CLOUD SUSTAINABILITY AND GREEN COMPUTING

Author

Mahima Pandey , Mr. Nilesh Khare, Komal Yadav


Abstract

 Cloud infrastructure provides elastic and shared computational capacity, yet workload placement can increase electricity demand, operational carbon emissions and service-level risks when resource decisions overlook regional energy characteristics. This paper presents Sustainability-Aware Scheduling and Consolidation (SASC), an interpretable multi-objective framework on cloud sustainability and green computing. SASC first rejects allocations that violate capacity, security or quality-of-service constraints, and then ranks feasible placements by normalized incremental IT energy, carbon cost, expected SLA risk, movement overhead and desirable host utilization. Geographic carbon-intensity differences and facility-efficiency factors are included in the emissions accounting boundary. A power-based energy model, an operational-carbon estimator, utilization safeguards and migration hysteresis support a reproducible simulation design. The proposed evaluation compares SASC with Round Robin, consolidation-oriented Best-Fit and carbon-oblivious energy-aware scheduling using heterogeneous hosts, variable workloads and temporally varying carbon-intensity profiles. Paired workload replays, multiple random seeds, ablation experiments, uncertainty intervals and QoS non-inferiority checks are specified to separate potential environmental gains from performance displacement. Analytical conditions show why the minimum-energy destination need not be the minimum-carbon destination and identify circumstances in which geographical shifting is counterproductive. The contribution is a testable, auditable decision architecture and a complete evaluation protocol rather than an assertion of unmeasured performance improvement. The framework is also translated into procurement and operational indicators for institutions seeking evidence-based sustainable cloud adoption.



Keywords

Cloud sustainability; green computing; carbon-aware scheduling; cloud simulation.


DOI : https://doi.org/10.5281/zenodo.23587863


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References


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