Skip to main content
SpringerLink
Log in

Analysis of Load Balancing Algorithms Used in the Cloud Computing Environment: Advantages and Limitations

  • Conference paper
  • First Online:
Proceedings of the Future Technologies Conference (FTC) 2022, Volume 3 (FTC 2022 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 561))

Included in the following conference series:

Abstract

Cloud computing as an advanced technology in the IT infrastructure presents nowadays a big concern of researches. It’s no longer a matter of on-demand successful delivery of computing resources. Throughput, performance, server response time, and cost had become the metrics that enable the quality-of-service agreement. Technically, cloud service provider guarantees to deliver computing resources (storage, servers and applications) through back-end data center. It consists of several hosts distributed geographically to answer the client requests. To ensure the service level agreement between clients and providers, cloud infrastructure software need to schedule and optimally manage the workload of several demands. Here, Load balancing technology enters as a major key with a set of algorithms to handle the most effectively and fairly the allocation and scheduling of computational resources, to serve the large amount of calling jobs. This review presents a comparative and comprehensive study that covers the principal concepts of cloud computing, and the well-known algorithms used for load balancing which are classified into static and dynamic sets. The objectives of this survey are to (1) mention, explain, compare and analyze some developed methods for load balancing by systematically reviewing papers from the years 2018 to 2021, (2) analyze the level of maturity of the solutions proposed in the literature and (3) present an insight into the actual solutions which may help with future improvements.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Data Availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Patel, K.D., Bhalodia, T.M.: An efficient dynamic load balancing algorithm for virtual machine in cloud computing. IEEE Xplore Part Number: CFP19K34-ART (2019). ISBN: 978-1-5386-8113-8

    Google Scholar 

  2. Salah al-Ahmad, A., Kahtan, H.: Cloud computing review: features and issues, 978-1-5386-5630-3/18/$31.00. IEEE (2018)

    Google Scholar 

  3. Manikandan, N., Pavin, A.: Comprehensive solution of scheduling and balancing load in cloud – a review. IEEE Xplore Part Number: CFP19OSV-ART (2019). ISBN: 978-1-7281-4365-1

    Google Scholar 

  4. Sahu, S., Pandey, M.: Efficient load balancing algorithm analysis in cloud computing. In: Proceedings of the Fourth International Conference on Communication and Electronics Systems, ICCES (2019)

    Google Scholar 

  5. Liu, F., et al.: NIST Cloud Computing Reference Architecture Special Publication 500-292 (2011)

    Google Scholar 

  6. Hentschel, R., Strahringer, S.: A broker-based framework for the recommendation of cloud services: a research proposal. In: Hattingh, M., Matthee, M., Smuts, H., Pappas, I., Dwivedi, Y.K., Mäntymäki, M. (eds.) I3E 2020. LNCS, vol. 12066, pp. 409–415. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44999-5_34

    Chapter  Google Scholar 

  7. Jyoti, A., Shrimali, M., Tiwari, S., Pratap Singh, H.: Cloud computing using load balancing and service broker policy for IT service: a taxonomy and survey. J. Ambient Intell. Humaniz. Comput. 11, 4785–4814 (2020)

    Article  Google Scholar 

  8. Ben Hamouda, R., Boussema, S., Ben Hafaiedh, I., Robbana, R.: Performance evaluation of dynamic load balancing protocols based on formal models in cloud environments. In: Atig, M.F., Bensalem, S., Bliudze, S., Monsuez, B. (eds.) VECoS. LNCS, vol. 11181, pp. 64–79. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00359-3_5

    Chapter  Google Scholar 

  9. Geeta, Prakash, S.: A literature review of QoS with load balancing in cloud computing environment. In: Aggarwal, V., Bhatnagar, V., Mishra, D. (eds.) Big Data Analytics. AISC, vol. 654, pp. 667–675. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-6620-7_64

  10. Ala’anzi, M., Othman, M.: Load balancing and server consolidation in cloud computing environments: a meta-study (2019). https://doi.org/10.1109/ACCESS.2019.2944420

  11. Asim Shahid, M., Islam, N., Alam, M., Mazliham, M.S., Musa, S.: A comprehensive study of load balancing approaches in the cloud computing environment and a novel fault tolerance approach. IEEE Access (2020). https://doi.org/10.1109/ACCESS.2020.3009184

  12. AlKhatib, A.A.A., Sawalha, T., AlZu’bi, S.: Load balancing techniques in software-defined cloud computing: an overview. In: Seventh International Conference on Software Defined Systems (SDS) (2020)

