Achieving resilient cities using data-driven energy transition: A statistical examination of energy policy effectiveness and community engagement

Abstract

In an era where energy usage is increasingly critical due to the growing prevalence of cloud computing data centers, this study delves into the realm of dynamic energy management (DEM) within cloud data centers (CDCs) to foster efficient power utilization. Our research introduces a Dynamic System Management (DSM) framework tailored for CDCs, encompassing a dynamic voltage scaling (DVS) management unit, a load balancing unit, and a task scheduling unit (TSU). The TSU employs a stochastic Petri net-based planning procedure and advances a 'role-based' resource placement approach, optimizing task scheduling for enhanced system performance and energy efficiency. Through comprehensive simulation tests, we validate the proposed system scheme. Furthermore, this paper extends its focus to augment the resilience of energy policies in the context of smart cities and foster community engagement. Building upon the existing Biogeography-Based Optimization (BBO), we propose enhancements to fortify its robustness and capabilities. Notably, our modified BBO approach demonstrates remarkable reductions in power consumption—27%, 32%, and 39% less when compared to BBO, particle swarm optimization, and genetic algorithms, respectively. These findings underscore the pivotal role of data-driven energy transition and community engagement in building resilient cities for the future.