| Autor: |
Mikram, Hind, El Kafhali, Said, Saadi, Youssef |
| Zdroj: |
International Journal of Cloud Applications and Computing (IJCAC); November 2021, Vol. 12 Issue: 1 p1-24, 24p |
| Abstrakt: |
In recent years, companies and researchers have hosted and rented computer resources over the internet due to cloud computing, which led to an increase in the energy consumed by data centers. This consumption is considered one of the world's highest, which pushed many researchers to propose several techniques such as server consolidation (SC) to solve the trade-off between energy saving and quality of service (QoS). SC requires maintaining service level agreements (SLA) violations and minimizing the number of active physical machines (PMs). Furthermore, to achieve this balance and avoid increasing hardware costs, the SC challenge targets placing new virtual machines (VMs) in suitable PMs. This work explored the existing SC algorithms that include CloudSim as a simulator environment and PlanetLab as a dataset. The authors compared the well-known optimization methods and extracted the weaknesses of the main three deployed approaches involved in the consolidation process: bin-packing model, metaheuristics, and machine learning-based solutions. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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