## 1 Introduction  
On a cloud network, cloud data are extensively distributed by cloud storage providers. While sharing the data services, the cloud users can share their required information within the group. This thus mitigates the data storage complexity. Furthermore, the users cannot control the storage capacity in a physical manner [1]. Moreover, some flaws can jeopardize the data integrity due to system faults due to the software or hardware and human intervention errors. To combat such problems, cloud storage is a prerequisite when sharing on a cloud network [2]. A user should be blocked from the group or be removed from the group due to misbehavior. Hence, revocation is the standard process when auditing cloud data for storage. To ensure the security level, data management requires a private key to verify the legitimacy of generating the fileblocks [3]. Through this authentication process, the fileblocks are proven to possess the data. During the user revocation from a group, his/her private key is also removed from the user group. In the general and traditional [4] auditing approaches, the authenticators of revoked users are transformed into the authenticators of the non-revoked cloud user group. For such a scenario, the non-revoked users have to fetch all of the information using the user's fileblocks, which are used to again sign and updatenew authenticators in the cloud network. Because of the high-dimensional representation of cloud data, this process has a high cost terms of computational and overhead communication [5]. Several auditing methods have been developed to further resolve the existing issues, along with user revocation in the storage of cloud data [6]. The revoked user groups are transformed into non-revoked groups, where the private key is again required for authentication. This adds to the computational complexity problem, as it results in more fileblocks [7]. Hence, this has an impact on the cloud environment. In real-world applications, user transformation is critical to achieving better storage performance [8]. Furthermore, the performance is degraded, as this often changes the membership function of the group. Hence, the challenging factor is to design an effective model for real-time data [9]. Depending on the needs of different users, various data files are employed to store the cloud data [10]. To meet the requirements, standard storage products are implemented by service providers to save the data [11]. Thus, it becomes challenging to achieve cost-effective networks and highprovider storage capacity. In cloud data storage management and virtual machine (VM) assignment, providing high-latency, low-cost, high-quality service and scalability is challenging for researchers. Optimization techniques provide high-quality service. Various technologies have been demonstrated to provide high-quality, reliable cloud data storage, and their challenges and features are illustrated in Table 1. CMPSO [12] provides highly secure and reliable resource allocation over wireless networks and has a low computational cost. However, it does not provide a fine-tuned strategy for accommodating connectivity. Furthermore, the power consumption of the entire system is very high. ANC [13] is easy to implement with increased network performance in terms of robustness and fidelity, and also, the communication throughput is very high. Yet, this strategy is not flexible because of the changing channel qualities in wireless networks, and this decreases the total response time for users when the workload is high. The Tabu meta-heuristic [14] meets the wireless requirements such as heterogeneity, reliability, and lowlatency, and also, it provides high synchronization and updating of data over wireless networks. If an unexpected power outage occurs, the valuable data stored in the data center could be lost and unrecoverable. Hence, there is a high cost to protectthe cloud storage system. OMT [15] provides automatic services when customers require more servicesover the network channel.