Advertisement

Advertisement

A comprehensive examination of load balancing algorithms in cloud environments: a systematic literature review, comparative analysis, taxonomy, open challenges, and future trends

  • Published: 24 April 2024
  • Volume 7 , pages 663–698, ( 2024 )

Cite this article

cloud computing algorithms research paper

  • Farida Siddiqi Prity 1 , 3 &
  • Md. Maruf Hossain 2 , 3  

216 Accesses

Explore all metrics

Cloud computing is a robust paradigm that empowers users and organizations to procure services tailored to their needs. This model encompasses many offerings, including storage solutions, platforms for seamless deployment, and convenient access to web services. Load balancing, a fundamental pillar in cloud computing, is crucial in distributing requests across multiple servers to optimize resource utilization and reduce response times. However, load balancing presents a common challenge in the cloud environment, as it hampers the ability to maintain optimal application performance while adhering to the stringent requirements of Quality of Service (QoS) measurements and Service Level Agreement (SLA) compliance mandated by cloud providers to enterprises. The equitable workload distribution across servers poses a significant challenge for cloud providers. Hence, an efficient load-balancing technique should optimize resource utilization in Virtual Machines (VMs) to ensure maximum user satisfaction and overall system efficiency. However, existing review papers on load balancing in cloud environments often exhibit limitations, lacking in-depth analyses, graphical representations, and comprehensive evaluations of performance metrics. This review paper aims to fill these gaps by providing a novel taxonomy of load balancing algorithms divided into four categories (types of algorithms, nature of problem, metrics, and simulation tools) and thoroughly examining their objectives, parameters, and operational flows. It evaluates the strengths and weaknesses of these algorithms, considering their nature and type, and employs qualitative QoS parameter-based criteria for effectiveness evaluation. The paper also includes a comparative analysis of simulation tools, visual representations, and experimental results. By offering valuable insights, open issues, recommendations, and future directions, this review paper equips researchers, practitioners, and cloud service providers with the knowledge to make informed decisions in selecting and optimizing load-balancing strategies for diverse cloud environments.

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

Access this article

Subscribe and save.

  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

cloud computing algorithms research paper

Similar content being viewed by others

cloud computing algorithms research paper

Load Balancing and Its Challenges in Cloud Computing: A Review

cloud computing algorithms research paper

Load Balancing Algorithms for Cloud Computing Performance: A Review

cloud computing algorithms research paper

Load Balancing in Cloud—A Systematic Review

Explore related subjects.

  • Artificial Intelligence

Availability of data and materials

The datasets generated during the current study are available from the corresponding author on reasonable request.

Luo, Y., Chen, Y., Li, T., Tan, C., Dou, H.: Cloud-SMPC: two-round multilinear maps secure multiparty computation based on LWE assumption. J. Cloud Comput. 13 (1), 22 (2024)

Article   Google Scholar  

Maurya, M., Panigrahi, I., Dash, D., Malla, C.: Intelligent fault diagnostic system for rotating machinery based on IoT with cloud computing and artificial intelligence techniques: a review. Soft. Comput. 28 (1), 477–494 (2024)

Mikram, H., El Kafhali, S., Saadi, Y.: HEPGA: a new effective hybrid algorithm for scientific workflow scheduling in cloud computing environment. Simul. Model. Pract. Theory 130 , 102864 (2024)

Shafiq, D.A., Jhanjhi, N.Z., Abdullah, A.: Load balancing techniques in cloud computing environment: a review. J. King Saud Univ. Comput. Inf. Sci. 34 (7), 3910–3933 (2022)

Google Scholar  

Chen, X., Li, J., Chen, D., Zhou, Y., Tu, Z., Lin, M., Kang, T., Lin, J., Gong, T., Zhu, L., Zhou, J.: CloudBrain-MRS: an intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis. J. Magn. Reson. 358 , 107601 (2024)

Kumar, A., Chawla, P.: A systematic literature review on load balancing algorithms of virtual machines in a Cloud computing environment. In: Proceedings of the International Conference on Innovative Computing and Communications (ICICC) (2020, March)

Mishra, S.K., Sahoo, B., Parida, P.P.: Load balancing in cloud computing: a big picture. J. King Saud Univ. Comput. Inf. Sci. 32 (2), 149–158 (2020)

Megharaj, G., Mohan, K.G.: A survey on load balancing techniques in cloud computing. IOSR J. Comput. Eng. (IOSR-JCE) 18 (2), 55–61 (2016)

