Skip to main content
SpringerLink
Log in

QoS and reliability aware matched bald eagle task scheduling framework based on IoT-cloud in educational applications

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Cloud computing is a popular paradigm that enables on-demand access to shared resources over the internet. Task scheduling is an important aspect of cloud computing that involves allocating resources to tasks in an efficient manner. The rapid growth of cloud computing has led to an increasing demand for efficient task scheduling algorithms. In cloud computing, task scheduling is critical for achieving high performance and resource utilization, while minimizing costs. However, traditional task scheduling algorithms often struggle to handle the intricacy and fluctuation in cloud computing environments. Therefore, a novel task scheduling framework called Matched Bald Eagle (MABLE) task scheduling framework for Cloud Computing to schedule tasks on Virtual Machines (VMs) in a cloud environment. The proposed framework consists of three major phases: matching, sorting and scheduling. The matching phase identifies the most suitable VMs for each task, while the sorting phase prioritizes the tasks based on their requirements and the types of VMs available. Finally, the scheduling phase uses the Enhanced Bald Eagle optimization (EBEO) algorithm in scheduling tasks on the chosen VMs. The simulated MABLE technique is proposed and its performance is compared with existing methods in terms of load balance, cost, resource utilization and makespan under two different scenarios. The outcomes demonstrate that the MABLE method outperforms existing techniques and is an efficient task scheduling framework for cloud computing.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Sunil Kumar Chowdhary and ALN Rao.The first draft of the manuscript was written by Sunil Kumar Chowdhary and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Conceptualization: Sunil Kumar Chowdhary; Methodology: Sunil Kumar Chowdhary; Formal analysis and investigation: Sunil Kumar Chowdhary and ALN Rao; Writing—original draft preparation: Sunil Kumar Chowdhary and ALN Rao; Writing—review and editing: Sunil Kumar Chowdhary and ALN Rao; Supervision: ALN Rao.

References

  1. Wang, J., Lim, M.K., Wang, C., Tseng, M.L.: The evolution of the internet of things (IoT) over the past 20 years. Comput. Ind. Eng. 155, 107174 (2021)

    Article  Google Scholar 

  2. Syed, A.S., Sierra-Sosa, D., Kumar, A., Elmaghraby, A.: IoT in smart cities: a survey of technologies, practices and challenges. Smart Cities 4(2), 429–475 (2021)

    Article  Google Scholar 

  3. Centenaro, M., Costa, C.E., Granelli, F., Sacchi, C., Vangelista, L.: A survey on technologies, standards and open challenges in satellite IoT. IEEE Commun. Surv. Tutor. 23(3), 1693–1720 (2021)

    Article  Google Scholar 

  4. Bhuiyan, M.N., Rahman, M.M., Billah, M.M., Saha, D.: Internet of things (IoT): a review of its enabling technologies in healthcare applications, standards protocols, security, and market opportunities. IEEE Internet Things J. 8(13), 10474–10498 (2021)

    Article  Google Scholar 

  5. Ajay, P., Nagaraj, B., Pillai, B.M., Suthakorn, J., Bradha, M.: Intelligent ecofriendly transport management system based on IoT in urban areas. Environ. Dev. Sustain. (2022). https://doi.org/10.1007/s10668-021-02010-x

    Article  Google Scholar 

  6. Al-Taai, S.H.H., Kanber, H.A., Al-Dulaimi, W.A.M.: The importance of using the internet of things in education. Int. J. Emerg. Technol. Learn. 18(1), 19–39 (2023)

    Article  Google Scholar 

  7. Abichandani, P., Sivakumar, V., Lobo, D., Iaboni, C., Shekhar, P.: Internet-of-things curriculum, pedagogy, and assessment for stem education: a review of literature. IEEE Access 10, 38351–38369 (2022)

    Article  Google Scholar 

  8. Jahangeer, A., Sajid, A., Zafar, A.: The impact of big data and IoT for computational smarter education system. In: Big Data Analytics and Computational Intelligence for Cybersecurity, pp. 269–281. Cham: Springer International Publishing (2022)

  9. Pappas, G., Siegel, J., Vogiatzakis, I.N. Politopoulos, K.: Gamification and the internet of things in education. In: Handbook on Intelligent Techniques in the Educational Process: Recent Advances and Case Studies, vol. 1, pp. 317–339. Cham: Springer International Publishing (2022)

  10. Zeeshan, K., Hämäläinen, T., Neittaanmäki, P.: Internet of things for sustainable smart education: an overview. Sustainability 14(7), 4293 (2022)

    Article  Google Scholar 

  11. Camarinha-Matos, L.M., Katkoori, S.: Challenges in IoT applications and research. In: Internet of Things. Technology and Applications: 4th IFIP International Cross-Domain Conference, IFIPIoT: Virtual Event, November 4–5, 2021, Revised Selected Papers, 3–10, p. 2022. Springer International Publishing, Cham (2021)

