Cloud Simulation Model Based on Large Numbers Law for Evaluating Fault Tolerance Approaches

Cloud computing is an emerging paradigm that consists of hosting and delivering computing services across the web. The availability is one of the security features such as integrity and confidentiality. Certainly endorsing high availability by the improvement of fault tolerance techniques is one of the major concerns of the cloud. Elsewhere we cannot afford to directly evaluate new approaches for cost reason. For this reason we introduce in this paper a probabilistic model for simulation based on the principle of “Large Numbers Law”. The idea is to simulate a scenarios of cloud virtual environment in which faults can occur in a random way following failure occurrence probabilities. The global unavailability measured is faithful to unavailability average known of Cloud providers. The model allows live virtual machine migration in order to evaluate proactive fault tolerance approaches based on preemptive migration.

[1]  Harshpreet Singh,et al.  Review on Fault Tolerance Techniques in Cloud Computing , 2015 .

[2]  Alexandru Iosup,et al.  Benchmarking in the Cloud: What It Should, Can, and Cannot Be , 2012, TPCTC.

[3]  Ravishankar K. Iyer,et al.  Toward a high availability cloud: Techniques and challenges , 2012, IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN 2012).

[4]  Soon Myoung Chung,et al.  A Survey on the Security of Hypervisors in Cloud Computing , 2015, 2015 IEEE 35th International Conference on Distributed Computing Systems Workshops.

[5]  Kartik Gopalan,et al.  Post-copy based live virtual machine migration using adaptive pre-paging and dynamic self-ballooning , 2009, VEE '09.

[6]  Carlos A. Varela,et al.  Impact of Cloud Computing Virtualization Strategies on Workloads' Performance , 2011, 2011 Fourth IEEE International Conference on Utility and Cloud Computing.

[7]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[8]  Calton Pu,et al.  Performance Overhead among Three Hypervisors: An Experimental Study Using Hadoop Benchmarks , 2013, 2013 IEEE International Congress on Big Data.

[9]  Nagarajan Kandasamy,et al.  Power and Performance Management of Virtualized Computing Environments Via Lookahead Control , 2008, ICAC.

[10]  Andrew Warfield,et al.  Live migration of virtual machines , 2005, NSDI.

[11]  David R. Kaeli,et al.  Quantifying load imbalance on virtualized enterprise servers , 2010, WOSP/SIPEW '10.

[12]  S. Sahni,et al.  A Hybrid Approach to Live Migration of Virtual Machines , 2012, 2012 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM).

[13]  Mohamed Batouche,et al.  Probabilistic model for evaluating a proactive fault tolerance approach in the cloud , 2015, 2015 IEEE International Conference on Service Operations And Logistics, And Informatics (SOLI).

[14]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[15]  Ruby B. Lee,et al.  Characterizing hypervisor vulnerabilities in cloud computing servers , 2013, Cloud Computing '13.

[16]  P. G. J. Leelipushpam,et al.  Live VM migration techniques in cloud environment — A survey , 2013, 2013 IEEE CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES.

[17]  Mahesh U. Shankarwar,et al.  Security and Privacy in Cloud Computing: A Survey , 2014, FICTA.