International Journal of Leading Research Publication

E-ISSN: 2582-8010   •   Impact Factor: 9.56

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 4 April 2025 Submit your research before last 3 days of to publish your research paper in the issue of April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 4
April 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Reinforcement Learning-Based Data Pipeline Optimization for Cloud Workloads

Author(s) Raghavender Maddali
Country India
Abstract The Use of machine learning (ML) and reinforcement learning (RL) techniques towards optimizing cloud workload, resource allocation, and data processing efficiency. Recent developments in ML-based models such as hybrid workload predictive models, data storage technology using deep learning techniques, and secure offloading architectures have brought excellent developments in cloud and fog computing environments. Reinforcement learning has also worked exceptionally well in network scheduling optimization, query engine optimization, and parallelism for big data. ML-based solutions have been used to provide solutions for low-latency problems in memory management, load balance, and multistep multi-workflow scheduling in data centers. This paper emphasizes the application of ML and RL for optimizing cloud workload to help achieve adaptive scalability, cost, and better usage of resources and some of them as intelligent systems to build workload forecasts, pipeline optimization for data, and fair resource allocation to have more scalable and effective cloud computing configuration.
Keywords Cloud Workload Prediction, Reinforcement Learning, Resource Provisioning, Big Data Processing, Machine Learning, Workload Optimization, Data Center Networks, Parallelization, Load Balancing, Query Engine Optimization, Fog-Cloud Computing, Adaptive Scalability, Low Latency, Cost Efficiency.
Field Computer > Data / Information
Published In Volume 1, Issue 1, September 2020
Published On 2020-09-03
Cite This Reinforcement Learning-Based Data Pipeline Optimization for Cloud Workloads - Raghavender Maddali - IJLRP Volume 1, Issue 1, September 2020. DOI 10.5281/zenodo.15107752
DOI https://doi.org/10.5281/zenodo.15107752
Short DOI https://doi.org/g8988d
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJLRP DOI prefix is
10.70528/IJLRP

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 5 Isu 12 Cover Page Vol 5 Isu 11 Cover Page Vol 5 Isu 10

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJLRP
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijlrp.com