Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 2113 2023-05-12 19:27:36 |
2 layout Meta information modification 2113 2023-05-15 05:37:16 | |
3 rollback to version 1 Meta information modification 2113 2023-05-15 05:38:11 | |
4 layout Meta information modification 2113 2023-05-15 05:38:58 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Tahir, A.; Chen, F.; Hayat, B.; Shaheen, Q.; Ming, Z.; Ahmad, A.; Kim, K.; Lim, B.H. Reliable Storage of Cloud Data. Encyclopedia. Available online: https://encyclopedia.pub/entry/44218 (accessed on 14 June 2024).
Tahir A, Chen F, Hayat B, Shaheen Q, Ming Z, Ahmad A, et al. Reliable Storage of Cloud Data. Encyclopedia. Available at: https://encyclopedia.pub/entry/44218. Accessed June 14, 2024.
Tahir, Adnan, Fei Chen, Bashir Hayat, Qaisar Shaheen, Zhong Ming, Arshad Ahmad, Ki-Il Kim, Byung Hyun Lim. "Reliable Storage of Cloud Data" Encyclopedia, https://encyclopedia.pub/entry/44218 (accessed June 14, 2024).
Tahir, A., Chen, F., Hayat, B., Shaheen, Q., Ming, Z., Ahmad, A., Kim, K., & Lim, B.H. (2023, May 12). Reliable Storage of Cloud Data. In Encyclopedia. https://encyclopedia.pub/entry/44218
Tahir, Adnan, et al. "Reliable Storage of Cloud Data." Encyclopedia. Web. 12 May, 2023.
Reliable Storage of Cloud Data
Edit

The prime objective of the cloud data storage process is to make the service, irrespective of being infinitely extensible, a more reliable storage and low-cost model that also encourages different data storage types. Owing to the storage process, it must satisfy the cloud users’ prerequisites. Nevertheless, storing massive amounts of data becomes critical as this affects the data quality or integrity. Hence, this poses various challenges for existing methodologies. An efficient, reliable cloud storage model is proposed using a hybrid heuristic approach to overcome the challenges. The prime intention of the proposed system is to store the data effectively in the cloud environment by resolving two constraints, which are general and specific (structural). The cloud data were initially gathered and used to analyze the storage performance. Since the data were extensive, different datasets and storage devices were considered. Every piece of data as specified by its corresponding features, whereas the devices were characterized by the hardware or software components. Subsequently, the objective function was formulated using the network’s structural and general constraints. The structural constraints were determined by the interactions between the devices and data instances in the cloud. Then, the general constraints regarding the data allocation rules and device capacity were defined. To mitigate the constraints, the components were optimized using the Hybrid Pelican–Billiards Optimization Algorithm (HP-BOA) to store the cloud data. Finally, the performance was validated, and the results were analyzed and compared against existing approaches. Thus, the proposed model exhibited the desired results for storing cloud data appropriately. 

cloud data storage cloud computing resource allocation virtualization

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 costin 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. Hence, it can easily interface with the applications and the data sources. It also has a higher offloading failure probability; therefore, the transmission reliability is decreased. Furthermore, it has less scalability in the search space. The EMSA algorithm [16] is highly elastic, costless, and trustworthy. Moreover, the information is quickly accessible by the users, and it is more reliable. Finally, it has high, virtually limitless storage capacity. Yet, it does not meet the bandwidth requirements and has a low maturity level. Furthermore, it does not have any loop-back connectivity or access control. PKI-based signatures [17] provide greater hardware redundancy and have automatic storage failover. However, the packet loss ratio is very high. In addition, the signal-to-noise ratio is very high during packet transmission. Ant colony optimization (ACO) [18] achieves excellent performance by balancing the network load, providing the increased security and integrity of the information over the network channel. However, it may result in considerable network delays, and it has a high overhead and low service quality in terms of cost, security, and latency. The Fibonacci cryptographic technique [19] can handle the network traffic and has lowcomputational complexity. However, it has a high consumption of network resources, poornode authentication, a high transmission time, and less caching ability. Hence, to resolve these challenges, a new reliable cloud data storage system was developed with optimization for high-quality service.
Table 1. Features and challenges of reliable data storage using optimization.
Diverse approaches have been deployed to mitigate the cost function and increase the system’s reliability [20]. During the storing process of cloud data, some critical issues are met, such as transmission and communication overhead.

