Cost-aware job scheduling for cloud instances using deep reinforcement learning
As the services provided by cloud vendors are providing better performance, achieving auto-
scaling, load-balancing, and optimized performance along with low infrastructure …
scaling, load-balancing, and optimized performance along with low infrastructure …
Cost-aware scheduling systems for real-time workflows in cloud: An approach based on Genetic Algorithm and Deep Reinforcement Learning
With the development of cloud computing, a growing number of applications are migrating to
a cloud environment. In the process, the real-time scheduling of workflows has gradually …
a cloud environment. In the process, the real-time scheduling of workflows has gradually …
Energy-aware systems for real-time job scheduling in cloud data centers: A deep reinforcement learning approach
With the advantages such as high-performance, low-maintenance, and reliability, more and
more companies are moving their computing infrastructures to the cloud. In the meantime …
more companies are moving their computing infrastructures to the cloud. In the meantime …
Deep reinforcement learning for load-balancing aware network control in IoT edge systems
Load balancing is directly associated with the overall performance of a parallel and
distributed computing system. Although the relevant problems in communication and …
distributed computing system. Although the relevant problems in communication and …
Deep adversarial imitation reinforcement learning for QoS-aware cloud job scheduling
Although cloud computing is one of the promising technologies for online business services,
how to schedule real-time cloud jobs with high quality of service (QoS) is still challenging …
how to schedule real-time cloud jobs with high quality of service (QoS) is still challenging …
A deep reinforcement learning-based preemptive approach for cost-aware cloud job scheduling
With some specific characteristics such as elastics and scalability, cloud computing has
become the most promising technology for online business nowadays. However, how to …
become the most promising technology for online business nowadays. However, how to …
[Retracted] AGTH‐Net: Attention‐Based Graph Convolution‐Guided Third‐Order Hourglass Network for Sports Video Classification
M Gao, W Cai, R Liu - Journal of Healthcare Engineering, 2021 - Wiley Online Library
As a hot research topic, sports video classification research has a wide range of applications
in switched TV, video on demand, smart TV, and other fields and is closely related to …
in switched TV, video on demand, smart TV, and other fields and is closely related to …
Cost-aware real-time job scheduling for hybrid cloud using deep reinforcement learning
L Cheng, A Kalapgar, A Jain, Y Wang, Y Qin… - Neural Computing and …, 2022 - Springer
Hybrid cloud computing enables enterprises to get the best of both private and public cloud
models. One of its primary benefits is to reduce operational costs, and the prerequisite is that …
models. One of its primary benefits is to reduce operational costs, and the prerequisite is that …
DRL-Based Backbone SDN Control Methods in UAV-Assisted Networks for Computational Resource Efficiency
The limited coverage extension of mobile edge computing (MEC) necessitates exploring
cooperation with unmanned aerial vehicles (UAV) to leverage advanced features for future …
cooperation with unmanned aerial vehicles (UAV) to leverage advanced features for future …
Adaptive routing in wireless mesh networks using hybrid reinforcement learning algorithm
S Mahajan, R Harikrishnan, K Kotecha - IEEE Access, 2022 - ieeexplore.ieee.org
Wireless mesh networks are popular due to their adaptability, easy-setup, flexibility, cost,
and transmission time-reductions. The routing algorithm plays a vital role in transferring the …
and transmission time-reductions. The routing algorithm plays a vital role in transferring the …