Analysis of Job Failure and Prediction Model for Cloud Computing Using Machine Learning.
Sensors (Basel)
; 22(5)2022 Mar 05.
Artículo
en Inglés
| MEDLINE | ID: covidwho-1742610
ABSTRACT
Modern applications, such as smart cities, home automation, and eHealth, demand a new approach to improve cloud application dependability and availability. Due to the enormous scope and diversity of the cloud environment, most cloud services, including hardware and software, have encountered failures. In this study, we first analyze and characterize the behaviour of failed and completed jobs using publicly accessible traces. We have designed and developed a failure prediction model to determine failed jobs before they occur. The proposed model aims to enhance resource consumption and cloud application efficiency. Based on three publicly available traces the Google cluster, Mustang, and Trinity, we evaluate the proposed model. In addition, the traces were also subjected to various machine learning models to find the most accurate one. Our results indicate a significant correlation between unsuccessful tasks and requested resources. The evaluation results also revealed that our model has high precision, recall, and F1-score. Several solutions, such as predicting job failure, developing scheduling algorithms, changing priority policies, or limiting re-submission of tasks, can improve the reliability and availability of cloud services.
Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Nube Computacional
Tipo de estudio:
Estudio experimental
/
Estudio pronóstico
Límite:
Animales
Idioma:
Inglés
Año:
2022
Tipo del documento:
Artículo
País de afiliación:
S22052035
Similares
MEDLINE
...
LILACS
LIS