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Impact Factor : 8.14

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Paper Title: ML -Based Multi Objective Task Scheduling in Cloud Computing using Genetic Algorithms with Integration Of IOT
Authors Name: Chilukuri Lakshmareddy , Motamarri Nikhitha , D.Bhanu Yaswanth , P.Praneeth , S.Bhargavi
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IJRTI_211439
Published Paper Id: IJRTI2604221
Published In: Volume 11 Issue 4, April-2026
DOI:
Abstract: Cloud computing has become a vital part of modern technology, enabling users to access computing resources such as processing power, storage, and networking through the internet. However, as the number of users and tasks continues to grow, efficiently managing and scheduling these tasks has become a significant challenge. Traditional scheduling techniques like Round Robin, First-Come-First-Served (FCFS), and Random Assignment are simple to implement but are not suitable for handling dynamic workloads and diverse resource requirements. As a result, they often lead to inefficient resource utilization, increased execution time, and higher operational costs. To address these challenges, this project introduces an intelligent scheduling system called ML-Based Multi-Objective Task Scheduling in Cloud Computing using Genetic Algorithms. The system is designed as a web-based application that automates the entire scheduling process. It allows users to upload task data in multiple formats such as CSV, JSON, Excel, DOCX, TXT, and PDF, which are then converted into a unified format for further processing. A Machine Learning model is used to predict task execution time based on parameters like CPU requirements, memory usage, task size, priority, and instance specifications. To enhance scheduling performance, a Genetic Algorithm is applied to determine the optimal allocation of tasks to available cloud resources. The algorithm generates multiple possible solutions and progressively improves them using operations such as selection, crossover, and mutation. A fitness function is used to evaluate each solution by considering factors like execution time, cost, and workload balance, ensuring the selection of the most efficient scheduling strategy. The system is integrated with AWS cloud services, where tasks are dispatched to EC2 instances via SQS for execution. It also provides visual outputs such as convergence graphs, Gantt charts, and cost analysis, helping users clearly understand system performance and efficiency
Keywords: Cloud Computing, Task Scheduling, Machine Learning, Genetic Algorithm, Multi- Objective Optimization, Execution Time Prediction, Resource Allocation, AWS EC2, Amazon SQS, Load Balancing, Makespan Minimization, Cost Optimization, Random Forest, Gradient Boosting, Distributed Computing
Cite Article: "ML -Based Multi Objective Task Scheduling in Cloud Computing using Genetic Algorithms with Integration Of IOT", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b620-b631, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604221.pdf
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2604221
Registration ID:211439
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: b620-b631
Country: NTR, Andhra Pradesh, India
Research Area: Computer Science & Technology 
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604221
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604221
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

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