Kumar, B.S., Parthiban, D.L.: An implementation of load balancing policy for virtual machines associated with a data centre. Int. J. Comput. Sci. Eng. Technol. (IJCSET) 5 (03), 253–261 (2014)

Kumar, P., Kumar, R.: Issues and challenges of load balancing techniques in cloud computing: a survey. ACM Comput. Surv. (CSUR) 51 (6), 1–35 (2019)

Lakhwani, K.: ‘An extensive survey on load balancing techniques in cloud computinG. J. Gujarat Res. Soc. 21 (10s), 309–319 (2019)

Afzal, S., Kavitha, G.: Load balancing in cloud computing—a hierarchical taxonomical classification. J. Cloud Comput. 8 (1), 22 (2019)

Kathalkar, P.R., Deorankar, A.V.: A review on different load balancing algorithm in cloud computing. Int. Res. J. Eng. Technol. 5 (2), 1–3 (2018)

Kumar, D.S., Dharma Prakash Raj, E.G.: A literature review on load balancing mechanisms in cloud computing. Int. J. Adv. Res. Comput. Sci. 9 (1), 1 (2018)

Kaur, M., Verma, D.B.: A review on various load balancing algorithms with Merits–Demerits in cloud computing. Int. J. Adv. Eng. Res. Dev. 5 (5), 1 (2018)

Hota, A., Mohapatra, S., Mohanty, S.: Survey of different load balancing approach-based algorithms in cloud computing: a comprehensive review. In: Computational Intelligence in Data Mining: Proceedings of the International Conference on CIDM 2017, pp. 99–110. Springer, Singapore (2019)

Mishra, K., Majhi, S.: A state-of-art on cloud load balancing algorithms. Int. J. Comput. Digit. Syst. 9 (2), 201–220 (2020)

Hamadah, S.: A survey: a comprehensive study of static, dynamic and hybrid load balancing algorithms. Int. J. Comput. Sci. Inf. Technol. Secur. (IJCSITS), ISSN, 2249–9555 (2017)

Sutagatti, S.S., Kulkarni, S.G.: Comparative analysis and evaluation of load balancing algorithms. Int. J. Comput. Appl. 171 (5), 6–11 (2017)

Deepa, T., Cheelu, D.: A comparative study of static and dynamic load balancing algorithms in cloud computing. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 3375–3378. IEEE (2017, August)

Archana, M., Shastry, M.: A review paper on various load balancing algorithms in cloud computing. J. Eng. Appl. Sci. 12 (9), 8579–8585 (2017)

Gupta, S., Dixit, A., Dev, H.: A study on various load balancing algorithms for response time reduction in cloud. Int. J. Curr. Eng. Sci. Res. (IJCESR) 4 (10), 1 (2017)

Thakur, A., Goraya, M.S.: A taxonomic survey on load balancing in cloud. J. Netw. Comput. Appl. 98 , 43–57 (2017)

Alam, M., Khan, Z.A.: Issues and challenges of load balancing algorithm in cloud computing environment. Indian J. Sci. Technol. 10 (25), 1–12 (2017)

Joshi, S., Kumari, U.: A comprehensive analysis of existing load balancing algorithms in cloud network. Mody Univ. Int. J. Comput. Eng. Res. 1 (2), 71–75 (2017)

Singh, A.B., Bhat, S., Raju, R., D’Souza, R.: Survey on various load balancing techniques in cloud computing. Adv. Comput. 7 (2), 28–34 (2017)

Milani, A.S., Navimipour, N.J.: Load balancing mechanisms and techniques in the cloud environments: systematic literature review and future trends. J. Netw. Comput. Appl. 71 , 86–98 (2016)

Elngomi, Z.M., Khanfar, K.: A comparative study of load balancing algorithms: a review paper. Int. J. Comput. Sci. Mob. Comput. 5 (6), 448–458 (2016)

Goyal, S., Verma, M.K.: Load balancing techniques in cloud computing environment: a review. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 6 (4), 583–588 (2016)

Gabi, D., Ismail, A.S., Zainal, A.: Systematic review on existing load balancing techniques in cloud computing. Int. J. Comput. Appl. 125 (9), 16–24 (2015)

Karthika, K., Kanakambal, K., Balasubramaniam, R.: Load balancing algorithm review’s in cloud environment. IJERGS 3 (3), 661–667 (2015)

