Optimization enabled deep learning method in container-based architecture of hybrid cloud for portability and interoperability-based application migration

Virtualisation is a major part of the cloud as it permits the deployment of several virtual servers over the same physical layer. Due to the adaption of cloud services, the count of the application running on repositories increases, resulting in overload. However, the application migration in the cl...

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Bibliographic Details
Published in:Journal of experimental & theoretical artificial intelligence 2024-10, Vol.36 (7), p.985-1002
Main Authors: Hiremath, Tej. C., K. S., Rekha
Format: Article
Language:English
Subjects:
Algorithms
Application migration
cloud
Cloud computing
Containers
deep long short-term memory
Interoperability
Optimization
Portability
Predictions
Resource allocation
resource capacity
Virtual environments
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Summary:Virtualisation is a major part of the cloud as it permits the deployment of several virtual servers over the same physical layer. Due to the adaption of cloud services, the count of the application running on repositories increases, resulting in overload. However, the application migration in the cloud with optimal resource allocation is still a challenging task. The application migration is employed to reduce the dilemma of resource allocation. Hence, this paper proposes a technique for portability and interoperability-based application migration in the cloud platform. The cloud simulation is done with the Physical Machine (PM), Virtual Machine (VM), and container. The interoperable application migration is provided using the newly devised Lion-based shuffled shepherd (Lion-SS) optimisation algorithm. The Lion-SS algorithm combines the shuffled shepherd optimisation algorithm (SSOA) and the Lion optimisation algorithm (LOA). The new objective function is devised based on predicted load, demand, transmission cost, and resource capacity. Besides, the prediction of the load is performed using Deep long short-term memory (Deep LSTM). The proposed technique obtained the minimal load of 0.007 and resource capacity of 0.342.
ISSN:0952-813X
1362-3079
DOI:10.1080/0952813X.2022.2117421