AI-based resource provisioning of IoE services in 6G: A deep reinforcement learning approach
Currently, researchers have motivated a vision of 6G for empowering the new generation of
the Internet of Everything (IoE) services that are not supported by 5G. In the context of 6G …
the Internet of Everything (IoE) services that are not supported by 5G. In the context of 6G …
Task scheduling, resource provisioning, and load balancing on scientific workflows using parallel SARSA reinforcement learning agents and genetic algorithm
Cloud computing is one of the most popular distributed environments, in which, multiple
powerful and heterogeneous resources are used by different user applications. Task …
powerful and heterogeneous resources are used by different user applications. Task …
Intelligent autoscaling of microservices in the cloud for real-time applications
Cloud applications are becoming more containerized in nature. Developing a cloud
application based on a microservice architecture imposes different challenges including …
application based on a microservice architecture imposes different challenges including …
Research on auto-scaling of web applications in cloud: survey, trends and future directions
Cloud computing emerging environment attracts many applications providers to deploy web
applications on cloud data centers. The primary area of attraction is elasticity, which allows …
applications on cloud data centers. The primary area of attraction is elasticity, which allows …
Reinforcement learning-based application autoscaling in the cloud: A survey
Reinforcement Learning (RL) has demonstrated a great potential for automatically solving
decision-making problems in complex, uncertain environments. RL proposes a …
decision-making problems in complex, uncertain environments. RL proposes a …
ADRL: A hybrid anomaly-aware deep reinforcement learning-based resource scaling in clouds
S Kardani-Moghaddam, R Buyya… - … on Parallel and …, 2020 - ieeexplore.ieee.org
The virtualization concept and elasticity feature of cloud computing enable users to request
resources on-demand and in the pay-as-you-go model. However, the high flexibility of the …
resources on-demand and in the pay-as-you-go model. However, the high flexibility of the …
A meta reinforcement learning approach for predictive autoscaling in the cloud
Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism
that supports autonomous adjustment of computing resources in accordance with fluctuating …
that supports autonomous adjustment of computing resources in accordance with fluctuating …
Deep reinforcement learning for network slicing with heterogeneous resource requirements and time varying traffic dynamics
Efficient network slicing is vital to deal with the highly variable and dynamic characteristics of
traffic in 5G networks. Network slicing addresses a challenging dynamic network resource …
traffic in 5G networks. Network slicing addresses a challenging dynamic network resource …
Why is it not solved yet? challenges for production-ready autoscaling
M Straesser, J Grohmann, J von Kistowski… - Proceedings of the …, 2022 - dl.acm.org
Autoscaling is a task of major importance in the cloud computing domain as it directly affects
both operating costs and customer experience. Although there has been active research in …
both operating costs and customer experience. Although there has been active research in …
Online scheduling of dependent tasks of cloud's workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based …
Due to different heterogeneous cloud resources and diverse and complex applications of
the users, an optimal task scheduling, which can satisfy users and cloud service providers …
the users, an optimal task scheduling, which can satisfy users and cloud service providers …