## 2 Related Work
To improve these shortcomings, fuzzy Q-learning (FQL), which is an online decision-making strategy, is suggested. The FQL can choose one of the VM selection strategies dynamically to obtain better results than using the individual selection strategies alone [21]. VM placement determines the PM to which the selected VM is migrated, which is the same mechanism as the BPP as mentioned previously. The BPP is a typical NP-hard problem, and the solution exploration time exponentially increases with the size of the problem. Hence, studies proposing optimization techniques, such as integer programming (IP), linear programming (LP), integer linear programming (ILP), and binary integer programming (BIP), to solve for VM placement simplify the conditions and have long execution times. Consequently, most of the research related to VM placement apply the greedy heuristic or metaheuristic techniques, such as first fit decreasing (FFD) and best fit decreasing (BFD) strategies [22]. [PERSON] et al. designed a suitable genetic algorithm (GA) for VM placement and compared this with the FFD method [23]. [PERSON] et al. utilized the genetic algorithm, NSGA, and NSGA II to maximize load balancing and profit and to minimize wastage of resources for VM consolidation [24]. [PERSON] et al. predicted the VM resource demand and proposed the evolutionary algorithm to minimize cost by optimizing VM placement [25]. Ant colony optimization (ACO), which was inspired by the pheromone mechanism in ants, demonstrated high performance in various studies, but it was characterized to be an algorithm specialized for routing problems. However, the ACO method became one of the most well-known metaheuristic techniques that can be applied for VM consolidation since several works have defined the VM migration rule as the pheromone [26; 27; 28]. [PERSON] et al. suggested the green cloud computing method for the Ant colony system-based VM consolidation (ACS-VMC). They used the ant colony to find the most appropriate PM for VM placement after detecting the overload host using linear regression based on CPU usage prediction. [PERSON] and [PERSON] proposed the VM consolidation model to manage a cloud resource and increase its utilization by applying the artificial bee colony (ABC) method to solve the model [29]. This proposed VM consolidation model aimed to reduce energy consumption by referring to historical resource utilization and energy consumption data. [PERSON] et al. proposed particle swarm optimization (PSO) to reduce power consumption without violating SLA and showed excellent results in terms of SLA through a comparative experiment with Power-Aware BFD Algorithm [30]. [PERSON] et al. used k-means clustering and proposed an improved micro-genetic algorithm. This effectively reduces the energy consumption of DC and simultaneously considers continuous quality of service. Comparative experiments were conducted with PSO and GA, and improved results were shown in terms of VM migration and make-span [31]. [PERSON] et al. proposed the grouping harmony search (GHS) method to solve the VM consolidation problem by applying an efficient representation aimed at maximizing the migration efficiency, which is the ratio of the number of released PMs to the number of migrations [32]. The simulation is referred from a generator presented in an existing study, and comparison experiments between the harmony search (HS) and GHS are performed. [PERSON] and [PERSON] also applied the HSto solve the VM consolidation model by considering energy [33]. [PERSON] et al. proposed the power-aware algorithm and an adaptive HS to reduce the power consumption of the PMs [34]. They proved the efficiency of the HSA for the energy problem, but the simulations did not consider varying workloads.