2. Reliable Cloud Data Storage: System Model and Problem Formulation

2.1. System Model

Reliable cloud data storage has become the most-effective process for managing data. Cloud storage is the process of managing the data remotely and the process of safeguarding the data with third-party servers. To store the data in the cloud, the cloud can give the assurance to improve the security of the data. It considers the four distinct kinds of entities to achieve the storage mechanism. They are “the data owner, the data user, the cloud user, and the third-party server”. The data owner manages the data to store them in various VMs. The data user can have the capacity to choose the machines where the data are recovered. Simultaneously, the third-party servers are used to check the data integrity frequently.
Storage mechanism: Cloud storage comprises many devices such as machines. Here, the data storage is nothing but mapping the logical and physical storage. Hence, considering the required components, the storage network may have several constraints while storing the data on the respective servers or VMs. Conversely, the storage process is differentiated into three types, which are explained as follows:
1
File storage: The files are hierarchically placed in this type. The information is stored in the metadata format of every file. Hence, the files are managed in higher-level abstraction types. Thus, it aids in improving performance.
2
Block storage: Here, the data or files are segmented into different chunks and represented with block addresses. This process does not contain the server for authorization.
3
Object storage: The encapsulation is performed with the object and metadata. Since the data belong to any type, they are distributed over the cloud. This also ensures the scalability and reliability of the system.
The major goals of designing a reliable cloud data storage system are listed below:
  • Data reliability and availability: By storing the data with more machines or servers, the data user can obtain the encoded data to be deciphered further as the original data. When any of the servers has a fault, they are then used by the other effective servers, thereby enhancing the data integrity and reliability of the cloud network.
  • Security: The better system enhances the security level. It also verifies the data integrity and confidentiality, which protects the network from any corrupted services.
  • Offline data owner: Once the data are outsourced to a server or machine, there is no need to check the integrity of the stored data in the system.
  • Efficiency: Due to this objective, the system’s efficacy is reached in terms of less storage space, resolving the overhead problem in communication and computation, and so on.
Considering the above key points, the proposed reliable data cloud storage system using heuristic development is represented in Figure 1.
Figure 1. Architectural representation of proposed reliable cloud data storage using HP-BOA.
The primary aim of this novel framework is to save cloud data by rectifying the general and structural constraints. Firstly, it considers the components and VMs for storage purposes. Since the components pose various constraints, a new reliable model is introduced. Each piece of cloud data is specified with individual traits, which are then characterized by hardware and software components. Consequently, the new objective function is derived for solving both constraints. The interactions and data instances in the cloud data storage define the structural constraint. Similarly, the allocation rules and device capacity are included in the general constraints. To alleviate the constraint issues, a novel HP-BOA is newly proposed. In the last stage, the performance was measured with metrics, and its simulation results were carried out. Thus, the extensive results proved that the proposed work appropriately stored the cloud data using the components.

