Stability since Overhaul Audit Cloud Permanence
|International Journal of Computer Science and Engineering|
|© 2015 by SSRG - IJCSE Journal|
|Volume 2 Issue 3|
|Year of Publication : 2015|
|Authors : M. Suriya, T. Loga Ranjini, Dr. L. Jabasheela|
M. Suriya, T. Loga Ranjini, Dr. L. Jabasheela, "Stability since Overhaul Audit Cloud Permanence" SSRG International Journal of Computer Science and Engineering 2.3 (2015): 30-34.
M. Suriya, T. Loga Ranjini, Dr. L. Jabasheela, (2015). Stability since Overhaul Audit Cloud Permanence. SSRG International Journal of Computer Science and Engineering 2.3, 30-34.
Cloud storage services have become commercially popular due to their tremendous advantages. To provide everywhere always-onaccess, a cloud service provider maintains multiple replicas for each piece of data on geologically distributed servers. A key problem of using the replication technique in the clouds is that it is very posh to achieve strong consistency on a global scale. In this project, I first present a consistency as a service model, which consists of a large data cloud and multiple small audit clouds. In the consistency as an service model, a data cloud is maintained by a cloud service provider, and a group of users that constitute an audit cloud can verify whether the data cloud provides the promised level of consistency or not. We propose a two-level auditing architecture, in the audit cloud. Which are an auditor and the administrator for validation. The auditor will be validating the user data and check on the violations on the user side. The administrator will be monitoring the auditor and the user of the system. In this project, to implement the secure transfer and auditing, the data uploaded by the user will audit and then upload in the cloud storage. To meet the promise of everywhere 24/7 access, the cloud service provider store data replicas on multiple geographically distributed servers. A key problem of using the replication technique in the clouds is that it is very expensive to achieve strong consistency on a worldwide scale, where a user is ensured to see the largest updates. This project providing the highest level of security of information stored in the cloud using local level as well as global level auditing. Local level auditing is done by using the auditor and global level auditing done by using the admin of the system.
M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, et al., “A view of cloud computing,” Commun. ACM, vol. 53, no. 4, 2010.
 P. Mell and T. Grance, “The NIST definition of cloud computing (draft),” NIST Special Publication 800-145 (Draft), 2011.  E. Brewer, “Towards robust distributed systems,” in Proc. 2000 ACM PODC.
 ——, “Pushing the CAP: strategies for consistency and availability,” Computer, vol. 45, no. 2, 2012.
 M. Ahamad, G. Neiger, J. Burns, P. Kohli, and P. Hutto, “Causal memory: definitions, implementation, and programming,” Distributed Computing, vol. 9, no. 1, 1995.
 W. Lloyd, M. Freedman, M. Kaminsky, and D. Andersen, “Don’t settle for eventual: scalable causal consistency for widearea storage with COPS,” in Proc. 2011 ACM SOSP.
 E. Anderson, X. Li, M. Shah, J. Tucek, and J. Wylie, “What consistency does your key-value store actually provide,” in Proc. 2010 USENIX HotDep.
 C. Fidge, “Timestamps in message-passing systems that preserve the partial ordering,” in Proc. 1988 ACSC.
 W. Golab, X. Li, and M. Shah, “Analyzing consistency properties for fun and profit,” in Proc. 2011 ACM PODC.
 A. Tanenbaum and M. Van Steen, Distributed Systems: Principles and Paradigms. Prentice Hall PTR, 2002.
 W. Vogels, “Data access patterns in the Amazon.com technology platform,” in Proc. 2007 VLDB.
 ——, “Eventually consistent,” Commun. ACM, vol. 52, no. 1, 2009.
 M. Brantner, D. Florescu, D. Graf, D. Kossmann, and T. Kraska, “Building a database on S3,” in Proc. 2008 ACM SIGMOD.
 T. Kraska, M. Hentschel, G. Alonso, and D. Kossmann, “Consistency rationing in the cloud: pay only when it matters,” in Proc. 2009 VLDB.
 S. Esteves, J. Silva, and L. Veiga, “Quality-ofservice for consistency of data geo-replication in cloud computing,” Euro- Par 2012 Parallel Processing, vol. 7484, 2012.
 H. Wada, A. Fekete, L. Zhao, K. Lee, and A. Liu, “Data consistency properties and the trade-offs in commercial cloud storages: the consumers’ perspective,” in Proc. 2011 CIDR.
 D. Bermbach and S. Tai, “Eventual consistency: how soon is eventual?” in Proc. 2011 MW4SOC.
 M. Rahman, W. Golab, A. AuYoung, K. Keeton, and J. Wylie, “Toward a principled framework for benchmarking consistency,” in Proc. 2012 Workshop on HotDep.
 D. Kossmann, T. Kraska, and S. Loesing, “An evaluation of alternative architectures for transaction processing in the cloud,” in Proc. 2010 ACM SIGMOD.
 L. Lamport, “On interprocess communication,” Distributed Computing, vol. 1, no. 2, 1986.
 A. Aiyer, L. Alvisi, and R. Bazzi, “On the availability of non-strict quorum systems,” Distributed Computing, vol. 3724, 2005.
 J. Misra, “Axioms for memory access in asynchronous hardware systems,” ACM Trans. Programming Languages and Systems, vol. 8, no.1, 1986.
 P. Gibbons and E. Korach, “Testing shared memories,” SIAM J. Computing, vol. 26, no. 4, 1997.
 G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels, “Dynamo: Amazon’s highly available key-value store,” in Proc. 2007 ACM SOSP.
 T. Gormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms. MIT Press, 1990.
Cloud storage, consistency as a service (CaaS), two-level auditing.