Aslam, S., Shah, M.A.: Load balancing algorithms in cloud computing: a survey of modern techniques. In 2015 National Software Engineering Conference (NSEC), pp. 30–35. IEEE (2015, December)

Kapoor, S.: A survey on dynamic load balancing algorithms in cloud computing. Adv. Comput. Sci. Inf. Technol 2 (7), 87–91 (2015)

Sanghavi, H.S., Patalia, D.T.P.: Load balancing algorithms for the cloud computing environment: a review. J. Inf. Knowl. Res. Comput. Eng. 3 (2), 591–598 (2014)

Kaur, R., Luthra, P.: Load balancing in cloud system using max min and min–min algorithm. Int. J. Comput. Appl. 975 , 8887 (2014)

Shafiq, D.A., Jhanjhi, N.Z., Abdullah, A.: Proposing a load balancing algorithm for the optimization of cloud computing applications. In: 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), pp. 1–6. IEEE (2019, December)

Shah, N., Farik, M.: Static load balancing algorithms in cloud computing: challenges and solutions. Int. J. Sci. Technol. Res. 4 (10), 365–367 (2015)

Islam, T., Hasan, M.S.: A performance comparison of load balancing algorithms for cloud computing. In: 2017 International Conference on the Frontiers and Advances in Data Science (FADS), pp. 130–135. IEEE (2017, October)

Rathore, J., Keswani, B., Rathore, V.S.: Analysis of load balancing algorithms using cloud analyst. In: Emerging Trends in Expert Applications and Security: Proceedings of ICETEAS 2018, pp. 291–298. Springer, Singapore (2019)

Nazar, T., Javaid, N., Waheed, M., Fatima, A., Bano, H., Ahmed, N.: Modified shortest job first for load balancing in cloud-fog computing. In: Advances on Broadband and Wireless Computing, Communication and Applications: Proceedings of the 13th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA-2018), pp. 63–76. Springer, London (2019)

Seth, S., Singh, N.: Dynamic heterogeneous shortest job first (DHSJF): a task scheduling approach for heterogeneous cloud computing systems. Int. J. Inf. Technol. 11 (4), 653–657 (2019)

Mondal, R.K., Nandi, E., Sarddar, D.: Load balancing scheduling with shortest load first. Int. J. Grid Distrib. Comput. 8 (4), 171–178 (2015)

Zakria, M., Javaid, N., Ismail, M., Zubair, M., Asad Zaheer, M., Saeed, F.: Cloud-fog based load balancing using shortest remaining time first optimization. In: Advances on P2P, Parallel, Grid, Cloud and Internet Computing: Proceedings of the 13th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC-2018), pp. 199–211. Springer, London (2019)

Tailong, V., Dimri, V.: Load balancing in cloud computing using modified optimize response time. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 6 (5), 1 (2016)

Kaurav, N.S., Yadav, P.: A genetic algorithm-based load balancing approach for resource optimization for cloud computing environment. Int. J. Inf. Comput. Sci. 6 (3), 175–184 (2019)

Issawi, S.F., Al Halees, A., Radi, M.: An efficient adaptive load balancing algorithm for cloud computing under Bursty workloads. Eng. Technol. Appl. Sci. Res. 5 (3), 795–800 (2015)

Richhariya, V., Dubey, R., Siddiqui, R.: Hybrid technique for load balancing in cloud computing using modified round robin algorithms. J. Comput. Math. Sci. 6 (12), 688–695 (2015)

Richhariya, V., Dubey, R., Siddiqui, R.: Hybrid approach for load balancing in cloud computing. Orient. J. Comput. Sci. Technol. 8 (3), 241–246 (2015)

Pasha, N., Agarwal, A., Rastogi, R.: Round robin approach for VM load balancing algorithm in cloud computing environment. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4 (5), 34–39 (2014)

Khatavkar, B., Boopathy, P.: Efficient WMaxMin static algorithm for load balancing in cloud computation. In: 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), pp. 1–6. IEEE (2017, April)

Moly, M.I., Hossain, A., Lecturer, S., Roy, O.: Load balancing approach and algorithm in cloud computing environment. Am. J. Eng. Res. 8 (4), 99–105 (2019)

Mayur, S., Chaudhary, N.: Enhanced weighted round robin load balancing algorithm in cloud computing. Int. J. Innov. Technol. Explor. Eng. 8 (9), 148–151 (2019)

James, J., Verma, B.: Efficient VM load balancing algorithm for a cloud computing environment. Int. J. Comput. Sci. Eng. 4 (9), 1658 (2012)

