Skip to main content

Advertisement

SpringerLink
Log in

DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Task scheduling in cloud paradigm brought attention of all researchers as it is a challenging issue due to uncertainty, heterogeneity, and dynamic nature as they are varied in size, processing capacity and number of tasks to be scheduled. Therefore, ineffective scheduling technique may lead to increase of energy consumption SLA violations and makespan. Many of authors proposed heuristic approaches to solve task scheduling problem in cloud paradigm but it is fall behind to achieve goal effectively and need improvement especially while scheduling multimedia tasks as they consists of more heterogeneity, processing capacity. Therefore, to handle this dynamic nature of tasks in cloud paradigm, a scheduling mechanism, which automatically takes the decision based on the upcoming tasks onto cloud console and already running tasks in the underlying virtual resources. In this paper, we have used a Deep Q-learning network model to addressed the mentioned scheduling problem that search the optimal resource for the tasks. The entire extensive simulationsare performed usingCloudsim toolkit. It was carried out in two phases. Initially random generated workload is used for simulation. After that, HPC2N and NASA workload are used to measure performance of proposed algorithm. DRLBTSA is compared over baseline algorithms such as FCFS, RR, Earliest Deadline first approaches. From simulation results it is evident that our proposed scheduler DRLBTSA minimizes makespan over RR,FCFS, EDF, RATS-HM, MOABCQ by 29.76%, 41.03%, 27.4%, 33.97%, 33.57% respectively. SLA violation percentage for DRLBTSA minimized overRR,FCFS, EDF, RATS-HM, MOABCQ by48.12%, 41.57%, 37.57%, 36.36%, 30.59% respectively and energy consumption for DRLBTSA over RR,FCFS, EDF, RATS-HM, MOABCQ by36.58%,43.2%, 38.22%, 38.52%, 33.82%existing approaches.

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
Fig. 6

Similar content being viewed by others

Data availability

Authors not interested to disclose the availability of data.

References

  1. Abualigah L, Alkhrabsheh M (2022) Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing. J Supercomput 78(1):740–765

    Article  Google Scholar 

  2. Adhikari M, Srirama SN, Amgoth T (2022) A comprehensive survey on nature-inspired algorithms and their applications in edge computing: Challenges and future directions. Softw Pract Exp 52(4):1004–1034

    Article  Google Scholar 

  3. Agrawal K, Khetarpal P (2022) Computational intelligence in edge and cloud computing. J Inf Optim Sci 43:607–613

    Google Scholar 

  4. Amer DA et al (2022) Elite learning Harris hawks optimizer for multi-objective task scheduling in cloud computing. J Supercomput 78(2):2793–2818

    Article  Google Scholar 

  5. Bal PK et al (2022) A Joint Resource Allocation, Security with Efficient Task Scheduling in Cloud Computing Using Hybrid Machine Learning Techniques. Sensors 22(3):1242

    Article  Google Scholar 

  6. Biswas D et al (n.d.) Optimized Round Robin Scheduling Algorithm Using Dynamic Time Quantum Approach in Cloud Computing Environment

  7. Calheiros RN et al (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50

    Article  MathSciNet  Google Scholar 

  8. Cheng F et al (2022) Cost-aware job scheduling for cloud instances using deep reinforcement learning. Clust Comput 25(1):619–631

    Article  Google Scholar 

  9. Ding D et al (2020) Q-learning based dynamic task scheduling for energy-efficient cloud computing. Futur Gener Comput Syst 108:361–371

    Article  Google Scholar 

  10. Dong T et al (2020) Task scheduling based on deep reinforcement learning in a cloud manufacturing environment. Concurr Comput Pract Exp 32(11):e5654

    Article  Google Scholar 

  11. Gazori P, Rahbari D, Nickray M (2020) Saving time and cost on the scheduling of fog-based IoT applications using deep reinforcement learning approach. Futur Gener Comput Syst 110:1098–1115

    Article  Google Scholar 

  12. Ghafari R, HassaniKabutarkhani F, Mansouri N (2022) Task scheduling algorithms for energy optimization in cloud environment: a comprehensive review. Clust Comput 25:1035–1093

    Article  Google Scholar 

  13. HPC2N: The HPC2N Seth log; 2016. http://www.cs.huji.ac.il/labs/parallel/workload/l_hpc2n/.0

  14. Huang Y et al (2021) Deep adversarial imitation reinforcement learning for QoS-aware cloud job scheduling. IEEE Syst J 16:4232–4242

    Article  Google Scholar 

  15. Karthiban K, Raj JS (2020) An efficient green computing fair resource allocation in cloud computing using modified deep reinforcement learning algorithm. Soft Comput 24(19):14933–14942

    Article  Google Scholar 

  16. Kruekaew B, WarangkhanaKimpan. (2022) 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

    Article  Google Scholar 

  17. Kumar R, Bhagwan J (2022) A comparative study of meta-heuristic-based task scheduling in cloud computing. In: Artificial Intelligence and Sustainable Computing. Springer, Singapore, pp 129–141

