An Intelligent and Sustainable Storage Scheduling Framework Based on Environmental Impact Metrics in Data Networks

The rapid expansion of digital data volumes and
the proliferation of distributed network infrastructures have intensified energy demand and greenhouse gas emissions within storage systems. These trends highlight the need for storage scheduling mechanisms that integrate sustainability objectives alongside performance requirements. This paper presents an intelligent and sustainable storage scheduling framework that incorporates environmental impact metrics into decisionmaking for decentralised data networks. In contrast to conventional cloud storage schedulers that focus on cost and service performance, the proposed framework evaluates storage nodes using a composite set of indicators, including energy consumption, end-to-end delay, renewable energy availability, and the carbon intensity of electricity generation. The framework adopts a decentralised architecture supported by blockchain technology, where smart contracts provide transparency, trust, and autonomous enforcement of scheduling policies. A multi-objective optimisation model coordinates
storage placement and access decisions to balance latency,
operational cost, and carbon emissions across the network.
Environmental impact awareness is embedded directly into the
scheduling process, enabling carbon-conscious data placement
without sacrificing system responsiveness or reliability.
Extensive simulation experiments demonstrate that the
proposed approach achieves notable reductions in carbon
emissions, with observed savings reaching 32% when compared
with traditional storage scheduling strategies. These reductions
occur while maintaining comparable levels of service quality,
network delay, and storage availability. The results indicate that
environmental metrics can function as first-class parameters
within storage management systems. This research contributes
a scalable green computing framework suitable for nextgeneration decentralised storage networks.