Abstract
In this paper, we present a statistical model based VM placement approach for Cloud infrastructures. The model is motivated by the fact that more and more resource demanding applications are deployed in Cloud Infrastructures and in particular, communication data rate and latency bound applications are suffering from common placement algorithms. Based on a requirements analysis from the use cases of the CloudPerfect Project and the bwCloud production infrastructure, the need for a network-aware VM placement is motivated. The solution approach is inspired from the data source modelling applied for statistical multiplexer components in ATM networks. For each VM deployed in the Cloud Infrastructure, a probability for data rate distributions is derived from the collected data traces and the overall network resource consumption is estimated by overlaying the individual data rate probability distributions. The second part of the paper outlines a possible integration into a cloud infrastructure using OpenStack as an example. The paper concludes with a discussion on the stability of the model and initial results derived from collected data traces along with the future work.
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Notes
- 1.
While more appropriate wording would be data rate we use the established term bandwidth in this document.
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Acknowledgement
The research leading to these results has received funding from the EC’s Framework Programme HORIZON 2020 under grant agreement number 732258 (CloudPerfect). We thank our colleagues from Nuberisim who provided us valuable input that greatly assisted the research.
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Sarker, M., Wesner, S. (2019). Statistical Model Based Cloud Resource Management. In: Coppola, M., Carlini, E., D’Agostino, D., Altmann, J., Bañares, J. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2018. Lecture Notes in Computer Science(), vol 11113. Springer, Cham. https://doi.org/10.1007/978-3-030-13342-9_9
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