## 1 Introduction
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author [Citation]** & **Methodology** & **Features** & **Challenges** \\ \hline [PERSON] et al. [12] & CMISO & • It provides highly secure and reliable resource allocation over wireless networks. & • It does not provide a fine-tuned strategy for accommodating connectivity. \\  & & • It has a low computational cost. & • The power consumption of the entire system is very high. \\ [PERSON] et al. [13] & ANC & • It is easy to implement with high network performance in terms of robustness and fidelity. & • The communication throughput is very high. \\ [PERSON] et al. [14] & Tabu meta-heuristic & • It meets the wireless requirements such as heterogeneity, reliability, and lowlatency. \\  & & • It provides high synchronization and updating of the data over wireless networks. \\ [PERSON] et al. [15] & OMT & • It provides automatic services when the customer requires more services over the network channel. & • It can easily interface with the applications and data sources. \\ [PERSON] et al. [16] & EMSA algorithm & • It is highly elastic, has a lower cost, and is trustworthy. & • The information is quickly accessible by the users, and it is more reliable. \\  & & • It has high, virtually limitless storage capacity. & • It can easily interface with the applications and data sources. \\ [PERSON] et al. [17] & PKI-based signature scheme & • It has the ability of automatic storage failover. & • It has the ability of automatic storage failover. \\ [PERSON] et al. [18] & ACO & • It achieves better performance by balancing the network load. & • It provides high security and integrity of the information over the network channel. \\ [PERSON] et al. [19] & Fibonacci cryptographic & • It can handle the network traffic. & • It provides lowcomputational complexity. & • It provides lowcomputational complexity. & • It has a high consumption of network failures. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Features and challenges of reliable data storage using optimization. for access and minimize the storage cost, where the performance was measured via the capacity, storage, bandwidth, and so on. In [24], they explored the data migration approach of using virtual machines, and they included the bandwidth of the network, energy optimization, server management, and data centers in the cloud. In [25], they presented a scheduling mechanism to reduce the usage of machines to store the cloud data, in which the efficacy of the model was validated using the bandwidth, storage, and network connection. Hence, effective models have been employed to improve the reliability of cloud data storage. The prime objectives of the paper are summarized as listed below:  
* The design and implementation of a novel and reliable cloud data storage scheme to conserve the data with a hybrid meta-heuristic approach to preserve and access the cloud data whenever required. * Resolving the general and specific constraints of the components to develop a new reliable framework to allocate the cloud data by optimizing the components while allocating the respective components to particular VMs. * Developing the the novel hybrid meta-heuristic algorithm, named HP-BOA, where the conventional POA is superimposed on the traditional BOA. It is mainly utilized for optimizing the components to provide the desired outcomes. * Analyzing the model's efficacy with convergence and statistical analysis, where the comparison was performed with other classical optimization algorithms. The rest part of the paper is organized as follows. The review of the works on the cloud data storage mechanism are elaborated in Section 2. Section 3 elucidates the system model and problem definition of the data storage system. Section 4 specifies the problem and multi-objective functions in the cloud data storage model. Section 5 illustrates the hybrid optimization algorithm for optimizing the components. Section 6 discusses the simulation results and their analyses.