Citation

Sungheetha, Akey and R., Rajesh Sharma (2026) Adaptive Parametric Framework for Sustainable Digital Innovation Optimization in Cloud-Edge Computing Environments. Procedia Computer Science, 283. pp. 1700-1706. ISSN 18770509

Text 4.pdf - Published Version Restricted to Repository staff only Download (343kB)

Abstract

The digital transformation era demands sustainable innovation frameworks that optimize resource utilization while maintaining performance efficiency. This research presents an Adaptive Parametric Framework (APF) for sustainable digital innovation optimization in cloud-edge computing environments. The proposed framework integrates parametric variables including Energy Efficiency Index (ηEE), Sustainability Coefficient (γS ), and Innovation Adaptability Factor (αIA) to address key challenges in digital sustainability. Through comprehensive analysis of 150 parametric configurations across heterogeneous computing environments, the framework demonstrates 34.7% improvement in energy efficiency, 28.3% reduction in carbon footprint, and 42.1% enhancement in innovation adaptability metrics. The methodology incorporates machine learning-driven optimization algorithms with real-time parametric adjustment capabilities, enabling dynamic sustainability optimization. Experimental validation using opensource datasets confirms the framework’s effectiveness in achieving sustainable digital innovation objectives while maintaining computational performance standards.

Item Type:	Article
Uncontrolled Keywords:	Cloud-Edge Computing, Energy Efficiency
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Sep 2026 07:08
Last Modified:	02 Sep 2026 07:08
URII:	http://shdl.mmu.edu.my/id/eprint/16536

Downloads

Edit (login required)