# Optimizing Energy Efficiency in Cloud Data Centers:
## 3 Related Work  
[PERSON] et al. (2017) introduced an innovative approach to virtual machine (VM) consolidation, employing a Transformer-based Deep Reinforcement Learning (DRL) method to tackle the complexity and inefficiency inherent in operating Software-Defined Network-enabled Network Function Virtualization (SDN-enabled NFV). The common practice of distributing Virtual Network Functions (VNFs) as VMs across servers often results in energy loss due to irregular deployment patterns. The proposed methodology focuses on enhancing energy efficiency while concurrently optimizing the performance of Service Function Chains (SFCs). Through a refined VM consolidation process and the utilization of a more sophisticated DRL method, this approach holds the promise of providing a significantly more efficient solution to VM consolidation challenges within SDN-enabled NFV environments. [PERSON] et al. (2018) discuss the significance of task scheduling and assignment in cloud computing due to the migration of applications from traditional environments. They emphasize the challenges of efficiently utilizing shared resources while ensuring quality of service and minimizing the carbon footprint. The paper underscores the importance of employing effective task strategies tailored to client requirements and IT resource capacities. Additionally, it highlights the relevance of optimizing resource utilization and task execution times. The article introduced optimization techniques such as artificial bee colony (ABC), cuckoo search (CS), and particle swarm optimization (PSO) for task distribution. The objective of the paper is to analyze these algorithms based on energy consumption and resource utilization. [PERSON] et al. (2019) focus on addressing the challenges of workload balancing and resource utilization in cloud scheduling to maximize the effectiveness and performance of cloud computing systems. Workload balancing ensures that computing workloads are evenly distributed across available resources, reducing the likelihood of resource constraints and enhancing system performance. Resource utilization aims to utilize processing power, memory, and network bandwidth to their fullest capacity, improving the efficacy and cost-effectiveness of the cloud infrastructure. To tackle these challenges, the study proposes a novel optimization technique called CHPSO (Chi-squared particle swarm optimization). The proposed algorithm demonstrates its effectiveness in optimizing resource utilization compared to other algorithms such as PSO (particle swarm optimization) and CS (cuckoo search). [PERSON] et al. (2020) emphasize the pivotal role of virtualization technology in cloud systems, which are among the fastest-growing energy consumers globally. Virtualization technology plays a critical role in resource management, offering solutions to various challenges in this area. Resource consolidation stands out as a widely used technique for developing more efficient resource management policies. Specifically, virtual machine pack ing enables cloud data centers to transition from one state to a more optimized state. The emergence of container-based cloud and containerized workloads has further expanded consolidation opportunities. In their article, the authors focus on the consolidation of data centers within distributed cloud systems. They provide a comprehensive overview of IT consolidation across different levels of cloud services, including virtualized data centers and consolidation techniques. Additionally, they offer a thematic taxonomy and discuss consolidation solutions from the literature. The article concludes with a discussion of research questions and proposed future directions in the field, highlighting their importance. [PERSON] et al. [21] address the growing demand for computational and storage resources due to the increasing volume of computation and big data analysis.