Multi-Cloud Resource Management Techniques for Cyber-Physical Systems
Information technology is based on data management between various sources. Software projects, as varied as simple applications or as complex as self-driving cars, are heavily reliant on the amounts, and types, of data ingested by one or more interconnected systems. Data is not only consumed but is...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/24/8364 |
| _version_ | 1797500771147710464 |
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| author | Vlad Bucur Liviu-Cristian Miclea |
| author_facet | Vlad Bucur Liviu-Cristian Miclea |
| author_sort | Vlad Bucur |
| collection | DOAJ |
| description | Information technology is based on data management between various sources. Software projects, as varied as simple applications or as complex as self-driving cars, are heavily reliant on the amounts, and types, of data ingested by one or more interconnected systems. Data is not only consumed but is transformed or mutated which requires copious amounts of computing resources. One of the most exciting areas of cyber-physical systems, autonomous vehicles, makes heavy use of deep learning and AI to mimic the highly complex actions of a human driver. Attempting to map human behavior (a large and abstract concept) requires large amounts of data, used by AIs to increase their knowledge and better attempt to solve complex problems. This paper outlines a full-fledged solution for managing resources in a multi-cloud environment. The purpose of this API is to accommodate ever-increasing resource requirements by leveraging the multi-cloud and using commercially available tools to scale resources and make systems more resilient while remaining as cloud agnostic as possible. To that effect, the work herein will consist of an architectural breakdown of the resource management API, a low-level description of the implementation and an experiment aimed at proving the feasibility, and applicability of the systems described. |
| first_indexed | 2024-03-10T03:08:37Z |
| format | Article |
| id | doaj.art-5c4b1284e8d74629b06dcff800eaa8d8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:08:37Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5c4b1284e8d74629b06dcff800eaa8d82023-11-23T10:30:25ZengMDPI AGSensors1424-82202021-12-012124836410.3390/s21248364Multi-Cloud Resource Management Techniques for Cyber-Physical SystemsVlad Bucur0Liviu-Cristian Miclea1MassMutual Romania, Record Park, Strada Onisifor Ghibu 20 A, 400185 Cluj-Napoca, RomaniaDepartment of Automation, Faculty of Automation and Computer Science, Technical University Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj-Napoca, RomaniaInformation technology is based on data management between various sources. Software projects, as varied as simple applications or as complex as self-driving cars, are heavily reliant on the amounts, and types, of data ingested by one or more interconnected systems. Data is not only consumed but is transformed or mutated which requires copious amounts of computing resources. One of the most exciting areas of cyber-physical systems, autonomous vehicles, makes heavy use of deep learning and AI to mimic the highly complex actions of a human driver. Attempting to map human behavior (a large and abstract concept) requires large amounts of data, used by AIs to increase their knowledge and better attempt to solve complex problems. This paper outlines a full-fledged solution for managing resources in a multi-cloud environment. The purpose of this API is to accommodate ever-increasing resource requirements by leveraging the multi-cloud and using commercially available tools to scale resources and make systems more resilient while remaining as cloud agnostic as possible. To that effect, the work herein will consist of an architectural breakdown of the resource management API, a low-level description of the implementation and an experiment aimed at proving the feasibility, and applicability of the systems described.https://www.mdpi.com/1424-8220/21/24/8364cloud computingAImachine learningneural networksmulti-cloudcloud storage performance |
| spellingShingle | Vlad Bucur Liviu-Cristian Miclea Multi-Cloud Resource Management Techniques for Cyber-Physical Systems Sensors cloud computing AI machine learning neural networks multi-cloud cloud storage performance |
| title | Multi-Cloud Resource Management Techniques for Cyber-Physical Systems |
| title_full | Multi-Cloud Resource Management Techniques for Cyber-Physical Systems |
| title_fullStr | Multi-Cloud Resource Management Techniques for Cyber-Physical Systems |
| title_full_unstemmed | Multi-Cloud Resource Management Techniques for Cyber-Physical Systems |
| title_short | Multi-Cloud Resource Management Techniques for Cyber-Physical Systems |
| title_sort | multi cloud resource management techniques for cyber physical systems |
| topic | cloud computing AI machine learning neural networks multi-cloud cloud storage performance |
| url | https://www.mdpi.com/1424-8220/21/24/8364 |
| work_keys_str_mv | AT vladbucur multicloudresourcemanagementtechniquesforcyberphysicalsystems AT liviucristianmiclea multicloudresourcemanagementtechniquesforcyberphysicalsystems |