Academic Journal
An Efficient Virtual Machine Consolidation Algorithm for Cloud Computing
| Title: | An Efficient Virtual Machine Consolidation Algorithm for Cloud Computing |
|---|---|
| Authors: | Ling Yuan, Zhenjiang Wang, Ping Sun, Yinzhen Wei |
| Source: | Entropy, Vol 25, Iss 2, p 351 (2023) |
| Publisher Information: | MDPI AG, 2023. |
| Publication Year: | 2023 |
| Collection: | LCC:Science LCC:Astrophysics LCC:Physics |
| Subject Terms: | virtual machine consolidation model, load prediction, virtual machine migration, blockchain, Science, Astrophysics, QB460-466, Physics, QC1-999 |
| More Details: | With the rapid development of integration in blockchain and IoT, virtual machine consolidation (VMC) has become a heated topic because it can effectively improve the energy efficiency and service quality of cloud computing in the blockchain. The current VMC algorithm is not effective enough because it does not regard the load of the virtual machine (VM) as an analyzed time series. Therefore, we proposed a VMC algorithm based on load forecast to improve efficiency. First, we proposed a migration VM selection strategy based on load increment prediction called LIP. Combined with the current load and load increment, this strategy can effectively improve the accuracy of selecting VM from the overloaded physical machines (PMs). Then, we proposed a VM migration point selection strategy based on the load sequence prediction called SIR. We merged VMs with complementary load series into the same PM, effectively improving the stability of the PM load, thereby reducing the service level agreement violation (SLAV) and the number of VM migrations due to the resource competition of the PM. Finally, we proposed a better virtual machine consolidation (VMC) algorithm based on the load prediction of LIP and SIR. The experimental results show that our VMC algorithm can effectively improve energy efficiency. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1099-4300 |
| Relation: | https://www.mdpi.com/1099-4300/25/2/351; https://doaj.org/toc/1099-4300 |
| DOI: | 10.3390/e25020351 |
| Access URL: | https://doaj.org/article/061196d385094992bb8218e5766080c3 |
| Accession Number: | edsdoj.061196d385094992bb8218e5766080c3 |
| Database: | Directory of Open Access Journals |
| Full text is not displayed to guests. | Login for full access. |
| ISSN: | 10994300 |
|---|---|
| DOI: | 10.3390/e25020351 |
| Published in: | Entropy |
| Language: | English |