Enhancing security and scalability by AI/ML workload optimization in the cloud

The pervasive adoption of Artificial Intelligence (AI) and Machine Learning (ML) applications has exponentially increased the demand for efficient resource allocation, workload scheduling, and parallel computing capabilities in cloud environments. This research addresses the critical need for enhanc...

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Vydané v:	Cluster computing Ročník 27; číslo 10; s. 13455 - 13469
Hlavní autori:	Priyadarshini, Sabina, Sawant, Tukaram Namdev, Bhimrao Yadav, Gitanjali, Premalatha, J., Pawar, Sanjay R.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.12.2024 Springer Nature B.V
Predmet:	Access control Adaptive algorithms Artificial intelligence Automation Cloud computing Computer Communication Networks Computer Science Cryptography Cybersecurity Distributed processing Edge computing Efficiency Energy consumption Genetic algorithms Heuristic Machine learning Methods Operating Systems Optimization Privacy Processor Architectures Quantum computing Resource allocation Resource management Resource scheduling Scheduling Sparsity Workload Workloads Cloud computing Parallel computing Workload scheduling and Hybrid Heft Pso Ga (HEPGA) algorithm AI/Ml workloads Resource allocation
ISSN:	1386-7857, 1573-7543
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