Authors:
Jeyakumar Ramachandran

Addresses:
Department of Computer Science and Engineering, HCL Tech, Atlanta, Georgia, United States of America.

Abstract:

This research presents a new concept for a Predictive Cloud Engineering Architecture that addresses the increasing energy needs and diminishing resources in current distributed digital environments. The study proposes optimising the system's computational efficiency while minimising its ecological footprint by combining proactive forecasting models with adaptive scaling strategies. The overall aim is to strike a balance between the system's computational performance and its environmental impact by incorporating proactive forecasting models and dynamic scaling protocols. A specialised predictive engine is used to forecast workload changes, helping minimise idle resources and optimise server utilisation. The data set used in the study is carefully curated, including performance metrics from high-density data centres such as energy consumption, CPU usage, and network latency. The proposed architecture was validated using tools such as specialised simulation environments and resource-monitoring frameworks. Experimental results show that carbon footprints are reduced while maintaining quality of service and processing speeds. The research provides a scalable framework for future green computing initiatives and shows that intelligent coordination can balance the increasing demand for cloud services with global sustainability goals.

Keywords: Cloud Engineering; Sustainable Computing; Resource Coordination; Predictive Analytics; Distributed Systems; Digital Transformation; Dynamic Scaling Protocols; Sustainability Goals.

Received on: 01/04/2025, Revised on: 08/06/2025, Accepted on: 25/08/2025, Published on: 05/06/2026

DOI: 10.69888/FTSESS.2026.000715

FMDB Transactions on Sustainable Environmental Sciences, 2026 Vol. 3 No. 2, Pages: 71-81

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