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The evolution of ICT systems in the way data is accessed and used is very fast nowadays. Cloud computing is an innovative way of using and providing computing resources to businesses and individuals and it has gained a faster popularity in the last years. In this context, the user’s expectations are increasing and cloud providers are facing huge challenges. One of these challenges is fault tolerance and both researchers and companies have focused on finding and developing strong fault tolerance models. To validate these models, cloud simulation tools are used as an easy, flexible and fast solution. This paper proposes a Fault Injector Module for CloudSim tool (FIM-SIM) for helping the cloud developers to test and validate their infrastructure. FIM-SIM follows the event- driven model and inserts faults in CloudSim based on statistical distributions. The authors have tested and validated it by conducting several experiments designed to highlight the statistical distribution influence on the failures generated and to observe the CloudSim behavior in its current state and implementation.
Rocznik
Tom
Strony
14--23
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
autor
- Faculty of Automatic Control and Computers, Computer Science Department, University Politechnica of Bucharest, Bucharest, Romania
autor
- Faculty of Automatic Control and Computers, Computer Science Department, University Politechnica of Bucharest, Bucharest, Romania
autor
- Faculty of Automatic Control and Computers, Computer Science Department, University Politechnica of Bucharest, Bucharest, Romania
autor
- Faculty of Automatic Control and Computers, Computer Science Department, University Politechnica of Bucharest, Bucharest, Romania
Bibliografia
- [1] A. Hameed et al., “A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems”, Computing, 2014 (in Press).
- [2] R. Jhawar and V. Piuri, “Fault tolerance management in Iaas clouds”, in Proc. IEEE 1st AESS Eur. Conf. Satell. Telecommun. ESTEL 2012, Rome, Italy, 2012, pp. 1–6.
- [3] M. Nastase, C. Dobre, F. Pop, and V. Cristea, “Fault tolerance using a front-end service for large scale distributed systems” in Proc. 11th Int. Symp. Symb. Numeric Algorithms for Scient. Comput. SYNASC 2009, Timisoara, Romania, 2009, pp. 229–236.
- [4] Z. Zheng, T. C. Zhou, M. R. Lyu, and I. King, “Component ranking for fault-tolerant cloud applications”, IEEE Trans. Serv. Comput., vol. 5, no. 4, pp. 540–550, 2012.
- [5] J. Kolodziej, H. Gonzalez-Velez, and L. Wang, “Advances in dataintensive modeling and simulation”, Future Gener. Comp. Syst., 2014 (in Press).
- [6] C. Dobre, F. Pop, and V. Cristea, “A fault-tolerant approach to storing objects in distributed systems”, in Proc. Int. Conf. on P2P, Parall., Grid, Cloud and Internet Comput. 3PGCIC 2010, Fukuoka, Japan, 2010, pp. 1–8.
- [7] P. Das and P. M. Khilar, “VFT: A virtualization and fault tolerance approach for cloud computing”, in Proc. IEEE Conf. Inform. Commun. Technol. ICT 2013, Jeju Island, South Korea, 2013, pp. 473–478.
- [8] S. Malik and F. Huet, “Adaptive fault tolerance in real time cloud computing”, in Proc. IEEE World Congr. Serv. SERVICES 2011, Washington, DC, USA, 2011, pp. 280–287.
- [9] Y. He et al., “A simulation cloud monitoring framework and its evaluation model”, Simul. Modell. Pract. and Theory, vol. 38, pp. 20–37, 2013.
- [10] S. Vilkomir, “Cloud testing: A state-of-the-art review”, Inform. & Secur.: An Int. J., vol. 28, no. 2, pp. 213–222, 2012.
- [11] A. Boteanu, C. Dobre, F. Pop, and V. Cristea, “Simulator for fault tolerance in large scale distributed systems”, in Proc. IEEE 6th Int. Conf. Intell. Comp. Commun. and Process. ICCP 2010, Cluj-Napoca, Romania, 2010, pp. 443–450.
- [12] A. Costan, C. Dobre, F. Pop, C. Leordeanu, and V. Cristea, “A fault tolerance approach for distributed systems using monitoring based replication”, in Proc. IEEE 6th Int. Conf. Intell. Comp. Commun. and Process. ICCP 2010, Cluj-Napoca, Romania, 2010, pp. 451–458.
- [13] D. Ford et al., “Availability in globally distributed storage systems”, in Proc. 9th USENIX Conf. Operat. Sys. Design and Implemen. OSDI’10, Berkeley, CA, USA, 2010, pp. 1–7. USENIX Association.
- [14] Y. Zhang, Z. Zheng, and M. R. Lyu, “BFTCloud: A byzantine fault tolerance framework for voluntary-resource cloud computing”, in Proc. IEEE 4th Int. Conf. Cloud Comput. CLOUD ’11, Washington, DC, USA, 2011, pp. 444–451.
- [15] F. Cappello, “Fault tolerance in petascale/ exascale systems: current knowledge, challenges and research opportunities”, Int. J. High Perform. Comput. Appl., vol. 23, no. 3, pp. 212–226, 2009.
- [16] A. N´u˜nez et al., “A flexible and scalable cloud infrastructure simulator”, J. Grid Comput., vol. 10, no. 1, pp. 185–209, 2012.
- [17] L. Liu et al., “Greencloud: A new architecture for green data center”, in Proc. 6th Int. Conf. Industry Session on Autonomic Comput. and Communi. Industry Session ICAC-INDST ’09, Barcelona, Spain, 2009, pp. 29–38.
- [18] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, “Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms”, Softw. Pract. Exper., vol. 41, no. 1, pp. 23–50, 2011.
- [19] K. J. Lang, “Practical algorithms for generating a random ordering of the elements of a weighted set”, Theor. Comp. Sys., vol. 54, no. 4, pp. 659–688, 2014.
- [20] A. M. Razali and A. A. Al-Wakeel, “Mixture weibull distributions for fitting failure times data”, Appl. Math. Comput., vol. 219, no. 24, pp. 11358–11364, 2013.
- [21] G. Shmueli, T. P. Minka, J. B. Kadane, S. Borle, and P. Boatwright, “A useful distribution for fitting discrete data: revival of the Conway-Maxwell-Poisson distribution”, J. Royal Statist. Soc.: Series C (Applied Statistics), vol. 54, no. 1, pp. 127–142, 2005.
- [22] M. E. J. Newman, “Power laws, pareto distributions and Zipf’s law”, Contemporary Phys., vol. 46, no. 5, pp. 323–351, 2005.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-b656c17f-c18e-41b2-991f-f960ad6935b3