Skip to main content

Advertisement

Log in

Streamlining Task Planning Systems for Improved Enactment in Contemporary Computing Surroundings

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Task planning algorithms are essential for maximizing efficiency and enhancing performance in modern computing systems, given the escalating demand for computational resources. This paper delves into the effectiveness of various scheduling algorithms across different computing environments, characterized by their unique workload dynamics and resource limitations. This method integrates adaptive algorithms and machine learning to dynamically optimize task planning, enhancing performance and efficiency in cloud, IoT, and distributed computing environments. These algorithms are crucial for optimizing key factors such as task completion times, job prioritization, and resource allocation while also addressing quality of service (QoS), cost, reliability, and specific resource needs. One significant challenge is that many task scheduling approaches do not account for the potential failure of tasks or resources. While some strategies effectively reduce overall completion time (makespan), they can result in severe workload imbalances. To address these issues, our study proposes a new approach that harnesses the processing capabilities of grid systems, thus boosting application performance and throughput. Our algorithm particularly focuses on balancing workload and accelerating scheduling operations, even in the face of grid node failures, by incorporating QoS metrics. This allows for a more robust and adaptable scheduling solution. Comparative analysis with existing methods demonstrates that our algorithm not only improves resource utilization but also significantly diminishes flow time and makespan, confirming its efficacy. Through this research, we contribute to the evolving field of autonomous task scheduling, presenting a solution that responds dynamically to changing environmental conditions and workload demands.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

The data collected and generated during and/or analyzed during the current study are available from the corresponding author on request.

References

  1. Yi S, Li C, and Li Q. A survey of fog computing: concepts, applications and issues, Proceedings of the ACM Workshop on Mobile Big Data (Mobidata ’15), Hangzhou, China, 2015; pp. 37–42.

  2. Xu X, Zhang X, Khan M, Dou W, Xue S, and Yu S. A Balanced Virtual Machine Scheduling Method for Energy Performance TradeOffs in Cyber-Physical Cloud Systems, Future Generation Computer Systems, 2017; pp. 1–11.

  3. Gaurav S, Puneet B. Task aware switcher scheduling for batch mode mapping in computational grid environment. Int J Adv Res Comput Sci Softw Eng. 2013;3:1292–9.

    Google Scholar 

  4. Alharbi F. Multi objectives heuristic algorithm for grid computing. Int J Comput Appl. 2012;46(18):39–45.

    Google Scholar 

  5. Verma M, Bhardwaj N, Yadav A. Real time efficient scheduling algorithm for load balancing in fog computing environment. Int J Inf Technol Comput Sci. 2016;4:1–10.

    Google Scholar 

  6. Choudhari T, Moh M and Moh T. Prioritized task scheduling in fog computing. In: Proceedings of the ACMSE 2018 Conference (ACMSE '18). ACM, New York, USA, Article 22, 2018; 8 pages.

  7. Liu L, Qi D, Zhou N and Wu Y. A task scheduling algorithm based on classification mining in fog computing environment. Wireless Communications and Mobile Computing, 2018, Article ID 2102348, 2018; 11 pages.

  8. Roy V. An effective FOG computing based distributed forecasting of cyber-attacks in internet of things. J Cybersecur Inf Manag. 2023;12(2):8–17.

    Google Scholar 

  9. Shukla PK, Roy V, Shukla PK, Chaturvedi AK, Saxena AK, Maheshwari M, Pal PR. An advanced EEG motion artifacts eradication algorithm. Comput J. 2021. https://doi.org/10.1093/comjnl/bxab170.

    Article  Google Scholar 

  10. Sahil, Sood SK. FOG-cloud centric IoT-based cyber physical framework for panic oriented disaster evacuation in smart cities. Earth Sci Inform. 2022;15:1449–70.

    Article  Google Scholar 

  11. Roy V. A context-aware internet of things (IoT) founded approach to scheming an operative priority-based scheduling algorithms. J Cybersecur Inform Manage. 2024;13(1):28–35.

    Article  Google Scholar 

  12. Samuel J, Fenil E, Manogaran, Gunasekaran G, Vivekananda, Thanjaivadivel M, Jeeva S, Ahilan A. Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM. Comput Netw. 2019. https://doi.org/10.1016/j.comnet.2019.01.028.