Manaseer, S., Alzghoul, M., Mohmad, M.: An advanced algorithm for load balancing in cloud computing using MEMA technique. Int. J. Innov. Technol. Explor. Eng 8 (3), 36–41 (2019)

Manikandan, N., Pravin, A.: An efficient improved weighted round Robin load balancing algorithm in cloud computing. Int. J. Eng. Technol. (UAE) 7 (3.1), 110–117 (2018)

Chen, S.L., Chen, Y.Y., Kuo, S.H.: CLB: a novel load balancing architecture and algorithm for cloud services. Comput. Electr. Eng. 58 , 154–160 (2017)

Ali, S.A., Alam, M.: Resource-aware Min–Min (RAMM) algorithm for resource allocation in cloud computing environment. Preprint arXiv:1803.00045 (2018)

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

Shanthan, B.H., Arockiam, L.: Resource based load balanced min–min algorithm (RBLMM) for static meta task scheduling in cloud. In International conference on advances in computer science and technology. Int. J. Eng. Technol. Spec. 1–8 , 1 (2018)

Nayak, P., Vania, J., Robin, R.: Load balancing using modified Throttled algorithm. Int. J. Sci. Res. Dev. 3 (3), 3614–3616 (2015)

Ghosh, S., Banerjee, C.: Priority based modified throttled algorithm in cloud computing. In: 2016 International Conference on Inventive Computation Technologies (ICICT), vol. 3, pp. 1–6. IEEE (2016, August)

Phi, N.X., Tin, C.T., Thu, L.N.K., Hung, T.C.: Proposed load balancing algorithm to reduce response time and processing time on cloud computing. Int. J. Comput. Netw. Commun. 10 (3), 87–98 (2018)

Sachdeva, R., Kakkar, S.: A novel approach in cloud computing for load balancing using composite algorithms. Int. J. 7 (2), 198 (2017)

Subalakshmi, S., Malarvizhi, N.: Enhanced hybrid approach for load balancing algorithms in cloud computing. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 2 (2), 136–142 (2017)

Rathore, J., Keswani, B., Rathore, V.S.: An efficient load balancing algorithm for cloud environment. J. Invent. Comput. Sci. Commun. Technol. 4 (1), 37–41 (2018)

Aliyu, A.N., Souley, P.B.: Performance analysis of a hybrid approach to enhance load balancing in a heterogeneous cloud environment. Int. J. Adv. Sci. Res. Eng. 5 (7), 246–257 (2019)

Khanchi, M., Tyagi, S.: An efficient algorithm for load balancing in cloud computing. Int. J. Eng. Sci. Res. Technol. 5 (6), 468–475 (2016)

Alamin, M.A., Elbashir, M.K., Osman, A.A.: A load balancing algorithm to enhance the response time in cloud computing. J. Basic Appl. Sci. 2 (2), 473–490 (2017)

Mishra, S., Tondon, R.: A shared approach of dynamic load balancing in cloud computing. Int. J. Sci. Res. Sci. Eng. Technol. (ijsrset. com) 2 (02), 632–638 (2016)

Somani, R., Ojha, J.: A hybrid approach for VM load balancing in cloud using cloudsim. Int. J. Sci. Eng. Technol. Res. (IJSETR) 3 (6), 1734–1739 (2014)

Alankar, B., Sharma, G., Kaur, H., Valverde, R., Chang, V.: Experimental setup for investigating the efficient load balancing algorithms on virtual cloud. Sensors 20 (24), 7342 (2020)

Dam, S., Mandal, G., Dasgupta, K., Dutta, P.: Genetic algorithm and gravitational emulation based hybrid load balancing strategy in cloud computing. In: Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT), pp. 1–7. IEEE (2015, February)

Kaur, S., Sengupta, J.: Load balancing using improved genetic algorithm (IGA) in cloud computing. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 6 (8), 1323–2278 (2017)

Saadat, A., Masehian, E.: Load balancing in cloud computing using genetic algorithm and fuzzy logic. In: 2019 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1435–1440. IEEE (2019, Dec.)

