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2018 | OriginalPaper | Buchkapitel

Risk Aware Stochastic Placement of Cloud Services: The Multiple Data Center Case

verfasst von : Galia Shabtai, Danny Raz, Yuval Shavitt

Erschienen in: Algorithmic Aspects of Cloud Computing

Verlag: Springer International Publishing

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Abstract

Allocating the right amount of resources to each service in any of the data centers in a cloud environment is a very difficult task. In a previous work we considered the case where only two data centers are available and proposed a stochastic based placement algorithm to find a solution that minimizes the expected total cost of ownership. This approximation algorithm seems to work well for a very large family of overflow cost functions, which contains three functions that describe the most common practical situations. In this paper we generalize this work for arbitrary number of data centers and develop a generalized mechanism to assign services to data centers based on the available resources in each data center and the distribution of the demand for each service. We further show, using simulations based on synthetic data that the scheme performs very well on different service workloads.

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Vorheriges Kapitel Improving Rule-Based Elasticity Control by Adapting the Sensitivity of the Auto-Scaling Decision Timeframe

Nächstes Kapitel Automatic Scaling of Resources in a Storm Topology

Nur mit Berechtigung zugänglich

The best integral solution on the bottom sorted path is not necessarily the optimal integral solution. However, in any reasonable situation, its cost is close to the optimal cost, and even to the optimal fractional solution. To see that notice that by [1] the optimal fractional solution is on the bottom sorted path, and if there is no dominant service (see [1] for a rigorous definition) there must be an integral point on the bottom sorted path that is close to the optimal fractional solution, and therefore by continuity (if indeed the cost function is continuous) the cost of the best integral solution on the bottom sorted path is close to the optimal fractional cost. A rigorous analysis for two bins and the cost functions SP-MED and SP-MWOP is presented in [1]. The k bin case is a straightforward extension of these results.

Where a is the portion of the total mean allocated to the first bin, i.e., \(a = \frac{\mu _1}{\mu }\).

Note that we could probably improve results by moving each stick to the closest integral point, which is either left or right of it. However, we think that this improvement is minor when n gets larger.

Shabtai, G., Raz, D., Shavitt, Y.: Risk aware stochastic placement of cloud services: the case of two data centers. In: ALGOCLOUD (2017)

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Nikolova, E., Kelner, J.A., Brand, M., Mitzenmacher, M.: Stochastic shortest paths via quasi-convex maximization. In: Azar, Y., Erlebach, T. (eds.) ESA 2006. LNCS, vol. 4168, pp. 552–563. Springer, Heidelberg (2006). https://doi.org/10.1007/11841036_50 CrossRef

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Titel: Risk Aware Stochastic Placement of Cloud Services: The Multiple Data Center Case
verfasst von: Galia Shabtai
Danny Raz
Yuval Shavitt
Verlag: Springer International Publishing
Buch: Algorithmic Aspects of Cloud Computing
Print ISBN: 978-3-319-74874-0

Electronic ISBN: 978-3-319-74875-7

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-74875-7_9

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