Construction and Research on Cloud-edge Collaborative Power Measurement and Security Model

Jiajia Huang; Ying Sun; Xiao Jiang; Youpeng Huang; DongXu Zhou

doi:10.4108/ew.5522

Authors

Jiajia Huang Guangdong Power Grid Co., Ltd. ShaoGuan Power Supply Bureau. No. 66 Gongye West Road, Wujiang District, Shaoguan City, Guangdong Province, 512000, China
Ying Sun Guangdong Power Grid Corporation No. 757 Dongfeng East Road, Yuexiu District, Guangzhou City, Guangdong Province, 510600，China
Xiao Jiang Metrology Center of Guangdong Power Grid Corporation, Yuedian Building No. 8 Shuijungang, Dongfeng East Road, Yuexiu District, Guangzhou City, 510000, China
Youpeng Huang Metrology Center of Guangdong Power Grid Corporation, Yuedian Building No. 8 Shuijungang, Dongfeng East Road, Yuexiu District, Guangzhou City, 510000, China
DongXu Zhou Metrology Center of Guangdong Power Grid Corporation, Yuedian Building No. 8 Shuijungang, Dongfeng East Road, Yuexiu District, Guangzhou City, 510000, China

DOI:

https://doi.org/10.4108/ew.5522

Keywords:

Cloud-Edge Computing, Power Measurement, Security Model, Collaborative Systems, Smart Grids

Abstract

Accurate power consumption assessment is of critical importance in the fast-evolving world of cloud and edge computing. These technologies enable rapid data processing and storage but they also require huge amounts of energy. This energy requirement directly impacts operational costs, as well as environmental responsibility. We are conducting research to develop a specialized cloud-edge power measurement and security model. This model delivers reliable power usage data from these systems while maintaining security for the data they process and store. A combination of simulation-based analysis and real-world experimentation helped us to deliver these results. Monte Carlo based simulations produced power usage predictions under various conditions and Load Testing validated their real-world performance. A Threat Modeling-based security study identified potential vulnerabilities and suggested protection protocols. A collaborative approach enhances power measurements accuracy and encourages secure operation of the combined cloud-edge systems. By fusing these metrics, a more efficient and secure operation of computing resources becomes possible. This research underscores the critical importance of developing advanced techniques for power metering and security in cloud-edge computing systems. Future research may focus on both expanding the model’s use to an array of larger, more complex networks, as well as the inclusion of AI driven predictive analytics to amplify accuracy of power management.

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