Abstract
In the era of Industry 4.0, commoditized services (such as electricity, healthcare, etc.), require accuracy, availability, security and Quality of service (QoS) in the Cloud space. This paper presents cloud advanced metering infrastructure (CAMI) using Zynq UltraScale+ device field programmable gate array (FPGA). The architectural layout for energy tracking and profile measurement is discussed. Unlike existing systems with digital signal processors, it uses precision-based meter reading with encryption driven demand side management (DSM) to protect end-users. An energy service application with supporting hardware prototype is designed. Cryptographic algorithms, dynamic auto-scaling and predictive QoS provisioning are introduced as features of its backend cloud virtualization Infrastructure controller (CVIC). Process integration is achieved with CVIC synthesis for energy analytics and DSM. For the use case scenario, the CAMI prototype runs on Zynq UltraScale+ device with support for end-to-end dataset captures. The system provides on-demand visualization of energy consumption patterns for the end-users. In the experimental setup, two case scenarios demonstrate how the metering system executes fast edge computing profiling. Optimal performance is achieved for latency, utilization, and throughput under CVIC overhead constraint. It was observed that resource utilization responses for heterogeneous and non heterogeneous CVIC are 71.43% and 28.57% respectively. Latency profiles gave 71.43% and 28.57% respectively. With the VM controller, FPGA CAMI offered 47.36% while without VM controller, 52.63% throughput is observed. Consequently, the results highlights how FPGA hardware acceleration can significantly improve request distribution as well as workload processing for cloud based metering systems.
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Okafor, K.C., Obayi, A.A. (2020). Industry 4.0 CAMI: An Elastic Cloud Zynq UltraScale FPGA Metering Architecture. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12254. Springer, Cham. https://doi.org/10.1007/978-3-030-58817-5_39
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DOI: https://doi.org/10.1007/978-3-030-58817-5_39
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