M
Marco Gramaglia
Researcher at Carlos III Health Institute
Publications - 85
Citations - 2112
Marco Gramaglia is an academic researcher from Carlos III Health Institute. The author has contributed to research in topics: Orchestration (computing) & Vehicular ad hoc network. The author has an hindex of 24, co-authored 76 publications receiving 1449 citations. Previous affiliations of Marco Gramaglia include Complutense University of Madrid & IMDEA.
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Proceedings ArticleDOI
Mobile traffic forecasting for maximizing 5G network slicing resource utilization
Vincenzo Sciancalepore,Konstantinos Samdanis,Xavier Costa-Perez,Dario Bega,Marco Gramaglia,Albert Banchs +5 more
TL;DR: This paper focuses on the design of three key network slicing building blocks responsible for traffic analysis and prediction per network slice, admission control decisions for network slice requests, and adaptive correction of the forecasted load based on measured deviations.
Proceedings ArticleDOI
Optimising 5G infrastructure markets: The business of network slicing
Dario Bega,Marco Gramaglia,Albert Banchs,Vincenzo Sciancalepore,Konstantinos Samdanis,Xavier Costa-Perez +5 more
TL;DR: This paper designs an algorithm for the admission and allocation of network slices requests that maximises the infrastructure provider's revenue and ensures that the service guarantees provided to tenants are satisfied and designs an adaptive algorithm that achieves close to optimal performance.
Proceedings ArticleDOI
DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning
TL;DR: Comparative evaluations with real-world measurement data prove that DeepCog’s tight integration of machine learning into resource orchestration allows for substantial (50% or above) reduction of operating expenses with respect to resource allocation solutions based on state-of-the-art mobile traffic predictors.
Proceedings ArticleDOI
How Should I Slice My Network?: A Multi-Service Empirical Evaluation of Resource Sharing Efficiency
TL;DR: The efficiency gap introduced by non-reconfigurable allocation strategies of different kinds of resources, from radio access to the core of the network, is quantified and insights are provided on the achievable efficiency of network slicing architectures, their dimensioning, and their interplay with resource management algorithms.
Journal ArticleDOI
A Machine Learning Approach to 5G Infrastructure Market Optimization
TL;DR: A network slice admission control algorithm that ensures that the service guarantees provided to tenants are always satisfied and the design of a machine learning algorithm that can be deployed in practical settings and achieves close to optimal performance is designed.