Dynamic cost effective solution for efficient cloud infrastructure

Mukhopadhyay, Nirmalya; Tewari, Babul P.

doi:10.1007/s11227-022-04913-6

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2024

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Cited by 3 publications

References 24 publications

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Cost and energy aware migration through dependency analysis of VM components in virtual cloud infrastructure

Mukhopadhyay,

Tewari

2024

Computing

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Cost and energy aware migration through dependency analysis of VM components in virtual cloud infrastructure

Mukhopadhyay,

Tewari

2024

Computing

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Performance Analysis of a Virtualized Cloud Data Center through Efficient VM Migrations

Mukhopadhyay,

Tewari

2024

Proceedings of the 2024 Sixteenth International Conference on Contemporary Computing

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Dynamic pricing on maximum concurrency for heterogeneous instances using hyperparameter optimization in dueling deep reinforcement learning in a multi-cloud scenario

Arivanandhan,

Ramanathan,

Chellamuthu

2024

IFS

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Users possess the option to rent instances of various sorts, in a variety of regions, and a variety of availability zones, thanks to cloud service carriers like AWS, GCP, and Azure. In the cloud business right now, fixed price models are king when it comes to pricing. However, as the diversity of cloud providers and users grows, this approach is unable to accurately reflect the market’s current needs for cost savings. As a consequence, a dynamic pricing strategy has become a desirable tactic to better handle the erratic cloud demand. In this study, a deep learning model was used to propose a dynamic pricing structure that ensures service providers are treated fairly in a multi-cloud context. The computational optimization of DL approaches can be severely hampered by the requirement for human hyperparameter selection. Traditional automated solutions to this issue have inadequate durability or fail in specific circumstances. To choose the hyper-parameters in the Dueling Deep Q-Network (DDQN), the hybrid DL approach in this study uses the concept-based wild horse optimization (WHO) method. A community of untamed horses is evolved, and the fitness of the population is evaluated concurrently to estimate the optimum hyper-parameters. The plan changes the price appropriately to promote the use of underutilized resources and discourage the use of overutilized resources. The evaluation’s findings demonstrated that the suggested strategy can lower end-user costs while conducting compute- and data-intensive activities in a multi-cloud environment. The research was concluded by comparing current models after the results were analyzed using various performance indicators.

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Dynamic cost effective solution for efficient cloud infrastructure

Cited by 3 publications

References 24 publications

Cost and energy aware migration through dependency analysis of VM components in virtual cloud infrastructure

Cost and energy aware migration through dependency analysis of VM components in virtual cloud infrastructure

Performance Analysis of a Virtualized Cloud Data Center through Efficient VM Migrations

Dynamic pricing on maximum concurrency for heterogeneous instances using hyperparameter optimization in dueling deep reinforcement learning in a multi-cloud scenario

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