Kaur, K., Kumar, Y.: Swarm intelligence and its applications towards various computing: a systematic review. In: 2020 International Conference on Intelligent Engineering and Management (ICIEM), pp. 57–62. IEEE (2020, June)

Yadav, A.: Load balancing in cloud computing environment using hybrid approach (ESCEL and PSO) algorithms. Adv. Comput. Sci. Inf. Technol. 2 (8), 10–13 (2015)

Alguliyev, R.M., Imamverdiyev, Y.N., Abdullayeva, F.J.: PSO-based load balancing method in cloud computing. Autom. Control. Comput. Sci. 53 , 45–55 (2019)

Golchi, M.M., Saraeian, S., Heydari, M.: A hybrid of firefly and improved particle swarm optimization algorithms for load balancing in cloud environments: performance evaluation. Comput. Netw. 162 , 106860 (2019)

Pan, K., Chen, J.: Load balancing in cloud computing environment based on an improved particle swarm optimization. In: 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 595–598. IEEE (2015, Sept.)

Miao, Z., Yong, P., Mei, Y., Quanjun, Y., Xu, X.: A discrete PSO-based static load balancing algorithm for distributed simulations in a cloud environment. Futur. Gener. Comput. Syst. 115 , 497–516 (2021)

Hashem, W., Nashaat, H., Rizk, R.: Honey bee based load balancing in cloud computing. KSII Trans. Internet Inf. Syst. 11 (12), 1 (2017)

George, M.S., Das, K.N., Pushpa, B.R.: Enhanced honeybee inspired load balancing algorithm for cloud environment. In: 2017 International Conference on Communication and Signal Processing (ICCSP), pp. 1649–1653. IEEE (2017, April)

Ehsanimoghadam, P., Effatparvar, M.: Load balancing based on bee colony algorithm with partitioning of public clouds. Int. J. Adv. Comput. Sci. Appl. 9 (4), 1 (2018)

Kiritbhai, P.B., Shah, N.Y.: Optimizing load balancing technique for efficient load balancing. Int. J. Innov. Res. Technol. 4 (6), 39–44 (2017)

Gundu, S.R., Anuradha, T.: Improved hybrid algorithm approach based load balancing technique in cloud computing. Global J. Comput. Sci. Technol. 2019 , 1 (2019)

Kumar, R., Prashar, T.: Performance analysis of load balancing algorithms in cloud computing. Int. J. Comput. Appl. 120 (7), 1 (2015)

Dam, S., Mandal, G., Dasgupta, K., Dutta, P.: An ant colony based load balancing strategy in cloud computing. In: Advanced Computing, Networking and Informatics-Volume 2: Wireless Networks and Security Proceedings of the Second International Conference on Advanced Computing, Networking and Informatics (ICACNI-2014), pp. 403–413. Springer, London (2014)

Selvakumar, A., Gunasekaran, D.G.: A novel approach in load balancing for dynamic cloud environment using ACO. Int. Innov. Res. J. Eng. Technol. 2 (04), 67–70 (2017)

Singh, G.S., Vivek, T.: Implementation of a hybrid load balancing algorithm for cloud computing. Int. J. Adv. Technol. Eng. Sci. 3 (1), 73–81 (2015)

Gupta, A., Garg, R.: Load balancing based task scheduling with ACO in cloud computing. In: 2017 International Conference on Computer and Applications (ICCA), pp. 174–179. IEEE (2017, Sept.)

Ragmani, A., Elomri, A., Abghour, N., Moussaid, K., Rida, M.: An improved hybrid fuzzy-ant colony algorithm applied to load balancing in cloud computing environment. Proc. Comput. Sci. 151 , 519–526 (2019)

Junaid, M., Sohail, A., Ahmed, A., Baz, A., Khan, I.A., Alhakami, H.: A hybrid model for load balancing in cloud using file type formatting. IEEE Access 8 , 118135–118155 (2020)

Kumar, A., Kumar, D., Jarial, S.K.: A review on artificial bee colony algorithms and their applications to data clustering. Cybern. Inf. Technol. 17 (3), 3–28 (2017)

MathSciNet   Google Scholar  

Li, J.Q., Han, Y.Q.: A hybrid multi-objective artificial bee colony algorithm for flexible task scheduling problems in cloud computing system. Clust. Comput. 23 (4), 2483–2499 (2020)

Remesh Babu, K.R., Samuel, P.: Enhanced bee colony algorithm for efficient load balancing and scheduling in cloud. In: Innovations in Bio-Inspired Computing and Applications: Proceedings of the 6th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2015) held in Kochi, India during December 16–18, 2015, pp. 67–78. Springer, London (2016)

Abed-Alguni, B.H., Alawad, N.A.: Distributed Grey Wolf Optimizer for scheduling of workflow applications in cloud environments. Appl. Soft Comput. 102 , 107113 (2021)

