Skip to main content
SpringerLink
Log in

Load Balancing Algorithms in Cloud Computing Environment—An Effective Survey

  • Conference paper
  • First Online:
Congress on Intelligent Systems

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 114))

  • 473 Accesses

Abstract

In recent years, the usage of Internet services and the number of users accessing the cloud systems are increased tremendously since the cloud offers enormous services to users and allows them to access its services from anywhere at any time in a flexible manner. Cloud computing is an emerging technology which has high performance and high throughput systems that can handle multiple users requests simultaneously. However, handling multiple user requests is a major challenge as number of requests increased day by day. It is very difficult for a server to manage all these users request at one time. Sometimes it may result in system breakdown and overloading of servers which causes load unbalancing. Load balancing is a technique in cloud computing that solves the problem of load unbalancing by evenly distributes the user’s request among multiple servers in an optimized way. In this paper, we present an overview of various load balancing algorithms proposed by several authors in recent years with respect to different load balancing metrics and tools used.

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 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
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

References

  1. Gundu SR, Panem CA, Thimmapuram A (2020) Real-time cloud-based load balance algorithms and an analysis. SN Comput Sci 1:1–9

    Article  Google Scholar 

  2. Kumar S, Rana DS (2015) Various dynamic load balancing algorithms in cloud environment: a survey. Int J Com Appl 129(6):16

    Google Scholar 

  3. Kaur A, Luthra MP (2018) A review on load balancing in cloud environment. Int J 17(1)

    Google Scholar 

  4. Eladl SG, Ziedan NI, Gaafar TS (2019) Cloud computing load balancing using genetic and throttled hybrid algorithm. Int J Eng Technol 11(3):606–626

    Article  Google Scholar 

  5. Xu M, Tian W, Buyya R (2017) A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurrency Comput Pract Experience 29(12):e4123

    Google Scholar 

  6. Kherani FF, Vania J (2014) Load balancing in cloud computing. Int J Eng Develop Res 2(1)

    Google Scholar 

  7. Khatavkar B, Boopathy P (2017) Efficient WMaxMin static algorithm for load balancing in cloud computation. In: Innovations in power and advanced computing technologies (i-PACT), pp 1–6. IEEE

    Google Scholar 

  8. Chen H, Liu Q, Ai Q (2016) A new heuristic scheduling strategy lbmm in cloud computing. In: 8th international conference on intelligent human-machine systems and cybernetics (IHMSC), Vol 1, pp 314–317. IEEE

    Google Scholar 

  9. Chen H, Wang F, Helian N, kanmu G (2013) User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing. In: 2013 National Conference on Parallel computing technologies (PARCOMPTECH), pp 1–8. IEEE

    Google Scholar 

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

    Article  Google Scholar 

  11. Verma P, Shrivastava S, Pateriya RK (2017) Enhancing load balancing in cloud computing by ant colony optimization method. Int J Comput Eng Res Trends 4(6):277–284

    Google Scholar 

  12. Pham NMN, Le VS (2017) Applying ant colony system algorithm in multi-objective resource allocation for virtual services. J Inform Telecommun 1(4):319–333

    Article  Google Scholar 

  13. Phi NX, Tin CT, Thu LNK, Hung TC (2018) Proposed load balancing algorithm to reduce response time and processing time on cloud computing. Int J Comput Netw Commun 10(3):87–98

    Article  Google Scholar 

  14. Ghosh S, Banerjee C (2018) Dynamic time quantum priority based round robin for load balancing in cloud environment. In: 2018 4th International conference on research in computational intelligence and communication networks (ICRCICN) (pp. 33–37). IEEE

    Google Scholar 

  15. Tripathi S, Prajapati S, Ansari NA (2017) Modified optimal algorithm: for load balancing in cloud computing. In: 2017 International conference on computing, communication and automation (ICCCA), pp 116–121. IEEE

    Google Scholar 

  16. Maltare J, Prajapat B (2016) Dynamic load balancing in cloud computing using cloudsim. Int J Comput Appl 148(5)

    Google Scholar 

  17. Malhotra D, LD_ASG (2018) Load balancing algorithm in cloud computing. In: 2018 Fifth international conference on parallel, distributed and grid computing (PDGC), pp 387–392. IEEE

    Google Scholar 

  18. Sharma M, Jain VK (2019) Load balancing in cloud using hybrid approach. Int J Innovative Technol Exploring Eng (IJITEE)

    Google Scholar 

  19. Xu P, He G, Li Z, Zhang Z (2018) An efficient load balancing algorithm for virtual machine allocation based on ant colony optimization. Int J Distrib Sens Netw 14(12):1550147718793799

    Article  Google Scholar 

  20. Chiang ML, Hsieh HC, Tsai WC, Ke MC (2017) An improved task scheduling and load balancing algorithm under the heterogeneous cloud computing network. In: 2017 IEEE 8th international conference on awareness science and technology (iCAST), pp 290–295. IEEE

    Google Scholar 

  21. Islam T, Hasan MS (2017) 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

    Google Scholar 

  22. Volkova VN, Chemenkaya LV, Desyatirikova EN, Hajali M, Khodar A, Osama A (2018) Load balancing in cloud computing. In: 2018 IEEE conference of Russian young researchers in electrical and electronic engineering (EIConRus), pp 387–390. IEEE

    Google Scholar 

  23. Aslam S, Shah MA (2015) Load balancing algorithms in cloud computing: a survey of modern techniques. In: 2015 National software engineering conference (NSEC), pp 30–35. IEEE

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, Chennai, India

    N. Priya & S. Shanmuga Priya

Authors
  1. N. Priya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Shanmuga Priya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N. Priya .

Editor information

Editors and Affiliations

  1. Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, India

    Mukesh Saraswat

  2. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India

    Harish Sharma

  3. Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India

    K. Balachandran

  4. Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  5. South Asian University, New Delhi, India

    Jagdish Chand Bansal

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Priya, N., Shanmuga Priya, S. (2022). Load Balancing Algorithms in Cloud Computing Environment—An Effective Survey. In: Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. Lecture Notes on Data Engineering and Communications Technologies, vol 114. Springer, Singapore. https://doi.org/10.1007/978-981-16-9416-5_20

Download citation

Publish with us

Policies and ethics

Search

Navigation