Hybrid Access in Storage-class Memory-aware Low Power Virtual Memory System

Shirota, Yusuke; Shirai, Satoshi; Kanai, Tatsunori

doi:10.1109/coolchips.2019.8721344

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, the authors determined processes memory usage, but not in connection to CPU and DRAM memory usage. Also, Shirota et al (2019) developed hybrid access to memory hierarchical control mechanism, which adaptively alternated between SCM-aware low power Aggressive Paging (AP) with a small Dynamic Random-Access Memory (DRAM) as cache and direct access to memory bus coupled byte-addressable SCM. Using an optimal control prediction model created by ML, the study proposed an auto-tuning framework that dynamically determined the optimal control and optimal DRAM size when AP is selected.…”

Section: Memory Consumptionmentioning

confidence: 99%

Estimating Overhead Performance of Supervised Machine Learning Algorithms for Intrusion Detection

Baidoo

Yaokumah

Owusu

2023

International Journal of Information Technologies and Systems Approach

View full text Add to dashboard Cite

Estimating the energy and memory consumption of machine learning(ML) models for intrusion detection ensures efficient allocation of system resources. This study investigates the impact of supervised ML algorithms on the energy and memory consumption of intrusion detection systems. Experiments are conducted with seven ML algorithms and a proposed ensemble model, utilizing two intrusion detection datasets. Pearson correlation coefficient(PCC) and Spearman correlation coefficient are employed for the selection of optimum features. Regarding energy consumption, the findings reveal that the PCC with the UNSW-NB15 dataset uses the least amount of DRAM and CPU power. For ML methods, SVM utilizes the highest energy for both feature selection methods and datasets. Concerning memory consumption, the results show that decision tree uses the most current memory with PCC on the UNSW-NB15. The proposed ensemble model demonstrates the highest performance. These findings offer practical guidelines to ML experts when choosing the optimum model with the most efficient utilization of energy and memory.

show abstract

Section: Memory Consumptionmentioning

confidence: 99%