most citedDeep Back-Filling: a Split Window Technique for Deep Online Cluster Job Scheduling

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DC2024

Efficient Training Approaches for Performance Anomaly Detection Models in Edge Computing Environments

Duneesha Fernando, Maria A. Rodriguez, Patricia Arroba +2

Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these micro…

cs.RO2024

Autonomous Vehicle Patrolling Through Deep Reinforcement Learning: Learning to Communicate and Cooperate

Chenhao Tong, Maria A. Rodriguez, Richard O. Sinnott

Autonomous vehicles are suited for continuous area patrolling problems. Finding an optimal patrolling strategy can be challenging due to unknown environmental factors, such as wind…

cs.DC20244 cited

Deep Back-Filling: a Split Window Technique for Deep Online Cluster Job Scheduling

Lingfei Wang, Aaron Harwood, Maria A. Rodriguez

Job scheduling is a critical component of workload management systems that can significantly influence system performance, e.g., in HPC clusters. The scheduling objectives are ofte…

cs.DC20232 cited

CloudSim Express: A Novel Framework for Rapid Low Code Simulation of Cloud Computing Environments

Tharindu B. Hewage, Shashikant Ilager, Maria A. Rodriguez +1

Cloud computing environment simulators enable cost-effective experimentation of novel infrastructure designs and management approaches by avoiding significant costs incurred from r…

cs.DC2023

μ-DDRL: A QoS-Aware Distributed Deep Reinforcement Learning Technique for Service Offloading in Fog computing Environments

Mohammad Goudarzi, Maria A. Rodriguez, Majid Sarvi +1

Fog and Edge computing extend cloud services to the proximity of end users, allowing many Internet of Things (IoT) use cases, particularly latency-critical applications. Smart devi…