Skip to main content
SpringerLink
Log in

A novel hybrid multi-resource load balancing approach using ant colony optimization with Tabu search for cloud computing

  • S.i. : Intelligence for Systems and Software Engineering
  • Published:
Innovations in Systems and Software Engineering Aims and scope Submit manuscript

Abstract

Cloud computing has become an increasingly important way to process large and complex jobs and services. A powerful scheduler for cloud users' workloads must serve millions of users satisfied with cost and time. This paper designs a novel hybrid approach by integrating the ant colony optimization (ACO) with the Tabu search (TS) approach for multi-resource load balancing. The performance metrics, such as makespan, average throughput, and total cost are calculated and evaluated with the help of these metrics. The proposed ACOTS approach performs better than the existing four optimization approaches: GA, PSO, ACO, and TS. The proposed ACOTS approach performed 30% better than GA, PSO, ACO, and TS algorithms in data delivery. The proposed ACOTS shows the fast file delivery and processing.

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 statement

Submitted manuscript has no data association.

References

  1. Patidar S, Rane D, Jain P (2012) A survey paper on cloud computing. In: 2012 second international conference on advanced computing and communication technologies, Rohtak, Haryana, India, pp. 394–398. https://doi.org/10.1109/ACCT.2012.15

  2. Varghese B, Buyya R (2018) Next generation cloud computing: New trends and research directions. Future Gener Comput Syst 79:849–861. https://doi.org/10.1016/j.future.2017.09.020

    Article  Google Scholar 

  3. Shaw SB, Singh AK (2014) A survey on scheduling and load balancing techniques in cloud computing environment. In: 2014 international conference on computer and communication technology (ICCCT), Allahabad, India, pp 87–95. https://doi.org/10.1109/ICCCT.2014.7001474

  4. Hussain N, Rani P (2020) Comparative studied based on attack resilient and efficient protocol with intrusion detection system based on deep neural network for vehicular system security. In: Distributed artificial intelligence, CRC Press, pp 217–236

  5. Hussain N, Rani P, Chouhan H, Gaur US (2022) Cyber security and privacy of connected and automated vehicles (CAVs)-based federated learning: challenges, opportunities, and open issues. In: Federated learning for IoT applications. Springer, pp 169–183

  6. Rani P, Hussain N, Khan RAH, Sharma Y, Shukla PK (2021) Vehicular intelligence system: time-based vehicle next location prediction in software-defined internet of vehicles (SDN-IOV) for the smart cities. In: Al-Turjman F, Nayyar A, Devi A, Shukla PK (eds) Intelligence of things: AI-IOT based critical-applications and innovations. Springer, Cham, pp 35–54. https://doi.org/10.1007/978-3-030-82800-4_2

  7. Jyoti A, Shrimali M, Tiwari S, Singh HP (2020) Cloud computing using load balancing and service broker policy for IT service: a taxonomy and survey. J Ambient Intell Hum Comput 11(11):4785–4814. https://doi.org/10.1007/s12652-020-01747-z

    Article  Google Scholar 

  8. Afzal S, Kavitha G (2019) Load balancing in cloud computing: a hierarchical taxonomical classification. J Cloud Comput 8(1):22. https://doi.org/10.1186/s13677-019-0146-7

    Article  Google Scholar 

  9. Ren H, Lan Y, Yin C (2012) The load balancing algorithm in cloud computing environment. In: Proceedings of 2012 2nd international conference on computer science and network technology, Changchun, China, pp 925–928. https://doi.org/10.1109/ICCSNT.2012.6526078

  10. Mesbahi M, Rahmani AM (2016) Load balancing in cloud computing: a state of the art survey. Int J Mod Educ Comput Sci 8(3):64

    Article  Google Scholar 

  11. Xu M, Tian W, Buyya R (2017) A survey on load balancing algorithms for virtual machines placement in cloud computing: a survey on load balancing algorithms for VM placement in cloud computing. Concurr Comput Pract Exp 29(12):e4123. https://doi.org/10.1002/cpe.4123

    Article  Google Scholar 

  12. Gutierrez-Garcia JO, Ramirez-Nafarrate A (2015) Agent-based load balancing in cloud data centers. Clust Comput 18(3):1041–1062. https://doi.org/10.1007/s10586-015-0460-x

    Article  Google Scholar 

  13. Kerr A, Diamos G, Yalamanchili S (2009) A characterization and analysis of PTX kernels. In: 2009 IEEE international symposium on workload characterization (IISWC), Austin, TX, USA, pp. 3–12. https://doi.org/10.1109/IISWC.2009.5306801.

  14. Gabhane JP, Pathak S, Thakare NM (2021) Metaheuristics algorithms for virtual machine placement in cloud computing environments—a review. In: Pandian A, Fernando X, Islam SMS (eds) Computer networks, big data and IoT. Lecture notes on data engineering and communications technologies, vol 66. Springer, Singapore. https://doi.org/10.1007/978-981-16-0965-7_28

  15. Maheshwari R, Pathak S (2013) An optimal framework for safe data transmission in cloud environment without disturbing IP Layer. Inernation J Curr Issues Comput Sci 1(1):28–35

    Google Scholar 

  16. Junaid M, Sohail A, Ahmed A, Baz A, Khan IA, Alhakami H (2020) A hybrid model for load balancing in cloud using file type formatting. IEEE Access 8:118135–118155. https://doi.org/10.1109/ACCESS.2020.3003825

    Article  Google Scholar 

  17. Fahim Y et al (2018) Load balancing in cloud computing using meta-heuristic algorithm. J Inf Process Syst 14(3):569–589

