ARCHIVES
Adaptive Allocation of AI/ML Tasks in Cloud–Edge Computing
¹ ² ³ ⁴ ⁵ Department of Computer science and Engineering, KL University, Andhra Pradesh, India.
Published Online: March-April 2026
Pages: 177-185
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702023
Abstract
The integration of artificial intelligence (AI) and machine learning (ML) into cloud computing has led to the emergence of distributed AI/ML applications operating across the cloud–edge continuum. However, privacy concerns, regulatory compliance, and ethical constraints present challenges in determining optimal workload distribution. This paper explores a strategic allocation framework that dynamically assigns AI/ML workloads between cloud and edge resources based on data sensitivity, computational efficiency, and policy requirements. By leveraging intelligent orchestration mechanisms, we propose an adaptive approach that enhances performance, ensures compliance with regulatory frameworks, and upholds ethical AI principles. Our findings contribute to the development of secure, efficient, and responsible AI/ML deployment in cloud-edge environments.
Related Articles
2026
AI-Based Stomach Cancer Detection Using Biomarkers, Medical Images, and Voice Analysis
2026
Hydrogen-Efficient Eco-Driving and Route Planning for Fuel-Cell Electric Vehicles Using Multi-Objective Optimization Under Traffic and Terrain Uncertainty
2026
A Data-Driven Machine Learning Framework for Assessing Patent Commercial Value and Technological Significance
2026
Evaluating Student Academic Performance Through a Benchmark of Fuzzy Reasoning Models
2026
A Hybrid Soft Computing Approach for Managing Uncertainty in Data Analytics
2026
Soft Computing Approaches for Robust Analysis of Imbalanced and Noisy Data
2026
Mock Interviewer
2026
Smart Attendance System Using Face Recognition and Gaze-Based Attention Monitoring
2026
Analyzing Customer Review Sentiments using Machine Learning
2026
Agentic Artificial Intelligence as a Strategic HR Partner: Redefining Decision-Making Authority and Strategic Roles
