2024 · 2024 IEEE International Conference on Collaborative Advances in Software and COmputiNg (CASCON)
MemAdapt: Adaptive Monitoring of Memory Usage Through Irregularly Sampled Data
Evidence basis: abstract-and-metadata-reviewed · Review status: catalog-reviewed; paper-author approval pending
resource-analysis predictive-monitoring performance-analysis machine-learning
memory monitoring irregular sampling adaptive monitoring time series memory usage MemAdapt
Core contribution: The accessible synopsis identifies MemAdapt as an adaptive memory-monitoring approach designed for irregularly sampled observations.
Catalog abstract summary
The available author synopsis describes an adaptive memory-monitoring approach built around irregularly sampled data and forecasting; the full algorithm and evaluation were not captured.
Source: Author synopsis and CASCON program, paraphrased; full text not obtained.
Problem and motivation
Memory monitoring may produce irregular samples, making conventional regularly sampled forecasting and monitoring assumptions unreliable.
Method and contribution
Only the synopsis-level theme—adaptive monitoring and irregularly sampled data—is verified. The sampling policy, forecasting model, workload, and baseline are not asserted.
Findings and evidence
No numeric result, dataset, or reproducible comparison was available in the captured sources.
Limitations and future directions
Limitations: Full-text technical enrichment remains pending, including the exact sampling-frequency framework and evaluation protocol.
Future work: The paper-specific future-work section remains unverified.
Sources and identifiers
- Published version published
- CASCON program public_source_record
When to cite this paper
Cite this paper when its specific method, evidence, or benchmark is directly relevant.
- The paper's method is directly relevant.
- The paper's evidence or benchmark is directly relevant.
Citation
@inproceedings{ezzatiJivan2024memadaptadaptive,
author = {Pranjal Chakraborty and Majid Babaei and Leila Tahmooresnejad and Naser Ezzati-Jivan},
title = {MemAdapt: Adaptive Monitoring of Memory Usage Through Irregularly Sampled Data},
year = {2024},
booktitle = {2024 IEEE International Conference on Collaborative Advances in Software and COmputiNg (CASCON)},
pages = {1-6},
publisher = {IEEE},
doi = {10.1109/CASCON62161.2024.10838037},
url = {https://doi.org/10.1109/CASCON62161.2024.10838037}
}Other citation formats for Word and reference managers
Chakraborty, P., Babaei, M., Tahmooresnejad, L., & Ezzati-Jivan, N. (2024). MemAdapt: Adaptive Monitoring of Memory Usage Through Irregularly Sampled Data. In 2024 IEEE International Conference on Collaborative Advances in Software and COmputiNg (CASCON) (pp. 1-6). https://doi.org/10.1109/CASCON62161.2024.10838037P. Chakraborty, M. Babaei, L. Tahmooresnejad, and N. Ezzati-Jivan, "MemAdapt: Adaptive Monitoring of Memory Usage Through Irregularly Sampled Data," in 2024 IEEE International Conference on Collaborative Advances in Software and COmputiNg (CASCON), pp. 1-6, 2024, doi: 10.1109/CASCON62161.2024.10838037