DPRA: Dynamic Power-Saving Resource Allocation for Cloud Data Center Using Particle Swarm Optimization

Li Der Chou, Hui Fan Chen, Fan Hsun Tseng, Han Chieh Chao, Yao Jen Chang

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


Cloud computing provides the scalable computation capability based on a virtualization technique. The energy conservation for green computing is one of the vital issues while allocating resources. To improve energy efficiency, the dynamic power-saving resource allocation (DPRA) mechanism based on a particle swarm optimization algorithm is proposed. The DPRA mechanism not only considers the energy consumption of physical machine (PM) and virtual machine (VM) but also newly tackles the energy efficiency ratio of air conditioner. Moreover, the least squares regression method is utilized to forecast PM's resource utilization for allocating VM and eliminating VM migrations. In simulation, the proposed DPRA mechanism is compared with three familiar allocation schemes and one previous solution. Simulation results show that in the presence of VM number, DPRA outperforms traditional schemes and previous solution in terms of total energy consumption (includes PMs and air conditioners), total electric bill, VM migration, and the number of shutdown PMs, chosen as objective performance metrics.

Original languageEnglish
Pages (from-to)1554-1565
Number of pages12
JournalIEEE Systems Journal
Issue number2
StatePublished - Jun 2018


  • Cloud computing
  • data center
  • least squares regression
  • particle swarm optimization (PSO)
  • resource allocation


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