Crossref DOIMemberCiteFactoropen accessissn
SJIF Impact Factor 8.659 | ISSN: 2582-3930
IJSREM Logo

International Journal of Scientific Research in Engineering and Management

(Gateway to Global Scholars - An International, Scholarly, Multi-Disciplinary, Open Access Journal)

Peer-Reviewed Journal Designed to Meet UGC's Quality Parameters

HomeCurrent IssuePast IssuesPay OnlineConferencesEditorial BoardAbout UsContact Us
IJSREM

Loading article...

Follow Us

  • Google Scholar
  • ResearchGate
  • Academia.edu
  • Mendeley
  • LinkedIn
  • Facebook
  • Instagram

Journal Policies

  • Peer Review Policy
  • Publication Ethics
  • Copyright Infringement
  • Refund & Cancellation Policy
  • Terms and Conditions
  • Privacy Policy

Journal Identifiers & Licenses

  • ISSN Verified: 2582-3930ISSN Verified: 2582-3930
  • Crossref DOI: 10.55041Crossref DOI: 10.55041
  • SJIF Ranking: 08.659SJIF Ranking: 08.659
  • Licensed under CC BY 4.0Licensed under CC BY 4.0
  • ISO Certification: 9001:2015ISO Certification: 9001:2015
  • Indexed & Cited In: Google ScholarIndexed & Cited In: Google Scholar

Contact Us

International Journal of Scientific Research in Engineering and Management (IJSREM)

#62/1, New No 7, 1st Cross, 2nd Main, Ganganagar, R T Nagar, Bangalore North, Bangalore, Karnataka, India – 560032
[email protected]+91 93911 67991
logos
logos
Working Hours

Mon – Sat: 9:00 AM – 6:00 PM

Sunday: 10:00 AM – 4:00 PM

+91 93911 67991

© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

DisclaimerPrivacy PolicyTerms and Conditions
ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63950

Decentralized Load Balancing and Adaptive Scaling using Peer-to-Peer Topologies and Predictive Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63950

Volume:

10 |

5 |

May 2026

Pages:

1-8

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM63950

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Share this article:

Authors

Maaz Malik

Maaz Malik

Department of Computer Science and Engineering

Bharatiya Vidya Bhavan's Sardar Patel Institute of Technology (SPIT)

Download Author Certificate
Nikhil Chaudhari

Nikhil Chaudhari

Department of Computer Science and Engineering

Bharatiya Vidya Bhavan's Sardar Patel Institute of Technology (SPIT)

Download Co-Author Certificate
Ruchir Khare

Ruchir Khare

Department of Computer Science and Engineering

Bharatiya Vidya Bhavan's Sardar Patel Institute of Technology (SPIT)

Download Co-Author Certificate
Ninad Maadhavi

Ninad Maadhavi

Department of Computer Science and Engineering

Bharatiya Vidya Bhavan's Sardar Patel Institute of Technology (SPIT)

Download Co-Author Certificate

Author Services & Downloads

Full ArticlesAcceptance Certificate

Abstract

Horizontal scaling forms the backbone of modern cloud infrastructure, but traditional load balancing systems relying on a single master proxy introduce a critical single point of failure and performance bottleneck. This paper proposes a novel framework for Peer-to-Peer (P2P) Load Balancing coupled with Machine Learning (ML) based Adaptive Scaling to enhance service resilience and efficiency. Our P2P approach replaces the centralized proxy by distributing request routing responsibility among service instances (peers), eliminating the single point of failure. A key challenge in P2P systems is managing the excessive overhead of full connectivity; thus, we investigate how limiting peer-to-peer connection valencies affects network topology, specifically noting the tendency for low valencies to form inefficient, chain-like structures. Additionally, we integrate an ML-based CPU utilization forecasting model to predict load spikes and trigger unit spawning preemptively, mitigating crash risks associated with reactive scaling. We analyze various P2P configurations based on valency limits and their impact on load distribution, networking overhead, and latency, demonstrating a highly resilient and performance-optimized solution for high- concurrency environments.

KEYWORDS

Index Terms—peer-to-peer load balancing horizontal scaling adaptive scaling machine learning decentralized system net- work topology single point of failure

Cite This Article

Maaz Malik, Nikhil Chaudhari, Ruchir Khare, Ninad Maadhavi, "Decentralized Load Balancing and Adaptive Scaling using Peer-to-Peer Topologies and Predictive Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-8, May 2026. https://doi.org/10.55041/IJSREM63950

Full Article/Conclusion

This paper presented a novel decentralized architecture for horizontal scaling...

References

[1]     G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955. [2]     J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73. [3]     W. Zhang, ”Linux Virtual Server for Scalable Network Services,” in Proc. Ottawa Linux Symp., 2000, pp. 212–221. [4]     I. Stoica, R. Morris, D. Karger, M. Kaashoek, and H. Balakrishnan, ”Chord: A scalable peer-to-peer lookup service for internet applications,” IEEE/ACM Trans. Netw., vol. 11, no. 1, pp. 17-32, Feb. 2003. [5]     A. Rowstron and P. Druschel, ”Pastry: Scalable, decentralized object location and routing for large-scale peer-to-peer systems,” in Proc. IFIP/ACM Int. Conf. on Distributed Systems Platforms, 2001, pp. 329–350. [6]     P. S. Senthil Kumar, C. S. Shajitha, and V. R. Sarma, ”Gossip-based load balancing strategy for virtual machines in cloud computing,” in Proc. IEEE Int. Conf. on Computational Intelligence and Computing Research, 2013, pp. 1–6. [7]     D. J. Watts and S. H. Strogatz, ”Collective dynamics of ’small-world’ networks,” Nature, vol. 393, no. 6684, pp. 440–442, Jun. 1998. [8]     J. Wang, M. H. Li, B. B. Chen, and Y. Wang, ”Cloud resource prediction based on LSTM-RNN,” in Proc. IEEE Int. Conf. on Cloud Computing and Big Data Analytics, 2017, pp. 248–253. [9]     P. Maymounkov and D. Mazieres, ”Kademlia: A Peer-to-Peer Informa- tion System Based on the XOR Metric,” in Proc. IPTPS ’02, Springer, 2002, pp. 53-65. [10]    S. Ratnasamy, P. Francis, M. Handley, R. Karp, and S. Shenker, ”A Scalable Content-Addressable Network,” in Proc. ACM SIGCOMM, 2001, pp. 161–172.

Editorial & Review Transparency

Publication Date

5/25/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

Documents

Review Report (Confidential)
Copyright Form (Office)

Article Metrics & Impact

Article Views

1

Total views since publication

PDF Downloads

4

Total downloads since publication

Geographic Reach

5

Indexing, Archiving & Preservation

Google Scholar
Academia
ResearchGate
Crossref
SSRN
Semantic Scholar
Thomson Reuters
WorldCat
Mendeley
OpenAlex
EBSCO
Zenodo

Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.

Ethics, Compliance & Transparency

Plagiarism Screening

This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.

Conflict of Interest

The authors declare no conflict of interest related to this research work.

Funding Information

No funding information available for this research.

Similar & Recommended Articles

Similar Articles by Topic

Related research in the same subject area

Loading similar articles...