Dynamic Multi-objective Virtual Machine Placement in Cloud Data Centers
| Autor: | Ennio Torre, Gagangeet Singh Aujla, Shajulin Benedikt, Radu Prodan, Neeraj Kummar, Hamid Mohammadi Fard, Juan J. Durillo |
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| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science Quality of service Distributed computing 020206 networking & telecommunications Cloud computing 02 engineering and technology Energy consumption computer.software_genre Multi-objective optimization Resource (project management) Virtual machine 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data center business computer Efficient energy use |
| Zdroj: | SEAA |
| Popis: | Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. Determining the effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Cloud data centers and depends on how Virtual Machines (VMs) are allocated to physical resources. In this paper, we propose a multi-objective framework for dynamic placement of VMs exploiting live-migration mechanisms which simultaneously optimize the resource wastage, overcommitment ratio and migration cost. The optimization algorithm is based on a novel evolutionary meta-heuristic using an island population model underneath. We implemented and validated our method based on an enhanced version of a well-known simulator. The results demonstrate that our approach outperforms other related approaches by reducing up to 57% migrations energy consumption while achieving different energy and QoS goals. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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