    MathSciNet  Google Scholar 

  18. Jena UK, Das PK, Kabat MR (2020) Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2020.01.012

    Article  Google Scholar 

  19. Aktan MN, Bulut H (2021) Metaheuristic task scheduling algorithms for cloud computing environments. Concurr Comput Pract Exp. https://doi.org/10.1002/cpe.6513

    Article  Google Scholar 

  20. Janakiraman S, Priya MD (2021) Improved artificial bee colony using monarchy butterfly optimization algorithm for load balancing (IABC-MBOA-LB) in cloud environments. J Netw Syst Manag 29(4):39. https://doi.org/10.1007/s10922-021-09602-y

    Article  Google Scholar 

  21. Devaraj AFS, Elhoseny M, Dhanasekaran S, Lydia EL, Shankar K (2020) Hybridization of firefly and improved multi-objective particle swarm optimization algorithm for energy efficient load balancing in cloud computing environments. J Parallel Distrib Comput 142:36–45. https://doi.org/10.1016/j.jpdc.2020.03.022

    Article  Google Scholar 

  22. Gade A, Bhat M, Thakare N (2019) Adaptive league championship algorithm (ALCA) for independent task scheduling in cloud computing. Ing Syst Inf 24(3):353–359. https://doi.org/10.18280/isi.240316

    Article  Google Scholar 

  23. Alsaih MA, Latip R, Abdullah A, Subramaniam SK (2013) A Taxonomy of load balancing techniques in cloud computing. Int Rev Comput Softw 8(1):64–76

    Google Scholar 

  24. Mulla BP, Krishna CR, Tickoo RK (2020) Load balancing algorithm for efficient VM allocation in heterogeneous cloud. SSRN Electron J. https://doi.org/10.2139/ssrn.3560167

    Article  Google Scholar 

  25. Shafiq DA, Jhanjhi NZ, Abdullah A, Alzain MA (2021) A load balancing algorithm for the data centres to optimize cloud computing applications. IEEE Access 9:41731–41744. https://doi.org/10.1109/ACCESS.2021.3065308

    Article  Google Scholar 

  26. Ghomi EJ, Rahmani AM, Qader NN (2017) Load-balancing algorithms in cloud computing: a survey. J Netw Comput Appl 88:50–71

    Article  Google Scholar 

  27. Kumar P, Kumar R (2019) Issues and challenges of load balancing techniques in cloud computing: a survey. ACM Comput Surv 51(6):1–35. https://doi.org/10.1145/3281010

    Article  Google Scholar 

  28. Sidhu AK, Kinger S (2013) Analysis of load balancing techniques in cloud computing. Int J Comput Technol 4(2):737–741

    Article  Google Scholar 

  29. Alkhatib AA, Alsabbagh A, Maraqa R, Alzubi S Load balancing techniques in cloud computing: extensive review

  30. Mohanty SP, Patra K, Ray M, Mohapatra S (2021) A novel meta-heuristic approach for load balancing in cloud computing. In: Research anthology on architectures, frameworks, and integration strategies for distributed and cloud computing, IGI Global, pp 504–526

  31. Zuo L, Shu L, Dong S, Zhu C, Hara T (2015) A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access 3:2687–2699. https://doi.org/10.1109/ACCESS.2015.2508940

    Article  Google Scholar 

  32. Dewantoro RW, Sihombing P, Sutarman (2019) The combination of ant colony optimization (ACO) and Tabu search (TS) algorithm to solve the traveling salesman problem (TSP). In: 2019 3rd international conference on electrical, telecommunication and computer engineering (ELTICOM), Medan, Indonesia, pp 160–164. https://doi.org/10.1109/ELTICOM47379.2019.8943832

  33. Noman MA, Alatefi M, Al-Ahmari AM, Ali T (2021) Tabu search algorithm based on lower bound and exact algorithm solutions for minimizing the Makespan in non-identical parallel machines scheduling. Math Probl Eng 2021:1–9. https://doi.org/10.1155/2021/1856734

    Article  Google Scholar 

  34. Buyya R, Ranjan R, Calheiros RN (2009) Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: challenges and opportunities. In: 2009 international conference on high performance computing and simulation, Leipzig, Germany, pp 1–11. https://doi.org/10.1109/HPCSIM.2009.5192685.

  35. Lu S, Liu X, Pei J, Thai MT, Pardalos PM (2018) A hybrid ABC-TS algorithm for the unrelated parallel-batching machines scheduling problem with deteriorating jobs and maintenance activity. Appl Soft Comput 66:168–182. https://doi.org/10.1016/j.asoc.2018.02.018

    Article  Google Scholar 

  36. Mohialdeen IA (2013) Comparative study of scheduling al-gorithms in cloud computing environment. J Comput Sci 9(2):252–263

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Amity School of Engineering and Technology, Department of Computer Science and Engineering, Amity University Rajasthan, Jaipur, India

    Jyotsna P. Gabhane & Sunil Pathak

  2. Department of Computer Technology, Priyadarshini College of Engineering, Nagpur, Maharashtra, India

    Nita M. Thakare

Authors
  1. Jyotsna P. Gabhane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sunil Pathak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nita M. Thakare
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sunil Pathak.

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

Gabhane, J.P., Pathak, S. & Thakare, N.M. A novel hybrid multi-resource load balancing approach using ant colony optimization with Tabu search for cloud computing. Innovations Syst Softw Eng 19, 81–90 (2023). https://doi.org/10.1007/s11334-022-00508-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11334-022-00508-9

Keywords

Search

Navigation