Multi-VM-Workloads Learning-based Forecasting Model for Resilient Server Management in Industry Clouds

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 260, P. 923 - 929

Published: Jan. 1, 2025

Language: Английский

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SlackVM: Packing Virtual Machines in Oversubscribed Cloud Infrastructures

Published: Sept. 24, 2024

Language: Английский

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BoaviztAPI: A Bottom-Up Model to Assess the Environmental Impacts of Cloud Services

ACM SIGEnergy Energy Informatics Review, Journal Year: 2024, Volume and Issue: 4(5), P. 84 - 90

Published: Dec. 1, 2024

Awareness surrounding the environmental impacts of digital industry has led numerous professionals to incorporate these considerations into their work. However, conceptualization often been narrowed scope carbon footprint. This limitation can be attributed various technical and data accessibility constraints, hindering a comprehensive evaluation, including multi-criteria analysis over entire life cycle technologies. In response limitations, we introduce bottom-up evaluation method suitable for servers cloud instances, employing thinking approach. We start by modeling server based on its hardware configuration. Then, aggregate with physical environment define platform. finally model instance as portion The proposed approach implemented an open-source toolkit published API. initiative aims provide developers researchers tool conducting evaluations infrastructure open methodologies, enhancing ability explore materiality ICT products, services, infrastructures.

Language: Английский

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COIN: Chance-Constrained Imitation Learning for Safe and Adaptive Resource Oversubscription under Uncertainty

Published: Oct. 20, 2024

Language: Английский

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