PCVM.ARIMA: predictive consolidation of virtual machines applying ARIMA method


Cloud computing adopts virtualization technology, including migration and consolidation of virtual machines, to overcome resource utilization problems and minimize energy consumption. Most of the approaches have focused on minimizing the number of physical machines and rarely have devoted attention to minimizing the number of migrations. They also decide based on the current resources utilization without considering the demand for resources in the future. Some approaches minimize the number of active physical machines and Service Level Agreement (SLA) violations with the number of unnecessary migrations. They consider the current resource utilization of physical machines and neglect from demands for future resource requirements. As a result, as time passes, the number of unnecessary migrations, and subsequently, the rate of SLA violations in data centers increases. Alternatively, several approaches only focus on a hardware level and reduce the physical machine’s dynamic power consumption. The lack of control over the overload of physical machines increases the amount of violation. In this paper, a framework called PCVM.ARIMA is presented that focuses on the dynamic consolidation of virtual machines over the minimum number of physical machines, minimize the number of unnecessary migrations, detect the physical machine overloading, and SLA based on the ARIMA prediction model. Moreover, the Dynamic Voltage and Frequency Scaling (DVFS) technique is used to apply the optimal frequency to heterogeneous physical machines. The experimental results show that the presented framework significantly reduces energy consumption while it improves the QoS factors in comparison to some baseline methods.

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Correspondence to Faramarz Safi-Esfahani.

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Appendix A: The case study

Appendix A: The case study

Virtual machine consolidation improves resource efficiency and minimizes the number of active physical machines. A vm is assigned to a pm that possesses enough resources to execute it. The following example is presented to illustrate the issue of vm consolidation without the prediction of resources. In Fig. 9, at the current time t, two heterogeneous pms with maximum operating frequency are processing three computational vms. Host1 is processing both vm1 and vm2, while has enough resources to accept vm3. A conventional vm consolidation migrates vm3 to the host1 to reduce the number of active hosts. At time t + 1, the requested resources by vm1 and vm2 are increased. If host1 does not have sufficient computational resources (CPU, memory, etc.) for vm3 requirements, host1 is overloaded, and SLA violation may happen. To avoid SLA violation, vm3 should migrate to host2 again that is a new and extra migration.

Fig. 9

Consolidation method without the prediction of future resources

According to Fig. 10, the proposed PCVM.ARIMA approach at the time t for migrating vm3 to host1 and switching host2 to the sleep mode, it firstly predicts the resource utilization of the destination host (host1) soon. As predicted, not only host1 resource demand will be increased at time t + 1, but also, the total CPU utilization host1 will exceed the upper dynamic threshold Th and SLA violation will occur. Therefore, unnecessary migration of vm3 is prevented, and each pm will continue operating with an optimized frequency.

Fig. 10

Consolidation method with the prediction of future resources

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Chehelgerdi-Samani, M., Safi-Esfahani, F. PCVM.ARIMA: predictive consolidation of virtual machines applying ARIMA method. J Supercomput 77, 2172–2206 (2021). https://doi.org/10.1007/s11227-020-03354-3

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  • ARIMA predictive model
  • Cloud computing
  • Dynamic VM consolidation
  • DVFS
  • Energy saving
  • Service Level Agreement (SLA)