Resource Allocation in Data Centers Using Fast Reinforcement Learning Algorithms

Jiang, Yuang; Kodialam, Murali; Lakshman, T. V.; Mukherjee, Sarit

doi:10.1109/tnsm.2021.3100460

Cited by 16 publications

(1 citation statement)

References 26 publications

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“…Genetic algorithms, for example, are inspired by natural selection and biological evolution to find optimal solutions. Reinforcement learning is another technique that has been successfully used in resource management and allocation in cloud computing [55][56][57]. This technique is based on a learning model in which an agent interacts with an environment and receives a reward or punishment based on its actions.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Resource Orchestration for 5G Edge Infrastructures

Moreno-Vozmediano,

Montero,

Huedo

et al. 2024

Future Internet

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The adoption of edge infrastructure in 5G environments stands out as a transformative technology aimed at meeting the increasing demands of latency-sensitive and data-intensive applications. This research paper presents a comprehensive study on the intelligent orchestration of 5G edge computing infrastructures. The proposed Smart 5G Edge-Cloud Management Architecture, built upon an OpenNebula foundation, incorporates a ONEedge5G experimental component, which offers intelligent workload forecasting and infrastructure orchestration and automation capabilities, for optimal allocation of virtual resources across diverse edge locations. The research evaluated different forecasting models, based both on traditional statistical techniques and machine learning techniques, comparing their accuracy in CPU usage prediction for a dataset of virtual machines (VMs). Additionally, an integer linear programming formulation was proposed to solve the optimization problem of mapping VMs to physical servers in distributed edge infrastructure. Different optimization criteria such as minimizing server usage, load balancing, and reducing latency violations were considered, along with mapping constraints. Comprehensive tests and experiments were conducted to evaluate the efficacy of the proposed architecture.

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Section: Related Workmentioning

confidence: 99%