Dynamic Resource Prediction and Allocation for Cloud Data Center Using the Multiobjective Genetic Algorithm

Fan Hsun Tseng, Xiaofei Wang, Li Der Chou, Han Chieh Chao, Victor C.M. Leung

Research output: Contribution to journalArticlepeer-review

96 Scopus citations


In order to optimize the resource utilization of physical machines (PMs), the workload prediction of virtual machines (VMs) is vital but challenging. Most of existing literatures focus on either resource prediction or allocation individually, but both of them are highly correlated. In this paper, we propose a multiobjective genetic algorithm (GA) to dynamically forecast the resource utilization and energy consumption in cloud data center. We formulate a multiobjective optimization problem of resource allocation, which considers the CPU and memory utilization of VMs and PMs, and the energy consumption of data center. The proposed GA forecasts the resource requirement of next time slot according to the historical data in previous time slots. We further propose a VM placement algorithm to allocate VMs for next time slot based on the prediction results of GA. In our simulation-based analysis, the optimal solution for resource prediction under stable and unstable utilization tendency is found by the proposed GA. The prediction result is superior to the previous proposed Grey forecasting model. Results show that the proposed VM placement algorithm not only increases the average utilization level of CPU and memory but also decreases the energy consumption of cloud data center.

Original languageEnglish
Pages (from-to)1688-1699
Number of pages12
JournalIEEE Systems Journal
Issue number2
StatePublished - Jun 2018


  • Cloud data center
  • genetic algorithm (GA)
  • multiobjective optimization (MOO)
  • resource allocation
  • resource prediction


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