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Extended Functional Minimum-Storage Cooperative Regenerating for Cloud Bandwidth Problem

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Cloud storage systems to protect data from corruptions, redundant data to be tolerate failures of storage and lost data should be repaired when storage fails. Regenerating codes provide fault tolerance by striping data across multiple servers, while using less repair traffic than minimized bandwidth consumption. In previous research implemented practical Data Integrity Protection (DIP) scheme for regenerating-coding based cloud storage. Functional Minimum-Storage Regenerating (FMSR) codes and it construct FMSR-DIP codes, which allow clients to remotely verify the integrity of random subsets of long-term archival data under a multi server setting. The problem is to optimize bandwidth consumption when repairing multiple failures. The cooperative repair of multiple failures can help to further save bandwidth consumption when multiple failures are being repaired.
Key Terms: Cloud computing, Minimum storage, Bandwidth consumption.
Several trends are opening up the era of Cloud Computing, which is an Internet-based development and use of computer technology. The ever cheaper and more powerful processors, together with the “Software as a Service” (SaaS) computing architecture, are transforming data centres into pools of computing service on a huge scale. Meanwhile, the increasing network bandwidth and reliable yet flexible network connections make it even possible that clients can now subscribe high-quality services from data and software that reside solely on remote data centers. Although envisioned as a promising service platform for the Internet, this new data storage paradigm in “Cloud” brings about many challenging design issues which have profound influence on the security and performance of the overall system. One of the biggest problem in existing method is it takes more bandwidth For repairing multiple failures.
So overcome this problem we use functional minimum bandwidth cooperative regenerating method for providing minimized bandwidth consumption. Consider the large size of the outsourced electronic data and the client’s constrained resource capability, the core of the problem can be generalized as how can the client find an efficient way to perform periodical integrity verifications without the local copy of data files.
In order to solve the problem of data integrity checking, many schemes are proposed under different systems and security models. In all these works, great efforts are made to design solutions that meet various requirements: high scheme efficiency, stateless verification, unbounded use of queries and irretrievability of data, etc. Considering the role of the verifier in the model, all the schemes presented before fall into two categories: private auditability and public auditability. Although schemes with private auditability can achieve higher scheme efficiency, public auditability allows any one, not just the client (data owner), to challenge the cloud server for correctness of data storage while keeping no private information. Then, clients are able to delegate the evaluation of the service performance to an independent Third Party Auditor (TPA), without devotion of their computation resources. In the cloud, the clients themselves are unreliable or may not be able to afford the overhead of performing frequent integrity checks. Thus, for practical use, it seems more rational to equip the verification protocol with public auditability, which is expected to play a more important role in achieving economies of scale for Cloud Computing. Moreover, for efficiency consideration, the outsourced data themselves should not be required by the verifier for the verification purpose.


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