Faris, H., Aljarah, I., Al-Betar, M.A., Mirjalili, S.: Grey wolf optimizer: a review of recent variants and applications. Neural Comput. Appl. 30 , 413–435 (2018)

Gohil, B.N., Patel, D.R.: A hybrid GWO-PSO algorithm for load balancing in cloud computing environment. In: 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), pp. 185–191. IEEE (2018, August)

Xingjun, L., Zhiwei, S., Hongping, C., Mohammed, B.O.: A new fuzzy-based method for load balancing in the cloud-based Internet of things using a grey wolf optimization algorithm. Int. J. Commun. Syst. 33 (8), e4370 (2020)

Ouhame, S., Hadi, Y.: A hybrid grey wolf optimizer and artificial bee colony algorithm used for improvement in resource allocation system for cloud technology. Int. J. Online Biomed. Eng. 16 (14), 1 (2020)

Ullah, A., Nawi, N.M., Khan, M.H.: BAT algorithm used for load balancing purpose in cloud computing: an overview. Int. J. High Perform. Comput. Netw. 16 (1), 43–54 (2020)

Shaddad, R.Q., Mohammad, A.B., Al-Gailani, S.A., Al-Hetar, A.M.: Optical frequency upconversion technique for transmission of wireless MIMO-type signals over optical fiber. Sci. World J. 2014 , 1 (2014)

Raj, B., Ranjan, P., Rizvi, N., Pranav, P., Paul, S.: Improvised bat algorithm for load balancing-based task scheduling. In: Progress in Intelligent Computing Techniques: Theory, Practice, and Applications: Proceedings of ICACNI 2016, Volume 1, pp. 521–530. Springer, Singapore (2018)

Fahim, Y., Rahhali, H., Hanine, M., Benlahmar, E.H., Labriji, E.H., Hanoune, M., Eddaoui, A.: Load balancing in cloud computing using meta-heuristic algorithm. J. Inf. Process. Syst. 14 (3), 1 (2018)

Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95 , 51–67 (2016)

Kaur, G., Arora, S.: Chaotic whale optimization algorithm. J. Computa. Des. Eng. 5 (3), 275–284 (2018)

Strumberger, I., Bacanin, N., Tuba, M., Tuba, E.: Resource scheduling in cloud computing based on a hybridized whale optimization algorithm. Appl. Sci. 9 (22), 4893 (2019)

Hemasian-Etefagh, F., Safi-Esfahani, F.: Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing. J. Supercomput. 75 (10), 6386–6450 (2019)

Chen, X., Cheng, L., Liu, C., Liu, Q., Liu, J., Mao, Y., Murphy, J.: A WOA-based optimization approach for task scheduling in cloud computing systems. IEEE Syst. J. 14 (3), 3117–3128 (2020)

James, J.Q., Li, V.O.: A social spider algorithm for global optimization. Appl. Soft Comput. 30 , 614–627 (2015)

Usurelu, C.C., Nita, M.C., Istrate, R., Pop, F., Tapus, N.: Spider mesh overlay for task load balancing in cloud computing. In: 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 433–440. IEEE (2015, September)

Mahato, D.P., Singh, R.S.: Balanced task allocation in the on-demand computing-based transaction processing system using social spider optimization. Concurr. Comput. Pract. Exp. 29 (18), e4214 (2017)

Arul Xavier, V.M., Annadurai, S.: Chaotic social spider algorithm for load balance aware task scheduling in cloud computing. Clust. Comput. 22 (Suppl 1), 287–297 (2019)

Abrol, P., Gupta, S., Singh, S.: QoS aware social spider cloud web algorithm: Analysis of resource placement approach. In: Proceedings of International Conference on Advancements in Computing & Management (ICACM) (2019, October)

Polepally, V., Shahu Chatrapati, K.: Dragonfly optimization and constraint measure-based load balancing in cloud computing. Clust. Comput. 22 (Suppl 1), 1099–1111 (2019)

Branch, S.R., Rey, S.: Providing a load balancing method based on dragonfly optimization algorithm for resource allocation in cloud computing. Int. J. Netw. Distrib. Comput. 6 (1), 35–42 (2018)

Neelima, P., Reddy, A.R.M.: An efficient load balancing system using adaptive dragonfly algorithm in cloud computing. Clust. Comput. 23 , 2891–2899 (2020)

