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Multi-Cloud Resource Management Techniques for Cyber-Physical Systems
Vlad Bucur1, Liviu-Cristian Miclea2
1MassMutual Romania, Record Park, Strada Onisifor Ghibu 20 A, 400185 Cluj-Napoca, Romania.
Sensors (Basel, Switzerland)
|December 28, 2021
Summary
This study presents a multi-cloud resource management API to handle increasing demands for complex AI systems like autonomous vehicles. The solution enhances scalability and resilience while maintaining cloud agnosticism.
Area of Science:
- Computer Science
- Artificial Intelligence
- Cloud Computing
Background:
- Modern software, including AI for autonomous vehicles, requires substantial data and computing resources.
- Data transformation and AI model training necessitate scalable and resilient infrastructure.
Purpose of the Study:
- To introduce a novel API for managing resources in a multi-cloud environment.
- To address the escalating computational demands of data-intensive applications.
- To enhance system scalability and resilience through cloud-agnostic resource orchestration.
Main Methods:
- Architectural design of a multi-cloud resource management API.
- Detailed low-level implementation strategies.
- Experimental validation of the proposed system's feasibility and applicability.
Main Results:
- A comprehensive architectural breakdown of the resource management API.
- A low-level implementation description.
- Experimental data demonstrating feasibility and applicability.
Conclusions:
- The developed API effectively manages resources in a multi-cloud setting.
- The system offers a scalable, resilient, and cloud-agnostic solution for AI and data-intensive applications.
- The experimental results validate the proposed approach for managing complex cyber-physical systems.
Keywords:
AIAPI developmentautonomous vehiclescloud computingcloud storage performancecyber-physical systemsmachine learningmulti-cloudneural networksresource managementself-driving carsMore Related Videos
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