## 2 Related Work  
[PERSON] et al. [12] implemented the \"constrained multi-objective particle swarm optimization (CMPSO)\" to enhance the reliability of data stored in the cloud. Here, three different costs were considered: data migration, occupation cost in the storage space, and communication cost. To ensure the effectiveness of the storage schemes, the diverse metrics considered were equipment stability, software, and transmission reliability. Finally, the implementation and a comparative analysis were performed. It was evaluated with the three criteria for data storage. Hence, the simulation outcome of the model was that it outperformed the scalability of other classical methodologies. [PERSON] et al. [13] explored the model of Adaptive Network Coding (ANC) to increase the transmission performance. It obtained the best storage results compared with the traditional mechanisms. Additionally, the optimum value was obtained to appropriately determine the allocation for data storage. Due to this allocation, the data were distributed with x number of data components among X total data centers, which were also processed based on the probability factor. Consequently, the Optimal Storage Allocation (OSA) was utilized to rectify the existing problems. Both qualitative and quantitative analyses were performed, which proved the reliability of the data storage scheme. [PERSON] et al. [14] utilized the Workload Assignment (WA) and Mixed-Integer Program (MIP) to derive a novel formulation for data storage. The problems in the WA were segregated into two sub-divisions: Latency-Aware Workload Assignment (LAWA-MIP) and Reliability-Aware Candidate Selection (RACS). Hence, the performance of the enhanced WA-Tabu model was measured using existing approaches and compared with the former methods. With the help of the simulation, they achieved the desired outcome to ensure the effectiveness of the cloud storage system. [PERSON] et al. [15] designed an automated data storage model in the cloud. It was designed based on the components used to allocate the machines to perform the services. This allocation was processed by considering the requirements of the software or hardware concepts. It aided in resolving the multi-objective issues while using the optimization algorithms and reduced the computational burden while storing the data. Techniquessuch as Mathematical Programming (MP) and Optimization Modulo Theory (OMT) were considered for the experimentation. Hence, the existing challenges were mitigated by using the objective of search space reduction. The prime purpose of this model was (a) to achieve a symmetric value in a cloud system and (b) to represent in a graphical manner the functionalities related to specific constraints and (c) their association. In the last stage, the empirical results were measured with diverse metrics, and the efficiency was enhanced by obtaining the optimum values. [PERSON] et al. [26] proposed a novel resource allocation algorithm for cloud computing based on the Reptile Search Algorithm (RSA), which mimics the hunting and encircling behaviors of crocodiles. The paper applied the algorithm to a healthcare application that involved Electrocardiogram (ECG) sensors and cloud data storage. The paper claimed that the algorithm can minimized the cumulative cost and improved the quality of service for cloud computing. Similarly, [PERSON] et al. [27] presented an improved Ant Colony Optimization (ACO) algorithm for resource allocation in cloud computing for the new energy industry. The paper considered the characteristics and requirements of the new energy industry, such as load balancing, energy efficiency, and real-time response. The paper showed that the improved ACO algorithm could achieve better performance than the traditional ACO algorithm in terms of task completion time, number of iterations, and the load-balancing effect. [PERSON] and [PERSON] [16] developed the \"Euclidean L3P-based multi-objective successive approximation (EMSA)\" to ensure the security or privacy of the data.