# Modeling and Evaluation of Power-Aware Software Rejuvenation in Cloud Systems
## 2 Related Work  
Evaluation of performance, availability, and power consumption of rejuvenation techniques in cloud systems using analytical models is an emerging topic. [PERSON] et al. studied different methods of rejuvenation and suggested three main methods for software rejuvenation in a server virtualized system: cold, warm, and migration rejuvenation [11]. In the cold rejuvenation method, active VMs on a VMM that want to be rejuvenated are switched off. After completion of the VMM rejuvenation, the switched-off VMs are restarted on the rejuvenated VMM. In the warm method, active VMs are suspended, and after completion of the VMM rejuvenation, the suspended VMs resume their running on the rejuvenated VMM. In the migration method, active VMs on the intended VMM are moved to another host VMM before rejuvenation of the intended VMM occurs. When rejuvenation is completed, they move back to their original PM. Therefore, the downtime caused by the migration method is shorter than that of other methods. Our model is also based on the migration rejuvenation method. In the model presented in [PERSON] et al. [11], the rejuvenation is periodically performed without considering the system's workload. In contrast, the rejuvenation in our model is performed according to the VMM's age. [PERSON] et al. analyzed the best period for rejuvenation to achieve high availability considering the VMM's workload [15]. They suggested a specific period of rejuvenation as the best one for each specified input workload. By changing the rate of input workload, the rejuvenation period also changes. More simply, the heavier the amount of workload, the longer the rejuvenation period; and the lighter the input workload, the shorter the rejuvenation period. Thus, rejuvenation time is adopted according to the VMM's workload. In contrast to [PERSON] et al. [15], in our proposed model, the rejuvenation time is determined based on the age of a VMM instead of using a constant period, which provides a flexible model and an accurate analysis. [PERSON] et al. presented a rejuvenation model for cloud data centers to evaluate the availability in the steady-state [10]. In their model, only one active VMM and one standby VMM were considered. Our proposed model is similar to the model presented in [PERSON] et al. [10]. In both models, the age of a VMM increases when a new workload is admitted. However, in [PERSON] et al. [10], rejuvenation is periodically performed, and the status of a VMM is checked at the end of each period to ensure that performance degradation is due to the age of the VMM and not the overload of the data center itself. If rejuvenation is required, all active VMs on the target VMM are moved to the standby VMM to continue their execution. Notably, the model with only one active VMM is not realistic for cloud data centers. In contrast to [PERSON] et al. [10], we consider several active VMMs in our model. [PERSON] et al. studied cloud data centers in different scenarios and analyzed different periods for setting rejuvenation timer in each scenario [29]. The authors specified the best rejuvenation time for achieving high availability in the steady-state. [PERSON] et al. studied rejuvenation from different aspects [16]. Amongst the presented ideas in [PERSON] et al. [16], saving a system's checkpoints periodically can also be used in case of failure to recover the state of the system. The authors applied warm rejuvenation, in which paused VMs are maintained on external memory. Compared to the approach presented in [PERSON] et al. [16], both migration rejuvenation and power-awareness are simultaneously applied in our presented model. [PERSON] et al. represented a SAN model using the Mobius tool to evaluate the availability and power consumption of cloud data centers [22]. Based on their evaluation, by switching off low load VMMs, providers not only decrease the power consumption of a cloud data center but also reach higher availability if the migration time is short enough. [PERSON] et al.