Two-Step Ahead Prediction for Elastic Cloud Control System
| dc.contributor.advisor | Surafel, Lemma (PhD) | |
| dc.contributor.author | Dessie, Teka | |
| dc.date.accessioned | 2020-07-06T05:42:05Z | |
| dc.date.accessioned | 2023-11-28T14:09:12Z | |
| dc.date.available | 2020-07-06T05:42:05Z | |
| dc.date.available | 2023-11-28T14:09:12Z | |
| dc.date.issued | 2020-02 | |
| dc.description.abstract | Elasticity property of cloud computing enables dynamic provisioning of computing resources in order to match changes in application demand at run time. Cloud elasticity solution acquires or releases cloud resources automatically using either proactive or reactive mechanism. Hybrid of the two approaches has been proposed in previous researches to take their advantages and mitigate their drawbacks; however, proposed approaches do not address proactive model inaccuracy before resource deployment and after system load behavior changes. In this research, threshold-based deviation between two-step ahead recursive predictions and observed values is proposed for decision making before scaling actions in combining proactive and reactive models. In addition, Stacked Long-Short Term Memory Recurrent Neural Network has been implemented as predictive model that can learn linear and nonlinear relationships of time series. The predictive model is retrained automatically with new observations at run time to adapt system load changes. To evaluate the proposed approach, experiments are conducted using CloudSim Plus with real system CPU usage. Empirical analysis of experimental simulation revealed significance of proposed solution in alleviating proactive model inaccuracy, deployment delay and oscillation to improve utilization efficiency and application performance. Moreover, performance of online retraining demonstrated that predictive model can learn new changes at run time to enrich proactive mechanism. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/21879 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Cloud Computing, | en_US |
| dc.subject | Cloud Elasticity | en_US |
| dc.subject | Recurrent Neural Network | en_US |
| dc.subject | Long-Short Term Memory | en_US |
| dc.title | Two-Step Ahead Prediction for Elastic Cloud Control System | en_US |
| dc.type | Thesis | en_US |