## 2 Literature Review  
The VMP in cloud data centers has attracted significant attention due to its critical role in enhancing energy efficiency, resource utilization, and overall system performance. [PERSON] et al. [1] presented an energy-efficient heuristic algorithm named minPR for optimizing VM placement in cloud data centers. Their approach focuses on minimizing energy consumption while ensuring optimal resource utilization and performance. By introducing the resource wastage factor model, the authors manage VM placement on PMs using reward and penalty mechanisms. The proposed algorithm outperformed other approaches like MBFD and RVMP [4] considering the total number of PMs and total resource waste. However, simulation results showed that the algorithm performance varies depending on the VM specification and the type of workloads. A dynamic VM consolidation approach was introduced by [PERSON] et al. [2] using a Discrete Time Markov Chain (DTMC) model and the e-MOABC algorithm, aiming to balance energy consumption, resource wastage, and system reliability in cloud data centers. The algorithm favors VMs with higher impact on the CPU utilization of the server in order to minimize VM migrations. Devising a resource usage factor technique to utilize PM resources efficiently, [PERSON] et al. [5] proposed a new VM placement algorithm to minimize the power consumption of the data center by decreasing number of total active PMs. While these approaches showed substantial improvements in energy efficiency, SLA violation need to be considered during the evaluations. [PERSON] [6] proposed the MBO-VM method to reduce energy consumption and minimize resource wastage by maximizing the packaging efficiency. Utilizing a multi-objective Monarch Butterfly Algorithm, the approach considers the CPU and memory dimensions for VM placement optimization. [PERSON] [7] proposed a normalization-based VM consolidation (NVMC) strategy that aims to place VMs while minimizing energy usage and SLA violations by reducing the number of VM live migrations. Despite considering multiple resources of PMs, reliance on CPU-centric calculations and VM sorting poses limitations in heterogeneous environments with varying workloads. The proposed approach by [PERSON] et al. [8] splits the VM consolidation problem into hosts overloaded/underload detection, the selection of VMs to be migrated,and VMs allocation. Their algorithm includes sorting the VMs in decreasing order based on CPU utilization and allocating each VM to the host that results in the least increase in power consumption due to the allocation. However, the proposed approach focused on improving energy efficiency without optimizing resource wastage. A Load-Balanced Multi-Dimensional Bin-Packing heuristic (LBMBP) to optimize resource allocation in cloud data centers was introduced by [PERSON] et al. [9]. Their approach balances active host loads while detecting overloaded and underloaded hosts for VM migration, resulting in improved resource utilization. Nevertheless, the exclusion of resource availability beyond CPU in the power model limits its reliability. [PERSON] et al. [10] focused on the bin packing with linear usage cost (BPLUC). They examined the VM placement problem with three dimensions, CPU, RAM, and bandwidth, aiming at reducing the power consumption. They compared their result with PABFD [8], GRVMP [11], and AFED-EF [12]. The approach demonstrates efficiency compared to existing methods. Following bin-packing heuristics, [PERSON] et al. [13] proposed energy-efficient VM placement algorithms, EEVMP and MEEVMP, considering server energy efficiency. The researchers aims to optimize energy consumption, QoS, and resource utilization while minimizing SLA violations. However, evaluations of the varied workloads and infrastructures while considering additional resources are necessary to ascertain the adaptability of these algorithms. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed by [PERSON] et al.