Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting Model For Cloud Datacenters

By J. Kumar, Rimsha Goomer, Ashutosh Kumar Singh. 2018

339 citations recorded by the source corpus.

Abstract In spite of various gains, cloud computing has got few challenges and issues including dynamic resource scaling and power consumption. Such affairs cause a cloud system to be fragile and expensive. In this paper we address both issues in cloud datacenter through workload prediction. The workload prediction model is developed using long short term memory (LSTM) networks. The proposed model is tested on three benchmark datasets of web server logs. The empirical results show that the proposed method achieved high accuracy in predictions by reducing the mean squared error up to 3.17 x 10-3.

Subfields: Building and Construction, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/f08b961d61eb2a61e8cb31a9befd9f1d18890f2c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Jiechao Gao
2. Haoyu Wang
3. Haiying Shen

In Computer Networks and Communications

1. Jiechao Gao
2. Haoyu Wang
3. Haiying Shen

In Information Systems

1. Jiechao Gao
2. Haoyu Wang
3. Haiying Shen

Citations in the corpus

• Task Failure Prediction in Cloud Data Centers Using Deep Learning (2019, methodology): “For each training and testing, we used the data of the first 100 time points of a task since many tasks are longer than 100 time points [27], [28].”