    Chapter  Google Scholar 

  18. Lahande P, Kaveri P (2022) Implementing FCFS and SJF for finding the need of Reinforcement Learning in Cloud Environment. ITM Web of Conferences. Vol. 50. EDP Sciences

  19. Li F, Bo H (2019) Deepjs: Job scheduling based on deep reinforcement learning in cloud data center. Proceedings of the 2019 4th international conference on big data and computing

  20. Madni SHH et al (2019) Hybrid gradient descent cuckoo search (HGDCS) algorithm for resource scheduling in IaaS cloud computing environment. Clust Comput 22(1):301–334

    Article  Google Scholar 

  21. Mohanapriya N et al (2018) Energy efficient workflow scheduling with virtual machine consolidation for green cloud computing. J Intell Fuzzy Syst 34(3):1561–1572

    Article  Google Scholar 

  22. Nabi S et al (2022) AdPSO: adaptive PSO-based task scheduling approach for cloud computing. Sensors 22(3):920

    Article  MathSciNet  Google Scholar 

  23. NASA (n.d.): https://www.cse.huji.ac.il/labs/parallel/workload/l_nasa_ipsc/

  24. Nayak SC et al (2022) An enhanced deadline constraint based task scheduling mechanism for cloud environment. J King Saud Univ Comput Inf Sci 34(2):282–294

    Google Scholar 

  25. Rjoub G, Bentahar J, Wahab OA (2020) BigTrustScheduling: Trust-aware big data task scheduling approach in cloud computing environments. Futur Gener Comput Syst 110:1079–1097

    Article  Google Scholar 

  26. Rjoub G et al (2021) Deep and reinforcement learning for automated task scheduling in large-scale cloud computing systems. Concurr Comput Pract Exp 33(23):e5919

    Article  Google Scholar 

  27. Sharma M, Garg R (2020) An artificial neural network based approach for energy efficient task scheduling in cloud data centers. Sustain Comput Inform Syst 26:100373

    Google Scholar 

  28. Sheng S et al (2021) Deep reinforcement learning-based task scheduling in iot edge computing. Sensors 21(5):1666

    Article  Google Scholar 

  29. Siddesha K, Jayaramaiah GV, Singh C (2022) A novel deep reinforcement learning scheme for task scheduling in cloud computing. Clust Comput 25(6):4171–4188

    Article  Google Scholar 

  30. Spano S et al (2019) An efficient hardware implementation of reinforcement learning: The q-learning algorithm. IEEE Access 7:186340–186351

    Article  Google Scholar 

  31. Staddon JER (2020) The dynamics of behavior: Review of Sutton and Barto: Reinforcement learning: An introduction. J Exp Anal Behav 113(2):485–491

    Article  Google Scholar 

  32. Swarup S, Shakshuki EM, Yasar A (2021) Task scheduling in cloud using deep reinforcement learning. Procedia Comput Sci 184:42–51

    Article  Google Scholar 

  33. Tong Z et al (2020) QL-HEFT: a novel machine learning scheduling scheme base on cloud computing environment. Neural Comput & Applic 32(10):5553–5570

    Article  Google Scholar 

  34. Tong Z et al (2020) A scheduling scheme in the cloud computing environment using deep Q-learning. Inf Sci 512:1170–1191

    Article  Google Scholar 

  35. Wang Y et al (2019) Multi-objective workflow scheduling with deep-Q-network-based multi-agent reinforcement learning. IEEE Access 7:39974–39982

    Article  Google Scholar 

  36. Wei Y et al (2018) DRL-scheduling: An intelligent QoS-aware job scheduling framework for applications in clouds. IEEE Access 6:55112–55125

    Article  Google Scholar 

  37. Yan J et al (2022) Energy-aware systems for real-time job scheduling in cloud data centers: A deep reinforcement learning approach. Comput Electr Eng 99:107688

    Article  Google Scholar 

  38. Zhang X et al (2019) Energy-aware virtual machine allocation for cloud with resource reservation. J Syst Softw 147:147–161

    Article  Google Scholar 

  39. Zhou G, Tian W, Buyya R (2021) Deep reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions. arXiv preprint arXiv:2105.04086

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Amaravati, AP, India

    Sudheer Mangalampalli & Ganesh Reddy Karri

  2. Department of Information Technology, NIT Jalandhar, Jalandhar, India

    Mohit Kumar

  3. Al-NahrinNanorenewable Energy Research Center, Al-Nahrin University, Bhagdad, Iraq

    Osama Ibrahim Khalaf

  4. Universidad Santiago de Cali, Cali, Colombia

    Carlos Andres Tavera Romero

  5. Department of Computer Engineering, University of Technology, Bhagdad, Iraq

    GhaidaMuttashar Abdul Sahib

Authors
  1. Sudheer Mangalampalli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ganesh Reddy Karri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohit Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Osama Ibrahim Khalaf
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Carlos Andres Tavera Romero
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. GhaidaMuttashar Abdul Sahib
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sudheer Mangalampalli.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Mangalampalli, S., Karri, G.R., Kumar, M. et al. DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing. Multimed Tools Appl 83, 8359–8387 (2024). https://doi.org/10.1007/s11042-023-16008-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16008-2

Keywords

Search

Navigation