Optimizing Data Centre Energy Consumption Using Green Computing and Virtualization

Iriakuma T. Christopher; October K. E. Lambert

doi:10.56201/ijcsmt.vol.12.no7.2026.pg222.236

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Optimizing Data Centre Energy Consumption Using Green Computing and Virtualization

Iriakuma T. Christopher, October K. E. Lambert

Abstract

Global data center electricity consumption reached approximately 415 terawatt-hours in 2024 and is projected to double to 945 TWh by 2030, driven by exponential growth in cloud services, big data analytics, and artificial intelligence workloads making data centre energy optimization one of the most pressing technical and environmental challenges of the digital era. In sub-Saharan Africa and Nigeria specifically, institutional data centers compound this global problem through outdated infrastructure, poor server utilization rates, inadequate cooling design, and the near-total absence of structured energy management frameworks. This study investigates the joint impact of server virtualization and workload consolidation and AI-driven power management and renewable energy integration on data centre energy consumption reduction in Nigerian institutions. Grounded in the Energy Efficiency Theory and the Technology Acceptance Model , a quantitative positivist research design was adopted. Structured questionnaires were administered to 236 data centre managers, server administrators, network engineers, and IT infrastructure specialists across 12 institutional and commercial data centers in Rivers State, Nigeria, yielding 211 usable responses (response rate = 89.4%). Instruments achieved strong psychometric properties (Cronbach's α = 0.87–0.90; Content Validity Index = 0.92). Data were analyzed using IBM SPSS Version 27 through descriptive statistics, Pearson correlation, and multiple linear regression. Multiple regression results establish that server virtualization and workload consolidation (β = 0.461, t = 7.43, p < .001) and AI-driven power management and renewable energy integration (β = 0.348, t = 5.61, p < .001) jointly and significantly predict data centre energy consumption reduction (R2 = 0.557, Adjusted R2 = 0.553, F(2,208) = 130.62, p < .001), jointly explaining 55.7% of outcome variance. Both null hypotheses were rejected. The study recommends that the Nigerian Communications Commission mandate energy efficiency reporting for all institutional data centers above 100 kW IT load, that data centre operators implement structured virtualization-first infrastructure policies, and that future research measure actual kilowatt-hour savings and Power Usage Effectiveness improvements from virtualization adoption across Nigerian data centers through longitudinal monitoring studies. IJCSMT

Keywords

data centre energy optimizationgreen computingserver virtualizationworkload consolidationAI-driven power managementrenewable energy

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Optimizing Data Centre Energy Consumption Using Green Computing and Virtualization

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