Abstract
In this paper, we consider a hierarchical cloud topology and address the problem of optimally placing a group of logical entities according to some policy constraining the allocation of the members of the group at the various levels of the hierarchy. We introduce a simple group hierarchical placement policy, parametrized by lower and upper bounds, that is generic enough to include several existing policies such as collocation and anti-collocation, among others, as special cases. We present an efficient placement algorithm for this group hierarchical placement policy and demonstrate a six-fold speed improvement over existing algorithms. In some cases, there exists a degree of freedom which we exploit to quantitatively obtain a placement solution, given the amount of group spreading preferred by the user. We demonstrate the quality and scalability of the algorithm using numerical examples.
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The number of samples generated in each iteration is 20 and a fraction, 0.1, of those is used as important samples. The stopping criterion is a relative improvement in the objective function of less than 0.001, or a maximum number of iterations of 10.
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Tantawi, A.N. (2015). Quantitative Placement of Services in Hierarchical Clouds. In: Campos, J., Haverkort, B. (eds) Quantitative Evaluation of Systems. QEST 2015. Lecture Notes in Computer Science(), vol 9259. Springer, Cham. https://doi.org/10.1007/978-3-319-22264-6_13
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DOI: https://doi.org/10.1007/978-3-319-22264-6_13
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