Rani, E., Kaur, H.: Efficient load balancing task scheduling in cloud computing using raven roosting optimization algorithm. Int. J. Adv. Res. Comput. Sci. 8 (5), 1 (2017)

Torabi, S., Safi-Esfahani, F.: Improved raven roosting optimization algorithm (IRRO). Swarm Evol. Comput. 40 , 144–154 (2018)

Torabi, S., Safi-Esfahani, F.: A dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computing. J. Supercomput. 74 (6), 2581–2626 (2018)

Bhargavi, K., Babu, B.S.: Load balancing scheme for the public cloud using reinforcement learning with raven roosting optimization policy (RROP). In: 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), pp. 1–6. IEEE (2019, December)

Chaturvedi, M., Agrawal, P.D.: Optimal load balancing in cloud computing by efficient utilization of virtual machines. Int. J. Innov. Res. Comput. Commun. Eng. 5 (12), 17705–17713 (2017)

Singh, A.N., Prakash, S.: WAMLB: weighted active monitoring load balancing in cloud computing. In: Big Data Analytics: Proceedings of CSI 2015, pp. 677–685. Springer, Singapore (2018)

Soni, G., Kalra, M.: A novel approach for load balancing in cloud data center. In: 2014 IEEE International Advance Computing Conference (IACC), pp. 807–812. IEEE (2014, February)

Panwar, R., Mallick, B.: Load balancing in cloud computing using dynamic load management algorithm. In: 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), pp. 773–778. IEEE (2015, October)

Kaur, S., Sharma, T.: Efficient load balancing using improved central load balancing technique. In: 2018 2nd International Conference on Inventive Systems and Control (ICISC), pp. 1–5. IEEE (2018, January)

Haidri, R.A., Katti, C.P., Saxena, P.C.: A load balancing strategy for cloud computing environment. In: 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014), pp. 636–641. IEEE (2014, July)

Kumar, M., Sharma, S.C.: Dynamic load balancing algorithm for balancing the workload among virtual machine in cloud computing. Proc. Comput. Sci. 115 , 322–329 (2017)

Kumar, M., Sharma, S.C.: Dynamic load balancing algorithm to minimize the makespan time and utilize the resources effectively in cloud environment. Int. J. Comput. Appl. 42 (1), 108–117 (2020)

Nair, A., Anand, S., Sinha, S.: A performance booster for load balancing in cloud computing with my load balancer technique. Int. J. Recent Technol. Eng. 8 (1), 1 (2019)

Banerjee, S., Adhikari, M., Kar, S., Biswas, U.: Development and analysis of a new cloudlet allocation strategy for QoS improvement in cloud. Arab. J. Sci. Eng. 40 , 1409–1425 (2015)

Article   MathSciNet   Google Scholar  

Patel, P., Prajapati, D., Suthar, K.: An efficient and modified load balancing method for cloud computing. Int. J. Innov. Res. Comput. Commun. Eng. 5 (4), 8198–8205 (2017)

Al-Marhabi, R., Haggag, M., Aboutabl, A.E.: Roulette wheel selection model based on virtual machine weight for load balancing in cloud computing. IOSR J. Comput. Eng. 16 (5), 65–70 (2014)

Rekha, P.M., Dakshayini, M.: Dynamic cost-load aware service broker load balancing in virtualization environment. Proc. Comput. Sci. 132 , 744–751 (2018)

Bhatt, H.H., Bheda, H.A.: Enhance load balancing using Flexible load sharing in cloud computing. In: 2015 1st International Conference on Next Generation Computing Technologies (NGCT), pp. 72–76. IEEE (2015, September)

Semmoud, A., Hakem, M., Benmammar, B., Charr, J.C.: Load balancing in cloud computing environments based on adaptive starvation threshold. Concurr. Comput. Pract. Exp. 32 (11), e5652 (2020)

Kaur, S., Ghumman, M.N.S.: Allocation of heterogenous cloudlets on priority basis in cloud environment. Int. J. 16 (3), 1 (2017)

Kamboj, S., Ghumman, M.N.S.: An implementation of load balancing algorithm in cloud environment. Int. J. 15 (9), 1 (2016)

Kamboj, S., Ghumman, M.N.S.: A novel approach of optimizing performance using K-means clustering in cloud computing. Int. J. 15 (14), 1 (2016)

Domanal, S.G., Reddy, G.R.M.: Optimal load balancing in cloud computing by efficient utilization of virtual machines. In: 2014 6th International Conference on Communication Systems and Networks (COMSNETS), pp. 1–4. IEEE.sachdeva (2014, January)