    Google Scholar 

  12. Goudarzi, M., Ilager, S., Buyya, R.: Cloud computing and internet of things: recent trends and directions. New Front. Cloud Comput. Internet Things 27, 3–29 (2022)

    Article  Google Scholar 

  13. Hassan, K.M., Abdo, A., Yakoub, A.: Enhancement of health care services based on cloud computing in IOT environment using hybrid swarm intelligence. IEEE Access 10, 105877–105886 (2022)

    Article  Google Scholar 

  14. Ketu, S., Mishra, P.K.: Cloud, fog and mist computing in IoT: an indication of emerging opportunities. IETE Tech. Rev. 39(3), 713–724 (2022)

    Article  Google Scholar 

  15. Verma, P., Tiwari, R., Hong, W.C., Upadhyay, S., Yeh, Y.H.: FETCH: a deep learning-based fog computing and IoT integrated environment for healthcare monitoring and diagnosis. IEEE Access 10, 12548–12563 (2022)

    Article  Google Scholar 

  16. Garbugli, A., Sabbioni, A., Corradi, A., Bellavista, P.: Tempos: Qos management middleware for edge cloud computing FAAS in the internet of things. IEEE Access 10, 49114–49127 (2022)

    Article  Google Scholar 

  17. Babar, M., Jan, M.A., He, X., Tariq, M.U., Mastorakis, S., Alturki, R.: An optimized IoT-enabled big data analytics architecture for edge-cloud computing. IEEE Internet Things J. 10(5), 3995–4005 (2022)

    Article  Google Scholar 

  18. Ali, H.S., Rout, R.R., Parimi, P., Das, S.K.: Real-time task scheduling in fog-cloud computing framework for IoT applications: a fuzzy logic-based approach. In: 2021 International Conference on COMmunication Systems & NETworkS (COMSNETS), pp. 556–564. IEEE (2021)

  19. Lakhan, A., Mastoi, Q.U., Elhoseny, M., Memon, M.S., Mohammed, M.A.: Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud. Enterp. Inf. Syst. 16(7), 1883122 (2022)

    Article  Google Scholar 

  20. Hoseiny, F., Azizi, S., Shojafar, M., Ahmadiazar, F., Tafazolli, R.: PGA: a priority-aware genetic algorithm for task scheduling in heterogeneous fog-cloud computing. In: IEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, pp. 1–6 (2021)

  21. Chowdhary, S.K., Rao, A.L.: QoS enhancement in cloud-IoT framework for educational institution with task allocation and scheduling with task-VM matching approach. Wirel. Pers. Commun. 121, 267–286 (2021)

    Article  Google Scholar 

  22. Kim, Y., Song, C., Han, H., Jung, H., Kang, S.: Collaborative task scheduling for IoT-assisted edge computing. IEEE Access 8, 216593–216606 (2020)

    Article  Google Scholar 

  23. Sadeeq, M.M., Abdulkareem, N.M., Zeebaree, S.R., Ahmed, D.M., Sami, A.S., Zebari, R.R.: IoT and cloud computing issues, challenges and opportunities: a review. Qubahan Acad J 1(2), 1–7 (2021)

    Article  Google Scholar 

  24. Shakeel, H., Alam, M.: Load balancing approaches in cloud and fog computing environments: a framework, classification, and systematic review. Int. J. Cloud Appl. Comput. 12(1), 1–24 (2022)

    Google Scholar 

  25. Khan, Z., Alam, M., Haidri, R.A.: Effective load balance scheduling schemes for heterogeneous distributed system. Int. J. Electr. Comput. Eng. 7(5), 2088–8708 (2017)

    Google Scholar 

  26. Alam, M., Haidri, R.A., Yadav, D.K.: Efficient task scheduling on virtual machine in cloud computing environment. Int. J. Pervasive Comput. Commun. 17(3), 271–287 (2021)

    Article  Google Scholar 

  27. Sabireen, H., Venkataraman, N.: A hybrid and light weight metaheuristic approach with clustering for multi-objective resource scheduling and application placement in fog environment. Expert Syst. Appl. 223, 119895 (2023)

    Article  Google Scholar 

  28. Nazeri, M., Khorsand, R.: Energy aware resource provisioning for multi-criteria scheduling in cloud computing. Cybern. Syst. (2022). https://doi.org/10.1080/01969722.2022.2071409

    Article  Google Scholar 

  29. Goel, G., Tiwari, R.: Resource scheduling techniques for optimal quality of service in fog computing environment: a review. Wirel Personal Commun. 131(1), 141–164 (2023)

    Article  Google Scholar 

  30. Moazeni, A., Khorsand, R., Ramezanpour, M.: Dynamic resource allocation using an adaptive multi-objective teaching-learning based optimization algorithm in cloud. IEEE Access. 11, 23407–23419 (2023)

