Extensive safety measures and performance analysis considering the CPU resource utilization
Keywords:
Cloud computing, Blowfish crypto- graphic technique, Homomorphism encryption, Authentication, OutsourcingAbstract
In this article, we propose and implement a novel architecture, the first of its kind, providing a high level of security for outsourcing data in a cloud computing environment consisting of multiple independent cloud providers. The framework consists of dual encryption combining Homomorphism encryption at the client end and Blowfish crypto- graphic technique at the server side for authorization. Also, we deploy the concept of data fragmentation at the client end before uploading the data to cloud storage in view to securely allocate information among multiple clouds. The diverse security issues associated with information integrity, security, confidentiality, and authentication must be addressed. Simulations and analysis were performed on an Oracle virtual machine Virtual-Box and a fog environment on Ubuntu 16.04 platform. Extensive safety measures and performance analysis considering the CPU resource utilization, integrity, cost, and delay demonstrate that our projected proposal is vastly proficient and satisfies the security requirements for secure data sharing and can withstand security attacks. Cloud computing is mentioned to evolve dynamically and cloud transformation is getting easier all the time. Different cloud aspects are emerging in an efficient manner and have the potential to transform the traditional way of computing. With the advent of data sharing in cloud computing, the demand for outsourcing data has rapidly increased in the last decade. However, several security and privacy challenges exist impeding the acceptance of cloud computing. A highly secure system is required to guard an organizational entity, its resources, and assets.
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Beloglazov, A., & Buyya, R. (2012). Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience, 24(13), 1397-1420.
Benevenuto, F., Fernandes, C., Santos, M., Almeida, V., Almeida, J., Janakiraman, G. J., & Santos, J. R. (2006, December). Performance models for virtualized applications. In International Symposium on Parallel and Distributed Processing and Applications (pp. 427-439). Springer, Berlin, Heidelberg.
Coarfa, C., Druschel, P., & Wallach, D. S. (2006). Performance analysis of TLS Web servers. ACM Transactions on Computer Systems (TOCS), 24(1), 39-69.
Dejun, J., Pierre, G., & Chi, C. H. (2009, November). EC2 performance analysis for resource provisioning of service-oriented applications. In Service-Oriented Computing. ICSOC/ServiceWave 2009 Workshops (pp. 197-207). Springer, Berlin, Heidelberg.
Esteve, M. A., Katoen, J. P., Nguyen, V. Y., Postma, B., & Yushtein, Y. (2012, June). Formal correctness, safety, dependability, and performance analysis of a satellite. In 2012 34th International Conference on Software Engineering (ICSE) (pp. 1022-1031). IEEE.
Gkantsidis, C., Miller, J., & Rodriguez, P. (2006, October). Comprehensive view of a live network coding P2P system. In Proceedings of the 6th ACM SIGCOMM conference on Internet measurement (pp. 177-188).
Henia, R., Hamann, A., Jersak, M., Racu, R., Richter, K., & Ernst, R. (2005). System level performance analysis–the SymTA/S approach. IEE Proceedings-Computers and Digital Techniques, 152(2), 148-166.
Iosup, A., Ostermann, S., Yigitbasi, M. N., Prodan, R., Fahringer, T., & Epema, D. (2011). Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Transactions on Parallel and Distributed systems, 22(6), 931-945.
Kalyvianaki, E., Charalambous, T., & Hand, S. (2009, June). Self-adaptive and self-configured CPU resource provisioning for virtualized servers using Kalman filters. In Proceedings of the 6th international conference on Autonomic computing (pp. 117-126).
Koh, Y., Knauerhase, R., Brett, P., Bowman, M., Wen, Z., & Pu, C. (2007, April). An analysis of performance interference effects in virtual environments. In 2007 IEEE International Symposium on Performance Analysis of Systems & Software (pp. 200-209). IEEE.
Li, X., Qian, Z., Lu, S., & Wu, J. (2013). Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center. Mathematical and Computer Modelling, 58(5-6), 1222-1235.
Maier, G., Sommer, R., Dreger, H., Feldmann, A., Paxson, V., & Schneider, F. (2008, August). Enriching network security analysis with time travel. In Proceedings of the ACM SIGCOMM 2008 conference on Data communication (pp. 183-194).
Mei, Y., Liu, L., Pu, X., Sivathanu, S., & Dong, X. (2011). Performance analysis of network I/O workloads in virtualized data centers. IEEE Transactions on Services Computing, 6(1), 48-63.
Pu, X., Liu, L., Mei, Y., Sivathanu, S., Koh, Y., & Pu, C. (2010, July). Understanding performance interference of i/o workload in virtualized cloud environments. In 2010 IEEE 3rd International Conference on Cloud Computing (pp. 51-58). IEEE.
Pu, X., Liu, L., Mei, Y., Sivathanu, S., Koh, Y., Pu, C., & Cao, Y. (2012). Who is your neighbor: Net i/o performance interference in virtualized clouds. IEEE Transactions on Services Computing, 6(3), 314-329.
Ramesh, A., & Suruliandi, A. (2013, March). Performance analysis of encryption algorithms for Information Security. In 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT) (pp. 840-844). IEEE.
Schad, J., Dittrich, J., & Quiané-Ruiz, J. A. (2010). Runtime measurements in the cloud: observing, analyzing, and reducing variance. Proceedings of the VLDB Endowment, 3(1-2), 460-471.
Syafrudin, M., Alfian, G., Fitriyani, N. L., & Rhee, J. (2018). Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors, 18(9), 2946.
Villari, M., Fazio, M., Dustdar, S., Rana, O., & Ranjan, R. (2016). Osmotic computing: A new paradigm for edge/cloud integration. IEEE Cloud Computing, 3(6), 76-83.
Wandeler, E. (2006). Modular performance analysis and interface-based design for embedded real-time systems (pp. 1-207). ETH Zurich.
Wandeler, E., Thiele, L., Verhoef, M., & Lieverse, P. (2006). System architecture evaluation using modular performance analysis: a case study. International Journal on Software Tools for Technology Transfer, 8(6), 649-667.
Xavier, M. G., Neves, M. V., & De Rose, C. A. F. (2014, February). A performance comparison of container-based virtualization systems for mapreduce clusters. In 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (pp. 299-306). IEEE.
Yasin, A. (2014, March). A top-down method for performance analysis and counters architecture. In 2014 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) (pp. 35-44). IEEE.
Zeng, L., Veeravalli, B., & Li, X. (2015). SABA: A security-aware and budget-aware workflow scheduling strategy in clouds. Journal of parallel and Distributed computing, 75, 141-151.
Zhou, Z., Abawajy, J., Chowdhury, M., Hu, Z., Li, K., Cheng, H., ... & Li, F. (2018). Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms. Future Generation Computer Systems, 86, 836-850.
Zonouz, S., Houmansadr, A., Berthier, R., Borisov, N., & Sanders, W. (2013). Secloud: A cloud-based comprehensive and lightweight security solution for smartphones. Computers & Security, 37, 215227.
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