Towards Advanced Monitoring for Scientific Workflows
Author:
Affiliation:
1. Technische Universität,Berlin,Germany
2. Zuse Institute,Berlin,Germany
3. TU Darmstadt,Germany
4. Humboldt-Universität zu,Berlin,Germany
Funder
Deutsche Forschungsgemeinschaft
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10020192/10020156/10020864.pdf?arnumber=10020864
Reference35 articles.
1. Tarema: Adaptive Resource Allocation for Scalable Scientific Workflows in Heterogeneous Clusters
2. Ruya: Memory-Aware Iterative Optimization of Cluster Configurations for Big Data Processing
3. Feedback-Based Resource Allocation for Batch Scheduling of Scientific Workflows
4. Get Your Memory Right: The Crispy Resource Allocation Assistant for Large-Scale Data Processing
5. Leveraging Reinforcement Learning for Task Resource Allocation in Scientific Workflows
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1. CMK: Enhancing Resource Usage Monitoring across Diverse Bioinformatics Workflow Management Systems;Journal of Grid Computing;2024-08-01
2. Towards a Peer-to-Peer Data Distribution Layer for Efficient and Collaborative Resource Optimization of Distributed Dataflow Applications;2023 IEEE International Conference on Big Data (BigData);2023-12-15
3. Predicting Dynamic Memory Requirements for Scientific Workflow Tasks;2023 IEEE International Conference on Big Data (BigData);2023-12-15
4. Leveraging Reinforcement Learning for Task Resource Allocation in Scientific Workflows;2022 IEEE International Conference on Big Data (Big Data);2022-12-17
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