    Article  Google Scholar 

  31. Mahmoud, H., Thabet, M., Khafagy, M.H., Omara, F.A.: Multiobjective task scheduling in cloud environment using decision tree algorithm. IEEE Access 10, 36140–36151 (2022)

    Article  Google Scholar 

  32. Kruekaew, B., Kimpan, W.: Multi-objective task scheduling optimization for load balancing in cloud computing environment using hybrid artificial bee colony algorithm with reinforcement learning. IEEE Access 10, 17803–17818 (2022)

    Article  Google Scholar 

  33. Najafizadeh, A., Salajegheh, A., Rahmani, A.M., Sahafi, A.: Multi-objective task scheduling in cloud-fog computing using goal programming approach. Clust. Comput. 25(1), 141–165 (2022)

    Article  Google Scholar 

  34. Ali, A., Iqbal, M.M.: A cost and energy efficient task scheduling technique to offload microservices based applications in mobile cloud computing. IEEE Access 10, 46633–46651 (2022)

    Article  Google Scholar 

  35. Abdullahi, M., Ngadi, M.A., Dishing, S.I., Abdulhamid, S.I.: An adaptive symbiotic organisms search for constrained task scheduling in cloud computing. J. Ambient Intell. Human. Comput. 14, 1–2 (2022)

    Google Scholar 

  36. Mangalampalli, S., Swain, S.K., Mangalampalli, V.K.: Prioritized energy efficient task scheduling algorithm in cloud computing using whale optimization algorithm. Wirel. Pers. Commun. 126(3), 2231–2247 (2022)

    Article  Google Scholar 

  37. Memari, P., Mohammadi, S.S., Jolai, F., Tavakkoli-Moghaddam, R.: A latency-aware task scheduling algorithm for allocating virtual machines in a cost-effective and time-sensitive fog-cloud architecture. J. Supercomput. 78(1), 93–122 (2022)

    Article  Google Scholar 

  38. Aktan, M.N., Bulut, H.: Metaheuristic task scheduling algorithms for cloud computing environments. Concurr. Comput. 34(9), e6513 (2022)

    Article  Google Scholar 

  39. Feng, H., Qiao, L., Lv, Z.: Innovative soft computing-enabled cloud optimization for next-generation IoT in digital twins. Appl. Soft Comput. 136, 110082 (2023)

    Article  Google Scholar 

  40. Wang, Z., Goudarzi, M., Gong, M., Buyya, R.: Deep reinforcement learning-based scheduling for optimizing system load and response time in edge and fog computing environments. Futur. Gener. Comput. Syst. 152, 55–69 (2024)

    Article  Google Scholar 

  41. Patel, G., Mehta, R., Bhoi, U.: Enhanced load balanced min-min algorithm for static meta task scheduling in cloud computing. Procedia Comput. Sci. 57, 545–553 (2015)

    Article  Google Scholar 

  42. Mao, Y., Chen, X., Li, X.: Max–min task scheduling algorithm for load balance in cloud computing. In: Proceedings of International Conference on Computer Science and Information Technology: CSAIT 2013, pp. 457–465, September 21–23, 2013, Kunming, China. Springer (2014)

  43. Alam, M., Haidri, R.A., Shahid, M.: Resource-aware load balancing model for batch of tasks (BoT) with best fit migration policy on heterogeneous distributed computing systems. Int. J. Pervas. Comput. Commun. 16(2), 113–141 (2020)

    Article  Google Scholar 

  44. Haidri, R.A., Alam, M., Shahid, M., Prakash, S., Sajid, M.: A deadline aware load balancing strategy for cloud computing. Concurr. Comput. 34(1), e6496 (2022)

    Article  Google Scholar 

  45. Zeng, Z., Veeravalli, B.: Design and performance evaluation of queue-and-rate-adjustment dynamic load balancing policies for distributed networks. IEEE Trans. Comput. 55(11), 1410–1422 (2006)

    Article  Google Scholar 

  46. Alam, T., Raza, Z.: An adaptive threshold based hybrid load balancing scheme with sender and receiver initiated approach using random information exchange. Concurr. Comput. 28(9), 2729–2746 (2016)

    Article  Google Scholar 

Download references

Funding

There is no funding for this study.

Author information

Authors and Affiliations

  1. Dr. A P J Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India

    Sunil Kumar Chowdhary

  2. Lloyd Institute of Engineering & Technology, Greater Noida, India

    A. L. N. Rao

Authors
  1. Sunil Kumar Chowdhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. L. N. Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All the authors have participated in writing the manuscript and have revised the final version. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sunil Kumar Chowdhary.

Ethics declarations

Conflict of interest

Authors declares that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants and/or animals performed by any of the authors.

Informed consent

There is no informed consent for this study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chowdhary, S.K., Rao, A.L.N. QoS and reliability aware matched bald eagle task scheduling framework based on IoT-cloud in educational applications. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04415-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10586-024-04415-5

Keywords

Search

Navigation