Abstract
We present a probabilistic and a possibilistic model for assessing the risk of a service level agreement for a computing task in a cluster/grid environment. These models can also be applied to cloud computing. Using the predictive probabilistic approach we develop a framework for resource management in grid computing, and by introducing an upper limit for the number of failures we approximate the probability that a particular computing task is successful. In the predictive possibility model we estimate the possibility distribution of the future number of node failures by a fuzzy nonparametric regression technique. Then the resource provider can use the probabilistic or the possibilistic model to get alternative risk assessments.
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References
Bernardo JM, Smith AFM (1994) Bayesian theory. Wiley, Chichester
Brandt JM, Gentile AC, Marzouk YM, Pébay PP (2005) Meaningful statistical analysis of large computational clusters. In: Proceedings of the 2005 IEEE international conference on cluster computing, Burlington, MA, pp 1–2. doi:10.1109/CLUSTR.2005.347090
Carlsson C, Fullér R (2002) Fuzzy reasoning in decision making and optimization. Springer, Berlin
Carlsson C, Weissman O (2009) Advanced risk assessment, D4.1. The AssessGrid Project, IST-2005-031772, Berlin
Carlsson C, Fullér R, Mezei J (2009) A lower limit for the probability of success of computing tasks in a grid. In: Proceedings of the tenth international symposium of Hungarian researchers on computational intelligence and informatics (CINTI 2009) Budapest, Hungary, pp 717–722
Czajkowski K, Foster I, Kesselman C (2005) Agreement-based resource management. Proc IEEE 93(3):631–643. doi:10.1109/JPROC.2004.842773
Diamond P (1988) Fuzzy least squares. Inf Sci 46(3):141–157. doi:10.1016/0020-0255(88)90047-3
Iosup A, Jan M, Sonmez OO, Epema, DHJ (2007) On the dynamic resource availability in grids. In: Proceedings of the 8th IEEE/ACM international conference on grid computing (GRID 2007), pp 26–33. doi:10.1109/GRID.2007.4354112
Lintner J (1965) The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Rev Econ Stat 47(1):13–37
Magana E, Serrat J (2007) Distributed and heuristic policy-based resource management system for large-scale grids. Lect Notes Comput Sci 4543:184–187
Marston S, Li Z, Bandyopadhyay S, Zhang J, Ghalsasi A (2011) Cloud computing—the business perspective. Decis Support Syst 51(1):176–189
Rimal BH, Jukan A, Katsaros D, Goelven Y (2011) Architectural requirements for cloud computing systems: an enterprise cloud approach. J Grid Comput 9(1):3–26
Robert CP, Casella G (2005) Monte Carlo statistical methods. Springer, New York
Schroeder B, Gibson GA (2006) A large-scale study of failures in high-performance computing systems. In: DSN2006 conference proceedings, Philadelphia, pp 249–258. doi:10.1109/DSN.2006.5
Sklar A (1959) Fonctions de Répartition àn Dimensions et Leurs Marges. Paris, France Publ. Inst. Statist. Univ. Paris 1959(8):229–231
Wang N, Zhang W-X, Mei C-L (2007) Fuzzy nonparametric regression based on local linear smoothing technique. Inf Sci 177(18):3882–3900. doi:10.1016/j.ins.2007.03.002
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353. doi:10.1016/S0019-9958(65)90241-X
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Carlsson, C., Fullér, R. Probabilistic versus possibilistic risk assessment models for optimal service level agreements in grid computing. Inf Syst E-Bus Manage 11, 13–28 (2013). https://doi.org/10.1007/s10257-011-0187-z
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DOI: https://doi.org/10.1007/s10257-011-0187-z