## 1 Introduction  
Datacenters (DCs) consume large amounts of energy for IT infrastructure operations, such as servers, storage devices, and network equipment, and also use energy to maintain an environment with constant temperature and humidity. DCs have significantly higher operating expenditures than capital expenditures and are considered to be the single largest power consuming buildings that account for approximately 2% of the global power consumption [1]. The recent growth of the ICT industry, along with the increasing demand for artificial intelligence, big data, and Internet of Things (IoT) has driven the transformation from on-premises to the cloud environment. Further, the number of DCs worldwide, which is about 8.6 million as of 2017, is expected to grow rapidly in the future. In fact, the Hyper-Scale DC, which is a datacenter with thousands of servers focused on processing and managing the exponentially increasing volumes of data, is expected to grow by about 53%, from 338 in 2016 to 628 by 2021 [2]. DCs must provide uninterrupted service for 365 days in a year, and the downtime cost per minute is estimated to be about $9000 [3]. In addition, malfunctions of DCs, which are associated with the social system, can cause secondary and tertiary damages, which are difficult to quantify in terms of scale. Therefore, the management of DCs has been conservative thus far instead of considering power-efficient operational measures. Examples of conservative management include allocating too many resources to react to less frequent potential peak loads when operating under the same original configuration from the beginning to end and allocating resources by estimating the demand growth of the cloud resource during the lifespan of the DC. Power usage effectiveness (PUE) is an evaluation index of a DC's energy efficiency and is the ratio of the total power requirement of the DC to the power consumed by the IT equipment in the DC. A PUE value close to unity means higher energy efficiency [4]. Globally, companies have built and operated eco-friendly DCs utilizing geological advantages, such as using external air-conditioning in the arctic circle or building DCs at the bottom of the sea [5; 6]. It also utilizes solar, wind, and renewable energy to operate DC efficiently [7]. These eco-friendly DCs show high energy efficiencies because heat management of the equipment requires less manual effort. However, as these DCs are built in remote areas, they are harder to access and respond to because of latency. Therefore, eco-friendly DCs with geological advantages are utilized as backups to analyze and store data rather than as main DCs for operating mission-critical systems. Thus, to improve the energy efficiencies of DCs, utilizing only geological advantages has limitations, and efforts must be considered to improve the operating systems internally. Many studies related to computing systems for energy efficiency have been conducted at the DC, operating system (OS), virtualization, hardware, and firmware levels. Analyses of various methodologies to reduce the power consumption of the DC have shown that the most effective scheme involves a server-idle switch [8]. It has been shown that the server-idle switch has a significant effect on energy savings because leaving the server on and idling accounts for approximately 70% of total energy consumed in comparison to full utilization of the server [9]. Servers (physical machines: PMs) have higher specifications than personal computers, with the purpose of providing computing power for large volumes of data, and server virtualization technology is applied to efficiently utilize these resources. Server virtualization refers to logically dividing a PM into several virtual machines (VMs). As shown in Figure 1, virtualization runs the application by dividing the main OS into several smaller OSs through a hypervisor, which is a virtualization platform, unlike the traditional architecture of one OS for each server.