Deep reinforcement learning-based scheduling in distributed systems: a critical review
Z Jalali Khalil Abadi, N Mansouri, MM Javidi - Knowledge and Information …, 2024 - Springer
Many fields of research use parallelized and distributed computing environments, including
astronomy, earth science, and bioinformatics. Due to an increase in client requests, service …
astronomy, earth science, and bioinformatics. Due to an increase in client requests, service …
Energy efficient task scheduling based on deep reinforcement learning in cloud environment: A specialized review
The expanding scale of cloud data centers and the diversification of user services have led
to an increase in energy consumption and greenhouse gas emissions, resulting in long-term …
to an increase in energy consumption and greenhouse gas emissions, resulting in long-term …
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 …
DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing
Task scheduling in cloud paradigm brought attention of all researchers as it is a challenging
issue due to uncertainty, heterogeneity, and dynamic nature as they are varied in size …
issue due to uncertainty, heterogeneity, and dynamic nature as they are varied in size …
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 …
A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques
Virtual machine (VM) integration methods have effectively proven an optimized load
balancing in cloud data centers. The main challenge with VM integration methods is the …
balancing in cloud data centers. The main challenge with VM integration methods is the …
Intelligent decision-making of load balancing using deep reinforcement learning and parallel PSO in cloud environment
Machine learning and parallel processing are extremely commonly used to enhance
computing power to induce knowledge from an outsized volume of data. To deal with the …
computing power to induce knowledge from an outsized volume of data. To deal with the …
Proficient job scheduling in cloud computation using an optimized machine learning strategy
P Neelakantan, NS Yadav - International Journal of Information …, 2023 - Springer
In contemporary technology, cloud computing is applicable in many fields like biomedical
systems, transactions, data mining, etc. In that, cloud computing job scheduling is a …
systems, transactions, data mining, etc. In that, cloud computing job scheduling is a …
A chameleon and remora search optimization algorithm for handling task scheduling uncertainty problem in cloud computing
Task scheduling in cloud computing is responsible for serving the user requirements. The
scheduling strategy must handle the problems of high load over virtual machines (VMs) …
scheduling strategy must handle the problems of high load over virtual machines (VMs) …
An energy-efficient task scheduling method for heterogeneous cloud computing systems using capuchin search and inverted ant colony optimization algorithm
S Rostami, A Broumandnia… - The Journal of …, 2024 - Springer
Cloud computing (CC) is a computing paradigm to satisfy end users' computing and storage
needs. Cloud data centers (DC) must continuously improve their performance due to the …
needs. Cloud data centers (DC) must continuously improve their performance due to the …