    Google Scholar 

  13. Ramya, R., Puspalatha, S., Hemalatha, T., Bhuvana, M.: A survey on and performance analysis of load balancing algorithms using meta heuristics approach in public cloud-service provider’s perspective, 978-1-5386-9432-9/18/$31.00. IEEE (2018)

    Google Scholar 

  14. Sanaj, M.S., Joe Prathap, P.M: An Enhanced Round Robin (ERR) algorithm for effective and efficient task scheduling in cloud environment, 978-1-7281-6453-3/20/$31.00. IEEE (2020)

    Google Scholar 

  15. Singh, G., Kaur, K.: An improved weighted least connection scheduling algorithm for load balancing in web cluster systems. Int. Res. J. Eng. Technol. (IRJET) (2018)

    Google Scholar 

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

    Google Scholar 

  17. Srinivasa Rao, G., Charan Arur, P., Anuradha, T.: Real time cloud based load balance algorithms and an analysis. SN Computer Science (2020)

    Google Scholar 

  18. Yang, Q., Shao, Y., Cui, H., Fang, Y., Yang, D., Pan, Y.: Energy-aware and load balancing based dynamic migration strategy for virtual machine. In: 4th International Conference on Recent Advances in Signal Processing, Telecommunications Computing (SigTelCom) (2020)

    Google Scholar 

  19. Sudhakar, C., Jain, R., Ramesh, T.: Cloud load balancing - honey bees inspired effective request balancing strategy. In: International Conference on Computing, Power and Communication Technologies (GUCON) (2018)

    Google Scholar 

  20. Naregal, K., Kalmani, V.: Study of lightweight ABE for cloud based IoT. In: Proceedings of the Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (2020)

    Google Scholar 

Download references

Funding

This research didn’t receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. ENSEM, Hassan 2 University of Casablanca (UH2C), Casablanca, Morocco

    Zakariyae Bouflous

  2. EST, Hassan 2 University of Casablanca (UH2C), Casablanca, Morocco

    Mohammed Ouzzif & Khalid Bouragba

Authors
  1. Zakariyae Bouflous
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammed Ouzzif
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khalid Bouragba
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Z.B wrote the main manuscript except conclusion, and prepared Fig. 5, 6, 7, 8 and 9. He contributed in gathering data of related works and analysis of each algorithm.

M.O prepared Fig. 1 and 2 and the conclusion of the manuscript, and contributed in gathering and choosing data of related works, thus analysis of each algorithm in the article.

K.B prepared Fig. 3 and 4 and contributes in gathering and choosing data of related works, thus analysis of each algorithm in the article

All authors reviewed the manuscript twice.

Corresponding author

Correspondence to Zakariyae Bouflous .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

Ethics declarations

The authors declare no competing financial interests.

List of Abbreviations

List of Abbreviations

  • A-

  • ACA: Ant Colony Algorithm

  • C-

  • CSP: Cloud Service Provider

  • CSC: Cloud Service Consumer

  • CSB: Cloud Service Broker

  • CC: Cloud Computing

  • CPU: Central Processing Unit

  • D-

  • DCC: Data Center Coalition

  • E-

  • ERR: Enhanced Round Robin

  • EALB: Energy Aware Load Balancing

  • F-

  • FT: Fault Tolerance

  • FCFS: First Come First Serve

  • G-

  • GA: Genetic Algorithm

  • H-HW: Hardware

  • I-

  • IaaS: Infrastructure as a Service

  • IT: Information Technology

  • I/O: Input/Output

  • L-

  • LB: Load Balancing

  • LC: Least Connection

  • LBMM: Load Balancing Min-Min

  • M-

  • MAMLB: Modified Active Monitoring Load Balancer

  • MTA: Modified Throttled Algorithm

  • N-

  • NIST: National Institute of standards and Technology

  • O-

  • OLB: Opportunistic Load Balancing

  • P-

  • PaaS: Platform as a Service

  • PM: Physical Machine

  • Q-

  • QoS: Quality of Service

  • QT: Quantum Time

  • R-

  • RR: Round Robin

  • S-

  • SaaS: Software as a Service

  • SW: Software SIP: Session Initiation Protocol

  • SLA: Service Level Agreement

  • V-

  • VM: Virtual Machine

  • VMM: Virtual Machine Manager

  • W-

  • WAN: Wide Area Network

  • WLC: Weighted Least Connection

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Note

The NIST defines CC as ‘a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction’ [5].

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bouflous, Z., Ouzzif, M., Bouragba, K. (2023). Analysis of Load Balancing Algorithms Used in the Cloud Computing Environment: Advantages and Limitations. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 3. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 561. Springer, Cham. https://doi.org/10.1007/978-3-031-18344-7_13

Download citation

Publish with us

Policies and ethics

Search

Navigation