Deep reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions
With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer
dynamic, reliable and elastic computing services. Efficient scheduling of resources or …
dynamic, reliable and elastic computing services. Efficient scheduling of resources or …
A resource optimization scheduling model and algorithm for heterogeneous computing clusters based on GNN and RL
Z Zhang, C Xu, K Liu, S Xu, L Huang - The Journal of Supercomputing, 2024 - Springer
In the realm of heterogeneous computing, the efficient allocation of resources is pivotal for
optimizing system performance. However, user-submitted tasks are often complex and have …
optimizing system performance. However, user-submitted tasks are often complex and have …
Startup-aware dependent task scheduling with bandwidth constraints in edge computing
In edge computing, applications can be scheduled in the granularity of inter-dependent
tasks to proximate edge servers to achieve high performance. Before execution, the edge …
tasks to proximate edge servers to achieve high performance. Before execution, the edge …
Orchestrating scheduling, grou** and parallelism to enhance the performance of distributed stream computing system
In a big data stream computing environment, the arrival rate of data streams usually
fluctuates over time, posing a great challenge to the elasticity of system. The performance of …
fluctuates over time, posing a great challenge to the elasticity of system. The performance of …
Learning to optimize DAG scheduling in heterogeneous environment
J Luo, Y Zhou, X Li, M Yuan, J Yao, J Zeng - arxiv preprint arxiv …, 2021 - arxiv.org
Scheduling job flows efficiently and rapidly on distributed computing clusters is one of huge
challenges for daily operation of data centers. In a practical scenario, a single job consists of …
challenges for daily operation of data centers. In a practical scenario, a single job consists of …
An end-to-end bi-objective approach to deep graph partitioning
Graphs are ubiquitous in real-world applications, such as computation graphs and social
networks. Partitioning large graphs into smaller, balanced partitions is often essential, with …
networks. Partitioning large graphs into smaller, balanced partitions is often essential, with …
Deep Reinforcement Learning-Based Continuous Workflows Scheduling in Heterogeneous Environments
Workflow scheduling plays a critical role in optimizing completion time and throughput in
distributed cloud environments, leveraging the parallelism of heterogeneous computing …
distributed cloud environments, leveraging the parallelism of heterogeneous computing …
[PDF][PDF] Fast and Fine-grained Autoscaler for Streaming Jobs with Reinforcement Learning.
On computing clusters, the autoscaler is responsible for allocating resources for jobs or
finegrained tasks to ensure their Quality of Service. Due to a more precise resource …
finegrained tasks to ensure their Quality of Service. Due to a more precise resource …
CoRaiS: Lightweight Real-Time Scheduler for Multi-Edge Cooperative Computing
Multi-edge cooperative computing that combines constrained resources of multiple edges
into a powerful resource pool has the potential to deliver great benefits, such as a …
into a powerful resource pool has the potential to deliver great benefits, such as a …
Learning to optimize dag scheduling in heterogeneous environment
Y Zhou, X Li, J Luo, M Yuan, J Zeng… - 2022 23rd IEEE …, 2022 - ieeexplore.ieee.org
Scheduling job flows efficiently and rapidly on distributed computing clusters is one of huge
challenges for daily operation of data centers. In a practical scenario, a single job consists of …
challenges for daily operation of data centers. In a practical scenario, a single job consists of …