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A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference
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Design and Evaluation of a Cloud Computing System for Real-Time Measurements in Polarization-Independent Long-Range
Abdusomad Nur1,2, Almaz Demise2, Yonas Muanenda2
1Addis Ababa Institute of Technology, Addis Ababa University, King George VI St, Addis Ababa 1000, Ethiopia.
Sensors (Basel, Switzerland)
|January 8, 2025
Summary
This study evaluates cloud computing for Distributed Acoustic Sensing (DAS), finding that Virtual Machine (VM) selection impacts processing times. Cloud-based DAS is scalable for real-time data management.
Area of Science:
- Geophysics
- Computer Science
- Data Science
Background:
- Distributed Acoustic Sensing (DAS) enables vibration measurement over extended regions.
- Real-time data handling and processing for DAS applications present significant storage and computational challenges.
- Cloud computing offers a potential solution for managing large-scale DAS datasets.
Purpose of the Study:
- To design and evaluate a cloud computing scheme for long-range, polarization-independent DAS.
- To analyze the impact of Virtual Machine (VM) capacities and data block sizes on processing times.
- To assess the scalability and efficiency of cloud-based DAS data management.
Main Methods:
- Utilized the CloudSim simulation framework to model cloud infrastructure.
- Simulated a cloud computing scheme for DAS using coherent detection of Rayleigh backscattering signals.
- Investigated processing times across various VM configurations and data block sizes.
Main Results:
- Virtual Machine (VM) selection critically influences computational times for real-time DAS measurements.
- Achieving polarization independence in DAS introduces minimal processing overhead.
- Increasing data block size per cycle leads to diminishing increments in processing time, showing scalability.
Conclusions:
- Cloud computing schemes are scalable for long-range DAS, efficiently managing large datasets.
- The choice of VM resources significantly impacts the performance of real-time DAS processing.
- Cloud-based solutions offer a viable approach to address DAS data handling challenges.
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