Khaledian, N., Khamforoosh, K., Akraminejad, R., Abualigah, L., Javaheri, D.: An energy-efficient and deadline-aware workflow scheduling algorithm in the fog and cloud environment. Computing, 106 (1), 109–137 (2024)

Singh, S., Singh, P., Tanwar, S.: Energy aware resource allocation via MS-SLnO in cloud data center. Multimed. Tools Appl. 82 (29), 45541–45563 (2023)

Hima Bindu, G.B., Ramani, K., Shoba Bindu, C.: QOS enhanced energy aware task scheduling models in cloud computing. In: Intelligent Technologies: Concepts, Applications, and Future Directions, Volume 2, pp. 145–164. Springer, Singapore (2023)

Murad, S.A., Azmi, Z.R.M., Muzahid, A.J.M., Bhuiyan, M.K.B., Saib, M., Rahimi, N., Prottasha, N.J., Bairagi, A.K.: SG-PBFS: shortest gap-priority based fair scheduling technique for job scheduling in cloud environment. Futur. Gener. Comput. Syst. 150 , 232–242 (2024)

Ramezani Shahidani, F., Ghasemi, A., Toroghi Haghighat, A., Keshavarzi, A.: Task scheduling in edge-fog-cloud architecture: a multi-objective load balancing approach using reinforcement learning algorithm. Computing 105 (6), 1337–1359 (2023)

Belgacem, A., Mahmoudi, S., Ferrag, M.A.: A machine learning model for improving virtual machine migration in cloud computing. J. Supercomput. 2023 , 1–23 (2023)

Nebagiri, M.H., Hnumanthappa, L.P.: Multi-objective of load balancing in cloud computing using Cuckoo search optimization based simulation annealing. Int. J. Intell. Syst. Appl. Eng. 12 (9s), 466–474 (2024)

Junior, M.Y., Freire, R.Z., Seman, L.O., Stefenon, S.F., Mariani, V.C., dos Santos Coelho, L.: Optimized hybrid ensemble learning approaches applied to very short-term load forecasting. Int. J. Electr. Power Energy Syst. 155 , 109579 (2024)

Behera, I., Sobhanayak, S.: Task scheduling optimization in heterogeneous cloud computing environments: a hybrid GA-GWO approach. J. Parallel Distrib. Comput. 183 , 104766 (2024)

Download references

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and affiliations.

Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh

Farida Siddiqi Prity

Department of Information and Communication Engineering, Pabna University of Science and Technology, Pabna, 6600, Bangladesh

Md. Maruf Hossain

Department of Computer Science and Engineering, Shanto-Mariam University of Creative Technology, Dhaka, 1230, Bangladesh

Farida Siddiqi Prity & Md. Maruf Hossain

You can also search for this author in PubMed   Google Scholar

Contributions

Farida Siddiqi Prity: writing original draft, literature surveys, writing—review and editing; Md. Maruf Hossain: study conception and investigation on challenges.

Corresponding author

Correspondence to Farida Siddiqi Prity .

Ethics declarations

Conflict of interest.

The authors have no conflict of interest to disclose.

Consent for publication

Not applicable.

Ethics approval and consent to participate

This article does not contain any studies with human participants and 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

Prity, F.S., Hossain, M.M. A comprehensive examination of load balancing algorithms in cloud environments: a systematic literature review, comparative analysis, taxonomy, open challenges, and future trends. Iran J Comput Sci 7 , 663–698 (2024). https://doi.org/10.1007/s42044-024-00183-y

Download citation

Received : 29 January 2024

Accepted : 26 March 2024

Published : 24 April 2024

Issue Date : September 2024

DOI : https://doi.org/10.1007/s42044-024-00183-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Cloud computing
  • Load balancing
  • Find a journal
  • Publish with us
  • Track your research

IMAGES

  1. (PDF) Research Paper on Cloud Computing

    cloud computing algorithms research paper

  2. A comparative survey on load balancing algorithms in cloud

    cloud computing algorithms research paper

  3. Cloud Computing Research Paper Topics [IEEE Cloud Topics]

    cloud computing algorithms research paper

  4. (PDF) The Future of Cloud Computing: Opportunities, Challenges and

    cloud computing algorithms research paper

  5. Figure 1 from Analysis of Security Algorithms in Cloud Computing

    cloud computing algorithms research paper

  6. (PDF) A Review Paper on Cloud Computing

    cloud computing algorithms research paper