# Optimizing Energy Efficiency in Cloud Data Centers:
## 3 Related Work
While purchasing additional resources can be costly, cloud computing offers a solution through its \"pay as you go\" plans, allowing users to pay only for the resources they utilize. However, leveraging cloud technology presents challenges, particularly in resource management. Resource management focuses on allocating resources to users efficiently, minimizing waste. In their paper, the authors propose a novel energy-aware resource management technique. This approach combines joint virtual machine (VM) and container consolidation methods with the deep Q-learning algorithm to promote green computing in cloud data centers. The aim is to reduce resource waste, migration rates, and energy consumption through optimized resource allocation strategies. [PERSON] et al. [22] explore the challenges of efficiently managing virtual machines in cloud data centers to balance economic costs and ensure quality of service (QoS) while increasing end-user demands. They highlight the benefits of virtual machine migration, such as cost savings, energy efficiency, improved performance, stability, and availability. However, stakeholders' objectives often conflict with each other, and the optimal resource allocation problem in cloud infrastructure is typically NP-hard or NP-complete. In their paper, the authors formulate the virtual migration problem using game theory to achieve both load balance and resource utilization. They propose the V2 PQL algorithm, which integrates Markov decision processes and the Q-learning algorithm. Simulation results demonstrate the efficiency of their proposal, which consists of training and extraction phases. The V2 PQL algorithm is benchmarked against other algorithms such as Round-Robin, inverse Ant System, Max-Min Ant System, and Ant System, highlighting its strength and feasibility in the extraction phase. [PERSON] et al. [23] discuss the importance of multi-objective virtual machine (VM) placement in data centers for achieving various goals. While this is an NP-hard problem, numerous approaches have been proposed to address it. However, many existing methods overlook the selection of weights, which is crucial for multi-objective optimization. Poorly chosen weights can lead to solutions deviating from the Pareto optimal set. The authors propose a solution to this problem using the Chebyshev scalarization function in multi-objective reinforcement learning (RL). Their proposed VM placement algorithm, VMPMORL, leverages the Chebyshev scalarization function to find a Pareto approximate set that minimizes both energy consumption and resource wastage simultaneously. Unlike other multi-objective RL algorithms in VM placement, VMPMORL tackles the weight selection problem and utilizes the concept of the Pareto set. Comparative evaluations against state-of-the-art algorithms demonstrate that VMPMORL achieves superior performance. The approaches outlined in the literature address various aspects of virtual machine (VM) consolidation and resource management, primarily focusing on energy efficiency, task scheduling, and load balancing. As summarized in the Table 1, most previous works (e.g., [18; 19]) rely on heuristic or metaheuristic algorithms to optimize energy consumption. However, they often neglect other critical factors such as SLA compliance and VM sensitivity. Despite the emergence of reinforcement learning-based solutions in recent studies ([21, 22, 23]), these methods often overlook the heterogeneity and sensitivity of VMs. This oversight can result in suboptimal decisions, particularly for services that require high availability and performance guarantees. In contrast, our proposed framework introduces a context-aware and adaptive architecture that effectively addresses these limitations. Initially, we utilize random forest (RF) and self-organizing maps (SOMs) to classify VMs according to their sensitivity levels. This classification step allows the system to differentiate between critical and non-critical workloads.