## 1 Introduction
The hypervisor allocates PM resources, such as central processing unit (CPU), memory, and network, to individual OSs. One unit of such an OS is called the VM. Most of the recently developed PMs for DCs are managed in virtualization environments. The hypervisor configures the virtualization environment and offers live migration features that allow the operational VM to move between PMs without service interruptions. When a destination PM has available resources for the VM resource allocated to a source PM, then a VM can be migrated. When all the VMs in a certain PM are migrated to another PM, the original PM is empty and available for the idle switch. This methodology minimizes power consumption by migrating the VMs and switching a PM to idle, i.e., VM consolidation, which is one of the methods to effectively improve excessive power consumption by a DC [10]. VM consolidation maximizes PM idle switching by migrating the VMs to appropriate places, but the VM migration itself should be discouraged as much as possible [11; 12]. Figure 1: Traditional and virtual architecture server comparison. Sometimes, downtimes occurring during VM migration can violate the SLA although applications installed in the VM continue operating during the migration [13; 14]. In addition, migration itself is a task that induces overloading; hence, it is not recommended to migrate several VMs simultaneously. In the actual environment, this function is only utilized as cold migration after suspending the VM during a scheduled preventive maintenance. From the practical perspective of operating a DC, decision makers must have some insights into PM-VM mapping information. However, if this information changes frequently owing to VM migration, the complexity of management is intensified. PMs generate large quantities of heat while handling tasks; this heat generated by a PM is proportional to its performance and power consumption, which means that high performance and high power consumption generate large quantities of heat. Excessive heat generated from the rack of a DC may cause equipment damage, which adversely affects the lifespan of IT equipment. Consequently, budget planning should be established for when and how much of the IT equipment must be replaced during the course of operating a DC. However, budget planning for DCs cannot be accurately implemented, and breakdown cannot be accurately predicted if the PMs are frequently replaced before expiration of their lifespans owing to performance degradation. In this study, we propose a VM consolidation model to reduce the DC power consumption and minimize VM migrations. The VM consolidation model improves the power efficiency and ensures reliable operation of the DC. In addition, we explore VM combinations to steadily maintain PM workloads to enable reliable operation from the perspective of lifespan of the IT infrastructure. To effectively solve the proposed model, we apply the adaptive harmony search method to reduce the complexity of setting parameters among the various harmony search methods, which have shown excellent performance for optimizing various problems in the field. The remainder of this paper is organized as follows. In Section 2, research related to VM consolidation and harmony search are reviewed. We propose the VM consolidation model in Section 3 and describe the design of the metaheuristic technique for solving the model in Section 4. Section 5 presents the experimental design and results, and Section 6 presents some conclusions and scope for future study.