Quantum mechanics

Real-time computing

Operating system

Computer science

Cloud computing

Physics

Long short term memory

Workload

Artificial intelligence

Term (time)

Artificial neural network

Recurrent neural network

Machine learning

Procedia Computer Science

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.

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