.png){kind=icon}
Return
Augmented access pattern-based I/O performance prediction using directed acyclic graph regression
.png){kind=icon}
PRE
AI
DOI：10.1007/s10586-024-04719-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With the rise of big data processing is creating a new challenge i.e., keeping pace with the flow of information and efficient I/O performance is the key here. However, analyzing I/O performance is a complex task due to the many layers involved, from applications and libraries to the operating system, storage devices, and everything in between. In this paper, we propose a convolutional neural networks (CNN)-based directed acyclic graph regression (DAGR) network to predict the I/O performance of applications. The system first gathers I/O request information directly from the storage layer (block storage). This information is then converted into a visual representation (graph image) and augmented using various techniques to create additional training data. The core of the system is a CNN-based prediction model designed to identify potential I/O performance patterns by analyzing the generated graph images. Evaluations using real-world application benchmarks demonstrate that the proposed method can accurately predict the performance of various applications, including file servers, databases, mail servers, and video servers, with an accuracy of up to 99.73%.
Keywords:
Deep learning
Directed acyclic graph
High performance computing
Image augmentation
Machine learning
Journal
C
IF:
4.1
Papers:
5.1K
Citations:
7.5K
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSavePolarization imaging based bruise detection of nectarine by using ResNet-18 and ghost bottleneck
PREAI
ShareSaveShareSave
View more