References

  1. Tang, B.; Fedak, G. WukaStore: Scalable, Configurable and Reliable Data Storage on Hybrid Volunteered Cloud and Desktop Systems. IEEE Trans. Big Data 2017, 8, 85–98.
  2. Sookhak, M.; Richard Yu, F.; Zomaya, A.Y. Auditing Big Data Storage in Cloud Computing Using Divide and Conquer Tables. IEEE Trans. Parallel Distrib. Syst. 2018, 29, 999–1012.
  3. Ghaffar, Z.; Ahmed, S.; Mahmood, K.; Islam, S.H.; Hassan, M.M.; Fortino, G. An improved authentication scheme for remote data access and sharing over cloud storage in cyber-physical-social-systems. IEEE Access 2020, 8, 47144–47160.
  4. Yuan, Y.; Zhang, J.; Xu, W. Dynamic Multiple-Replica Provable Data Possession in Cloud Storage System. IEEE Access 2020, 8, 120778–120784.
  5. Mendes, R.; Oliveira, T.; Cogo, V.; Neves, N.; Bessani, A. Charon: A Secure Cloud-of-Clouds System for Storing and Sharing Big Data. IEEE Trans. Cloud Comput. 2021, 9, 1349–1361.
  6. Li, Y.; Yu, Y.; Min, G.; Susilo, W.; Ni, J.; Choo, K.K.R. Fuzzy identity-based data integrity auditing for reliable cloud storage systems. IEEE Trans. Dependable Secur. Comput. 2019, 16, 72–83.
  7. Yang, S.; Wieder, P.; Aziz, M.; Yahyapour, R.; Fu, X.; Chen, X. Latency-sensitive data allocation and workload consolidation for cloud storage. IEEE Access 2018, 6, 76098–76110.
  8. Wen, Z.; Cala, J.; Watson, P.; Romanovsky, A. Cost Effective, Reliable, and Secure Workflow Deployment over Federated Clouds. In Proceedings of the 2015 IEEE 8th International Conference on Cloud Computing, CLOUD 2015, New York, NY, USA, 27 June–2 July 2015; pp. 604–612.
  9. Lu, S.; Xia, Q.; Tang, X.; Zhang, X.; Lu, Y.; She, J. A reliable data compression scheme in sensor-cloud systems based on edge computing. IEEE Access 2021, 9, 49007–49015.
  10. Lu, H.; Foh, C.H.; Wen, Y.; Cai, J. Delay-Optimized File Retrieval under LT-Based Cloud Storage. IEEE Trans. Cloud Comput. 2015, 5, 656–666.
  11. Zhao, X.; Lucani, D.E.; Shen, X.; Wang, H. Reliable IoT storage: Minimizing bandwidth use in storage without newcomer nodes. IEEE Commun. Lett. 2018, 22, 1462–1465.
  12. Liu, X.; Fan, L.; Wang, L.; Meng, S. Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm. Math. Probl. Eng. 2016, 2016.
  13. Li, J.; Liu, Y.; Zhang, Z.; Ren, J.; Zhao, N. Towards Green IoT Networking: Performance Optimization of Network Coding Based Communication and Reliable Storage. IEEE Access 2017, 5, 8780–8791.
  14. Kherraf, N.; Sharafeddine, S.; Assi, C.M.; Ghrayeb, A. Latency and Reliability-Aware Workload Assignment in IoT Networks with Mobile Edge Clouds. IEEE Trans. Netw. Serv. Manag. 2019, 16, 1435–1449.
  15. Eraşcu, M.; Micota, F.; Zaharie, D. Scalable optimal deployment in the cloud of component-based applications using optimization modulo theory, mathematical programming and symmetry breaking. J. Log. Algebr. Methods Program. 2021, 121, 100664.
  16. Sathya, A.; Raja, S.K.S. Privacy Preservation-Based Access Control Intelligence for Cloud Data Storage in Smart Healthcare Infrastructure. Wirel. Pers. Commun. 2021, 118, 3595–3614.
  17. Ji, Y.; Shao, B.; Chang, J.; Bian, G. Flexible identity-based remote data integrity checking for cloud storage with privacy preserving property. Clust. Comput. 2022, 25, 337–349.
  18. Lee, O.T.; Akash, G.J.; Kumar, S.D.; Chandran, P. Storage Node Allocation Methods for Erasure Code-based Cloud Storage Systems. Arab. J. Sci. Eng. 2019, 44, 9127–9142.
  19. Sumathi, M.; Sangeetha, S. A group-key-based sensitive attribute protection in cloud storage using modified random Fibonacci cryptography. Complex Intell. Syst. 2021, 7, 1733–1747.
  20. Wen, M.; Ota, K.; Li, H.; Lei, J.; Gu, C.; Su, Z. Secure data deduplication with reliable key management for dynamic updates in CPSS. IEEE Trans. Comput. Soc. Syst. 2015, 2, 137–147.
More
Information
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , , , , , , ,
View Times: 356
Revisions: 4 times (View History)
Update Date: 15 May 2023
1000/1000
Video Production Service