    Kalpesh Popat

Rights and permissions

Reprints and permissions

Copyright information

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

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Godhrawala, H., Sridaran, R. (2022). Improving Architectural Reusability for Resource Allocation Framework in Futuristic Cloud Computing Using Decision Tree Based Multi-objective Automated Approach. In: Rajagopal, S., Faruki, P., Popat, K. (eds) Advancements in Smart Computing and Information Security. ASCIS 2022. Communications in Computer and Information Science, vol 1759. Springer, Cham. https://doi.org/10.1007/978-3-031-23092-9_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-23092-9_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23091-2

  • Online ISBN: 978-3-031-23092-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation