Apr 8, 2014 · In this paper, we fill this gap by proposing two online ensemble learning methods for workload prediction, which address these issues in large- ...

In this paper, we fill this gap by proposing two online ensemble learning methods for workload prediction, which address these issues in large-scale server ...

This paper proposes two online ensemble learning methods for workload prediction, which address the issues that arise specifically in large-scale server ...

In this work, we propose an online server workload prediction approach based on ensemble learning which addresses these issues. We evaluate the proposed ...

Jan 11, 2024 · Ensemble learning is a powerful technique for improving the accuracy and reliability of prediction models, especially in scenarios where individual models may ...

Growing scale of server infrastructure in large datacenters has led to an increased need for effective server workload prediction mechanisms.

We design a service workload prediction model based on the structure improved Long Short-Term Memory (LSTM) network and ensemble learning technology. •. We ...

An online server workload prediction approach based on ensemble learning which addresses issues of lack of large-scale training data and changes in the ...

using an ensemble learning approach that includes four different proposed clustering methods for identifying similar groups of VMs based on VM-level features ...

In this paper, we fill this gap by proposing two online ensemble learning methods for workload prediction which address these issues in large-scale server ...