A Secure Cloud Computing Scaling Model

Abstract:

Since many Internet enterprises employ the computing resources provided by Cloud Service Provider (CSP), how to dynamically adjust the number of rented servers and improve service quality becomes a crucial subject. A lot of efforts have been made to achieve intelligent energy saving capacity adjustment but scarcely any of them takes the security issue of cloud data into account. In this paper, we focus on the cloud storage security in the cloud computing capacity scaling. We design the security part of the whole model based on the idea of Proofs of Retrievability (POR). In the scaling part of the model, we determine the scaling strategy according to email server instances CPU utilization. With simulation and performance evaluation, we conclude that the designed model is able to enable verifier to check the integrity of the information in the cloud storage and maintain satisfied response time target within Service Level Agreement (SLA).

• Periodical: Applied Mechanics and Materials (Volumes 411-414)
• Pages: 60-66
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.411-414.60
• Online since: September 2013
• Authors: Ming Yue Lei, Zheng Huang, Qiao Yan Wen, Chun Ye Hu
• Keywords: Auto Scaling, Cloud Computing, Storage Security

References

[1] Mao M, Humphrey M. Auto-scaling to minimize cost and meet application deadlines in cloud workflows[C] High Performance Computing, Networking, Storage and Analysis (SC), 2011 International Conference for. IEEE, 2011: 1-12.

DOI: 10.1145/2063384.2063449

[2] Mao M, Li J, Humphrey M. Cloud auto-scaling with deadline and budget constraints[C] Grid Computing (GRID), 2010 11th IEEE/ACM International Conference on. IEEE, 2010: 41-48.

DOI: 10.1109/grid.2010.5697966

[3] Urgaonkar B, Shenoy P, Chandra A, et al. Agile dynamic provisioning of multi-tier internet applications[J]. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 2008, 1-31.

DOI: 10.1145/1342171.1342172

[4] Gandhi A, Harchol-Balter M, Raghunathan R, et al. Distributed, robust auto-scaling policies for power management in compute intensive server farms[C] Open Cirrus Summit (OCS), 2011 Sixth. IEEE, 2011: 1-5.

DOI: 10.1109/ocs.2011.6

[5] Qin W. and Wang Q. Modeling and control design for performance management of webservers via an IPV approach. IEEE Transactions on Control Systems Technology, 15(2): 249–265, March (2007).

[6] Leite J C B, Kusic D M, Mossé D, et al. Stochastic approximation control of power and tardiness in a three-tier web-hosting cluster[C] Proceedings of the 7th international conference on Autonomic computing. ACM, 2010: 41-50.

DOI: 10.1145/1809049.1809056

[7] Nathuji R, Kansal A, Ghaffarkhah A. Q-clouds: managing performance interference effects for qos-aware clouds[C] Proceedings of the 5th European conference on Computer systems. ACM, 2010: 237-250.

DOI: 10.1145/1755913.1755938

[8] Wang X, Chen M. Cluster-level feedback power control for performance optimization[C] High Performance Computer Architecture, 2008. HPCA 2008. IEEE 14th International Symposium on. IEEE, 2008: 101-110.

DOI: 10.1109/hpca.2008.4658631

[9] Hung C L, Hu Y C, Li K C. Auto-Scaling Model for Cloud Computing System[J].

[10] Arefin S U. Event-based automated management of cloud applications[J]. (2011).

[11] Shacham H, Waters B. Compact proofs of retrievability[M] Advances in Cryptology- ASIACRYPT 2008. Springer Berlin Heidelberg, 2008: 90-107.

DOI: 10.1007/978-3-540-89255-7_7