## 2 Literature Review
[14] for solving the VM placement problem combining levy flight and PSO algorithms. An EVMC method for energy-aware virtual machines consolidation was proposed by [PERSON] et al. [15], integrating machine learning and meta-heuristic techniques to optimize the energy consumption. While considering all server resources, including CPU, RAM, storage, and bandwidth, the method's scalability remains untested on larger cloud infrastructures. [PERSON] et al. [16] proposed an energy-efficient and QoS-aware approach for VM consolidation using Markov chain-based prediction and linear weighted sum. The simulation results showed a substantial reduction in the energy consumption, number of VM migrations and SLA violations compared with other VM consolidation approaches. A two-phase energy-aware load-balancing algorithm (EALBPSO) using PSO for VM migration in DVFS-enabled cloud data centers was presented by [PERSON] et al. [17]. While demonstrating improvements in power consumption and migration, scalability testing in larger data center settings is essential to validate its effectiveness. Many researchers have focused on cloud service selection as well as task assignments and their effect on resource utilization and energy consumption by effectively scheduling user tasks. [PERSON] et al. [18] introduced a comprehensive survey discussing the advantages and limitations of research investigating the cloud service brokerage concept. They also outlined various open research challenges and provided recommendations. Finally, they introduced an intelligent cloud broker for the effective selection and delivery of cloud services aiming at utilizing cloud resources. An energy-optimized embedded load-balancing approach that prioritizes the tasks regarding their execution deadline was proposed by [PERSON] et al. [19]. It also categorized the physical machines considering their configuration status. The algorithm prioritizes tasks based on execution deadlines and server configuration, utilizing DVFS to reduce energy consumption. A multi-resource alignment algorithm for VM placement and resource management in cloud environment was analyzed by [PERSON] et al. [20]. Several algorithms were compared based on CPU and memory utilization as well as the probability of task failure. Multi-resource alignment demonstrated better performance in CPU and memory utilization compared to other algorithms. However, scalability concerns and the need for evaluation in larger data center environments persist. [PERSON] et al. [21] introduced a VMP algorithm for optimizing the service allocation process. In their evaluation, they considered two scenarios based on task arrival. The first scenario assumes no prior knowledge of task execution time, and the second scenario assumes all tasks arrive simultaneously. The results indicated a significant improvement in the average task waiting time. A two-phase multi-objective VM placement and consolidation approach employing the DVFS technique was proposed by [PERSON] et al. [22]. Using a multi-objective ant colony algorithm, the approach aims to improve energy consumption and SLA violations. An effective and efficient VM consolidation approach EQ-VMC was introduced by [PERSON] et al. [23], which has the goal of optimizing energy efficiency and service quality integrating discrete differential evolution and heuristic algorithms. By considering host overload detection and VM selection, EQ-VMC aims to reduce energy consumption and enhance QoS. Simulation results demonstrated improvements in energy consumption and host overloading risk as well as improved QoS. A Best Fit Decreasing algorithm for VMP formulated as a bin-packing problem was proposed by [PERSON] et al. [24]. The simulation results demonstrated higher packing efficiency compared to other algorithms. Nevertheless, they did not take SLA violation into account.