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Warehouse-Scale Computing in the Post-Moore Era Read chapter Read first chapter

2018 | OriginalPaper | Chapter

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

Authors : Galia Shabtai, Danny Raz, Yuval Shavitt

Published in: Algorithmic Aspects of Cloud Computing

Publisher: 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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previous chapter Improving Rule-Based Elasticity Control by Adapting the Sensitivity of the Auto-Scaling Decision Timeframe
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Appendix
Available only for authorised users
Footnotes
1
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.
 
2
Where a is the portion of the total mean allocated to the first bin, i.e., \(a = \frac{\mu _1}{\mu }\).
 
3
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.
 
Literature
1.
Shabtai, G., Raz, D., Shavitt, Y.: Risk aware stochastic placement of cloud services: the case of two data centers. In: ALGOCLOUD (2017)
2.
3.
Goel, A., Indyk, P.: Stochastic load balancing and related problems. In: IEEE FOCS 1999, pp. 579–586 (1999)
4.
Wang, M., Meng, X., Zhang, L.: Consolidating virtual machines with dynamic bandwidth demand in data centers. In: IEEE INFOCOM 2011, pp. 71–75 (2011)
5.
Breitgand, D., Epstein, A.: Improving consolidation of virtual machines with risk-aware bandwidth oversubscription in compute clouds. In: IEEE INFOCOM 2012, pp. 2861–2865 (2012)
6.
7.
Nikolova, E.: Approximation algorithms for offline risk-averse combinatorial optimization. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, pp. 338–351 (2010)
Metadata
Title
Risk Aware Stochastic Placement of Cloud Services: The Multiple Data Center Case
Authors
Galia Shabtai
Danny Raz
Yuval Shavitt
Copyright Year
2018
Book
Algorithmic Aspects of Cloud Computing

Print ISBN: 978-3-319-74874-0

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

Copyright Year: 2018

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

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