An approximate dynamic programming approach to resource management in multi-cloud scenarios

DOI10.1080/00207179.2016.1185802zbMath1360.90270OpenAlexW2403425231MaRDI QIDQ2978074

Alessandro Giuseppi, Alessandro di Giorgio, Francesco Delli Priscoli, Vincenzo Suraci, Antonio Pietrabissa, Martina Panfili

Publication date: 21 April 2017

Published in: International Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207179.2016.1185802

zbMATH Keywords

Markov decision processreinforcement learningresource managementapproximate dynamic programmingcloud networks

Mathematics Subject Classification ID

Dynamic programming (90C39) Markov and semi-Markov decision processes (90C40) Distributed systems (68M14)

Related Items (4)

A centre–free approach for resource allocation with lower bounds ⋮ A Lyapunov-based version of the value iteration algorithm formulated as a discrete-time switched affine system ⋮ Modelling and solving resource allocation problems via a dynamic programming approach ⋮ Allocating resources via price management systems: a dynamic programming-based approach

Cites Work

• A reinforcement learning approach to call admission and call dropping control in links with variable capacity
• Admission control in UMTS networks based on approximate dynamic programming
• Reinforcement learning algorithms with function approximation: recent advances and applications
• Congestion control in high-speed communication networks using the Smith principle
• A lexicographic approach to constrained MDP admission control
• A policy approximation method for the UMTS connection admission control problem modelled as an MDP
• An Alternative LP Formulation of the Admission Control Problem in Multiclass Networks
• A reliable cooperative and distributed management for wireless industrial monitoring and control
• Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints
• Unnamed Item

This page was built for publication: An approximate dynamic programming approach to resource management in multi-cloud scenarios