A Meta-Analysis Of Load Balancing and Server Consolidation In Distributed Computing Environments

Authors

  • Dharavath Champla
  • D. Siva Kumar
  • Shravanchandra G
  • Ch. Satyanarayana

Keywords:

Load Balancing, Server Consolidation, Distributed Computing, Energy Efficiency, Quality of Service (QoS), Meta-Analysis, Cloud Computing, Resource Utilization, Scalability, System Reliability

Abstract

The increasing demand for cloud computing services has significantly impacted server farms, placing substantial strain on the infrastructure. In distributed systems, dynamic computing patterns can lead to irregular load distribution across server farm resources, resulting in over- or under-loaded servers. This imbalance not only increases energy consumption but also leads to inefficient operations and resource wastage. While optimizing for energy efficiency through server consolidation is a common approach, it often exacerbates issues like uneven load distribution across physical machines (PMs), which can negatively impact system performance. This paper presents a comprehensive review of load balancing algorithms achieved through server consolidation in distributed computing environments. A meta-analysis of existing literature on this topic highlights key approaches that optimize resource utilization while improving Quality of Service (QoS) metrics. Additionally, we provide a novel classification of load balancing and server consolidation strategies based on various factors, including migration overhead, hardware constraints, network traffic, and system reliability. By exploring these factors, we aim to contribute a refined framework that enables more efficient load balancing and server consolidation in real-world applications. This work offers insights into how these algorithms can enhance server farm operations, ensuring better scalability, reduced energy consumption, and improved service quality.

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References

Champla, Dharavath, SivakumarDhandapani, and NagarajanVelmurugan. "DL‐DC: Deep learning‐based deadline constrained load balancing technique." Concurrency and Computation: Practice and Experience 35.26 (2023): e7839.

Botta, W. De Donato, V. Persico, and A. Pescapé, ‘‘Integration of Cloud computing and Internet of Things: A survey,’’ Future Gener. Comput. Syst., vol. 56, pp. 684–700,2016.

Zhang, P., & Zhou, M. (2020). "Energy Consumption Patterns in Cloud Data Centers." Cloud Systems Management, 23(5), 99-112.

Champla, Dharavath, and Dhandapani Siva Kumar. "Survey of Load Balancing Algorithms in Cloud Environment Using Advanced Proficiency." Innovative Data Communication Technologies and Application: ICIDCA 2019 (2020): 395-403.

Kumar, R., & Gupta, S. (2021). "Performance Metrics for Load Balancing in Cloud Environments." International Journal of Cloud Computing and Applications, 14(3), 55-70.

Champla, D., V. Ramkumar, and P. Ajay. "C-AVPSO: dynamic load balancing using African vulture particle swarm optimization." Int. J. Comput. Eng. Optim 1.01 (2023): 24-32.

Champla, Dharavath, and Yasaram Ganesh. "Providing Security to User Confidentiality Data in Large Scale Networks." (2015).

Smith, J., & Lee, A. (2021). "The Evolution of Distributed Computing Systems in the Cloud Era." Journal of Cloud Computing and Applications, 15(3), 134-146.

Williams, B., & Harris, T. (2020). "Global Cloud Migration: Trends and Challenges." International Journal of Cloud Technology, 12(4), 78-92.

Taylor, D., & Patel, V. (2019). "Cloud Computing and Its Security Challenges." Cloud Security Review, 18(2), 211-223.

Miller, E., & Yang, L. (2022). "Emerging Trends in Data Center Energy Consumption." Journal of Energy in IT Systems, 7(1), 29-41.

[12] Zhang, P., & Zhou, M. (2020). "Energy Consumption Patterns in Cloud Data Centers." Cloud Systems Management, 23(5), 99-112.

Kumar, R., & Gupta, S. (2021). "Addressing the Energy Crisis in Cloud Computing." Energy Efficiency in Data Centers, 4(1), 67-84.

Hsu, J., & Chang, K. (2018). "Load Balancing Techniques in Cloud Computing." International Journal of Cloud Computing, 9(2), 103-118.

Thomas, G., & Singh, R. (2020). "Server Consolidation and Energy Efficiency in Data Centers." Journal of Cloud Infrastructure, 11(3), 56-72.

Anderson, P., & Lee, S. (2021). "Optimizing Task Scheduling and Resource Assignment in Distributed Systems." Journal of Cloud Infrastructure Management, 14(4), 215-227.

Gupta, A., & Sharma, N. (2020). "Resource Management and Load Balancing in Distributed Systems." Computing Systems Journal, 19(2), 33-48.

Harris, M., & Blake, D. (2019). "The Carbon Footprint of Backup Servers in Data Centers." Sustainability in Technology, 22(1), 78-90.

Liu, F., & Wang, X. (2021). "Cloud Computing and the Internet of Things: A Symbiotic Relationship." Cloud Computing Journal, 18(5), 177-189.

Cheng, Y., & Kim, H. (2020). "The Intersection of Cloud Computing and Vehicular Networks." Journal of Cloud and VANETs, 8(3), 102-115.

Rodriguez, L., & Yang, X. (2022). "Cloud Computing in Healthcare: The Role of WBANs." E-Health Applications and Cloud Integration, 5(1), 45-60.

Ganesh, Yasaram, and DharavathChampla. "Finding the closest path to point out the neighbour with keyword."

H. Nashaat, N. Ashry, and R. Rizk, ‘‘Smart elastic scheduling algorithm for virtual machine migration in cloud computing,’’ J. Supercomput., vol. 75, pp. 3842–3865, Jul.2019.

F.F.Moghaddam,R.F.Moghaddam, and M. Cheriet,‘‘Carbon-awaredis-tributed cloud: Multi levelgroupinggeneticalgorithm,’’ClusterComput., vol. 18, no. 1, pp. 477– 491,2014.

C. B. Pop, I. Anghel, T. Cioara, I. Salomie, and I. Vartic, ‘‘A swarm- inspired data center consolidation methodology,’’ in Proc. 2nd Int. Conf. Web Intell., Mining Semantics (WIMS), 2012, Art. no.41.

W. Guo, P. Kuang, Y. Jiang, X. Xu, and W. Tian, ‘‘SAVE: Self-adaptive consolidation of virtual machines for energy efficiency of CPU-intensive applications in the cloud,’’ J. Supercomput., pp. 1–25, Jun. 2019. doi:10.1007/s11227-019-02927-1.

M.Hähnel,J.Martinovic,G.Scheithauer,A.Fischer,A.Schill,and Dargie, ‘‘Extending the cutting stock problem for consolidating ser- vices with stochastic workloads,’’ IEEE Trans. Parallel Distrib. Syst., vol. 29, no. 11, pp. 2478–2488, Nov.2018.

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Published

2025-07-03

How to Cite

1.
Champla D, Kumar DS, G S, Satyanarayana C. A Meta-Analysis Of Load Balancing and Server Consolidation In Distributed Computing Environments. J Neonatal Surg [Internet]. 2025Jul.3 [cited 2025Jul.19];14(32S):3347-55. Available from: https://jneonatalsurg.com/index.php/jns/article/view/7935

Issue

Vol. 14 No. 32S (2025): Journal of Neonatal Surgery

Section

Original Article

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