    Article  Google Scholar 

  13. Vijayalakshmi R and Vasudevan V. Scheduling independent obligations on heterogeneous computing systems by heuristic approach. Int J Pure Appl Math. 2018;119(12):13983–9. http://www.ijpam.eu. (ISSN: 1314-3395)

  14. Zhu W, Wang K, Xu H, Liu Z, Fang W. Development of multifunctional integration technology. Aerosp Electron Warf. 2020;36:1–10.

    Google Scholar 

  15. Wang M, Chen P, Cao Z, Chen Y. Reinforcement learning-based UAVs resource allocation for integrated sensing and communication (ISAC) system. Electronics. 2022;11:441.

    Article  Google Scholar 

  16. Feng Z, Fang Z, Wei Z, Chen X, Quan Z, Ji D. Joint radar and communication: a survey. China Commun. 2020;17:1–27.

    Article  Google Scholar 

  17. Shukla PK, Roy V, Shukla PK, et al. Network physical address based encryption technique using digital logic. Int J Sci Technol Res. 2020;9(4):3119–22.

    Google Scholar 

  18. Niu, H, Zhi L, Kang A, Jiangzhou W, Gan Z, Naofal A-D, Kai-Kit W. Active RIS assisted rate-splitting multiple access network: spectral and energy efficiency tradeoff. IEEE J Sel Areas Commun. 2023;41(5):1452–67.

    Article  Google Scholar 

  19. Korrai P, Lagunas E, Sharma SK, Chatzinotas S, Bandi A, Ottersten B. A RAN resource slicing mechanism for multiplexing of eMBB and URLLC services in OFDMA based 5G wireless networks. IEEE Access. 2020;8:45674–88.

    Article  Google Scholar 

  20. Khabaz S, Boulila KO, Nguyen TMT, El Aoun M, Velloso BP. A new priority and satisfaction-based resource allocation algorithm with mixed numerology for 5G-V2X communications. In: Proceedings of the 2022 14th IFIP Wireless and Mobile Networking Conference (WMNC), Sousse, Tunisia, 17–19 October 2022.

  21. Kaloxylos A. A survey and an analysis of network slicing in 5G networks. IEEE Commun Stand Mag. 2018;2:60–5.

    Article  Google Scholar 

  22. Roy V. An improved image encryption consuming fusion transmutation and edge operator. J Cybersecur Inf Manage. 2021;8(1):42–52.

    Google Scholar 

  23. Zhang C, Mingyong L, Di W. Federated multidomain learning with graph ensemble autoencoder GMM for emotion recognition. IEEE Trans Intell Transp Syst. 2022;24(7):7631–41.

    Article  Google Scholar 

  24. Luo X, Zhang C, Bai L. A fixed clustering protocol based on random relay strategy for EHWSN. Digit Commun Netw. 2023;9:90–100.

    Article  Google Scholar 

  25. Chen H, Wen J, Pedrycz W, Wu G. Big data processing workflows oriented real-time scheduling algorithm using task-duplication in geo-distributed clouds. IEEE Trans Big Data. 2020;6:131–44.

    Article  Google Scholar 

  26. AminiMotlagh A, Movaghar A, Rahmani AM. Task scheduling mechanisms in cloud computing: a systematic review. Int J Commun Syst. 2020;33: e4302.

    Article  Google Scholar 

  27. Kumar M, Sharma S, Goel A, Singh S. A comprehensive survey for scheduling techniques in cloud computing. J Netw Comput Appl. 2019;143:1–33.

    Article  Google Scholar 

Download references

Funding

No funding help has been taken from any source.

Author information

Authors and Affiliations

Authors

Contributions

Sindhu Menon—acquisition of data, design of study, Santosh Reddy Addula—analysis and interpretation, Parkavi A—conceptualization, Ch. Subbalakshmi—formalization an editing, Bala Dhandayuthapani V—drafting, Kiran Sree Pokkuluri—review, Anita Soni—final analysis.

Corresponding author

Correspondence to Ch. Subbalakshmi.

Ethics declarations

Conflict of Interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Ethical Approval

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

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

Menon, S., Addula, S.R., Parkavi, A. et al. Streamlining Task Planning Systems for Improved Enactment in Contemporary Computing Surroundings. SN COMPUT. SCI. 5, 993 (2024). https://doi.org/10.1007/s42979-024-03267-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-024-03267-5

Keywords

Navigation