multi cloud
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2022 ◽  
K. Kalyana Chakravarthi ◽  
P. Neelakantan ◽  
L. Shyamala ◽  
V. Vaidehi

2022 ◽  
pp. 108439
Lav Gupta ◽  
Tara Salman ◽  
Ali Ghubaish ◽  
Devrim Unal ◽  
Abdulla Khalid Al-Ali ◽  

Rahul Mishra ◽  
Dharavath Ramesh ◽  
Damodar Reddy Edla ◽  
Lianyong Qi

Olha Kozina ◽  
Volodymyr Panchenko ◽  
Oleksandr Rysovanyi

Multi-cloud middleware must perform many different resource management, control, and monitoring functions that must interoperate but may differ in implementation in each cloud service provider. A mechanism for monotonic recording model implementation for multi-cloud systems with a geo-distributed middleware architecture is proposed in the article. It is shown, the middleware modules location defines the algorithm of synchronization of start moments of adjusting intervals required to generating the global sequence numbers for customer's data recording into the databases of multi-cloud systems. Figs.: 2. Tabl.: 1. Refs.: 10 titles. Keywords: middleware architecture, geo-distributed middleware architecture, multicloud systems.

Digitale Welt ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 58-59
Bernd Mährlein

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8364
Vlad Bucur ◽  
Liviu-Cristian Miclea

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.

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