As the interest for mobile cloud computing keeps on expanding, cloud specialist organizations confront the overwhelming test to meet the arranged SLA agreements, as far as dependability and convenient .execution, while accomplishing cost and energy efficiency. This paper proposes Shadow Replication, a novel fault-tolerance mechanism for Mobile cloud computing, which flawlessly address fault at scale, while limiting energy utilization and lessening its effect on cost. Energy conservation is achieved by creating dynamic cores rather than static cores. Cores are created by the application of cloudlets. In other words proportionate cores are created. Core failure metrics are considered to be memory capacity, energy and power consumption. In case any of the parameter exceeded threshold value, core is supposed to be faulted and progress is maintained within shadow which is maintained 1 per host. Progress of deteriorated Core is shifted to next core within other VM. In case all the core within VM deteriorated, VM migration is performed. Comparative study of techniques used to establish reliability within MCC is presented for future enhancements in terms of latency, downtime and migration time.

Mobile cloud computing, shadow Replication; fault tolerance; Energy Conservation

[1] B. Meroufel and G. Belalem, “Adaptive time-based coordinated checkpointing for cloud computing workflows,” Scalable Comput., vol. 15, no. 2, pp. 153–168, 2014.
[2] D. Kliazovich, P. Bouvry, and S. U. Khan, “GreenCloud : A Packet-level Simulator of Energy- aware Cloud Computing Data Centers,” J. Supercomput., vol. 62, no. 3, pp. 1263–1283, 2012.
[3] B. Alami Milani and N. Jafari Navimipour, “A comprehensive review of the data replication techniques in the cloud environments: Major trends and future directions,” J. Netw. Comput. Appl., vol. 64, pp. 229–238, 2016.
[4] R. Balamanigandan, “Analyzing massive machine data maintaining in a cloud computing,” vol. 23, no. 10, pp. 78–81, 2013.
[5] D. Singh, J. Singh, and A. Chhabra, “High availability of clouds: Failover strategies for cloud computing using integrated checkpointing algorithms,” Proc. - Int. Conf. Commun. Syst. Netw. Technol. CSNT 2012, pp. 698–703, 2012.
[6] Y. Zhang, Z. Zheng, and M. R. Lyu, “BFTCloud: A Byzantine Fault Tolerance framework for voluntary-resource cloud computing,” Proc. - 2011 IEEE 4th Int. Conf. Cloud Comput. CLOUD 2011, no. July 2011, pp. 444–451, 2011.
[7] P. K. Szwed, D. Marques, R. M. Buels, S. A. McKee, and M. Schulz, “SimSnap: Fast-forwarding via native execution and application-level checkpointing,” Proc. - Eighth Work. Interact. between Compil. Comput. Archit. INTERACT-8 2004, pp. 65–74, 2004.
[8] K. H. Kim and C. Subbaraman, “A modular implementation model of the Primary-Shadow TMO replication scheme and a testing approach using a real-time environment simulator,” Softw. Reliab. Eng. 1998. Proceedings. Ninth Int. Symp., pp. 247–256, 1998.
[9] K. H. Kim and C. Subbaraman, “An Integration of the Primary-Shadow TMO Replication (PSTR) Scheme with a Supervisor-based Network Surveillance Scheme and its Recovery Time Bound Analysis,” Proc. SRDS ’98 (IEEE CS 17th Symp. Reliab. Distrib. Syst. 1998, pp.168-176., pp. 168–176, 1998.
[10] “chain_declustering.pdf.” .
[11] M. R. Marty and M. D. Hill, “Virtual hierarchies to support server consolidation,” ACM SIGARCH Comput. Archit. News, vol. 35, no. 2, p. 46, 2007.
[12] R. T. Kaushik, “GreenHDFS : Towards An Energy-Conserving , Storage-Efficient , Hybrid Hadoop Compute Cluster,” HotPower, pp. 1–9, 2010.
[13] D. Kliazovich, P. Bouvry, and S. U. Khan, “DENS: Data center energy-efficient network-aware scheduling,” Cluster Comput., vol. 16, no. 1, pp. 65–75, 2013.
[14] Y. Lin and H. Shen, “EAFR: An Energy-Efficient Adaptive File Replication System in Data-Intensive Clusters,” IEEE Trans. Parallel Distrib. Syst., vol. 28, no. 4, pp. 1017–1030, 2017.
[15] J. Liu, F. Zhao, X. Liu, and W. He, “Challenges Towards Elastic Power Management in Internet Data Centers,” 2009 29th IEEE Int. Conf. Distrib. Comput. Syst. Work., pp. 65–72, 2009.
[16] B. Mills, T. Znati, R. Melhem, K. B. Ferreira, and R. E. Grant, “Energy consumption of resilience mechanisms in large scale systems,” Proc. - 2014 22nd Euromicro Int. Conf. Parallel, Distrib. Network-Based Process. PDP 2014, pp. 528–535, 2014.
[17] A. Odlyzko, “Data Networks are Lightly Utilized, and will Stay that Way,” Rev. Netw. Econ., vol. 2, no. 3, pp. 210–237, 2003.
[18] H.-I. Hsiao and D. J. DeWitt, “A performance study of three high availability data replication strategies,” [1991] Proc. First Int. Conf. Parallel Distrib. Inf. Syst., pp. 18–28.
[19] X. Cui, T. Znati, and R. Melhem, “Adaptive and Power-Aware Resilience for Extreme-scale Computing.”
[20] C. S. Shih and T. K. Trieu, “Shadow phone: Context aware device replication for disaster management,” Proc. - 2012 5th IEEE Int. Conf. Serv. Comput. Appl. SOCA 2012, 2012.
[21] K. Akherfi, M. Gerndt, and H. Harroud, “Mobile cloud computing for computation offloading : Issues and challenges,” Appl. Comput. Informatics, 2017.
[22] S. Choi, K. Chung, and H. Yu, “Fault tolerance and QoS scheduling using CAN in mobile social cloud computing,” 2013.
[23] N. M. Dhanya and G. Kousalya, “Adaptive and Secure Application Partitioning for Of fl oading in Mobile Cloud Computing,” vol. 1, pp. 45–53, 2015.
[24] L. A. Tawalbeh, “Mobile Cloud Computing Model and Big Data Analysis for Healthcare,” vol. 3536, no. c, 2016.
[25] C. You, K. Huang, and H. Chae, “Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer,” vol. 8716, no. c, pp. 1–14, 2016.
[26] J. P. D. Comput, B. Javadi, J. Abawajy, and R. Buyya, “Failure-aware resource provisioning for hybrid Cloud infrastructure,” J. Parallel Distrib. Comput., vol. 72, no. 10, pp. 1318–1331, 2012.
[27] S. Zhang, Z. Qian, Z. Luo, J. Wu, and S. Lu, “Burstiness-Aware Resource Reservation for Server Consolidation in Computing Clouds,” vol. 9219, no. c, pp. 1–14, 2015.
[28] F. Doelitzscher, A. Sulistio, C. Reich, H. Kuijs, and D. Wolf, “Private cloud for collaboration and e-Learning services : from IaaS to SaaS,” pp. 23–42, 2011.