Reinforcement Learning to Improve Resource Scheduling and Load Balancing in Cloud Computing

Kaveri, Parag Ravikant; Lahande, Prathamesh Vijay

doi:10.1007/s42979-022-01609-9

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…Also, in cloud computing environments, database systems hold a significant position as a crucial class of services. The performance optimization for database resources is proposed in this paper [2] "Mathematical model for higher utilization of database resources in cloud computing". The author provides an approach based on mathematical formulation and linear programming methodology to optimize database performance.…”

Section: The Literature Reviewmentioning

confidence: 99%

Finding the Optimal Number of Computing Containers in IoT Systems: Application of Mathematical Modeling Methods

Zakutynskyi

2023

ECS

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The integration of computing containers into Internet of Things (IoT) systems created a lot of challenges and opportunities in the connected devices and cloud computing industries. In this paper, the author proposed a mathematical modeling method to analyze and optimize the deployment of computing containers into an IoT-based ecosystem. By implementing mathematical modeling techniques, such as queuing theory, optimization algorithms, and statistical analysis, we aim to address key concerns related to resource allocation, workload distribution, and performance optimization. Proposed models take the dynamic nature of an IoT system, considering factors such as real-time data streams and varying workloads for the satisfaction of scalability requirements. The author aids in identifying the optimal placement strategies for computing containers, ensuring efficient resource utilization and workload balancing across the IoT network.

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Section: The Literature Reviewmentioning

confidence: 99%

Finding the Optimal Number of Computing Containers in IoT Systems: Application of Mathematical Modeling Methods

Zakutynskyi

2023

ECS

View full text Add to dashboard Cite

show abstract

Reinforcement Learning to Improve Resource Scheduling and Load Balancing in Cloud Computing

Cited by 1 publication

References 19 publications

Finding the Optimal Number of Computing Containers in IoT Systems: Application of Mathematical Modeling Methods

Finding the Optimal Number of Computing Containers in IoT Systems: Application of Mathematical Modeling Methods

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