Prediction of cloud data center networks loads using stochastic and neural models
Abstract
The increasing demand for cloud computing resources has led to a commensurate increase in the operating power consumption of the systems that comprise the cloud. In this paper, we introduce a novel framework combining load demand prediction and stochastic state transition models. We claim that our model will lead to optimal cloud resource allocation by minimizing energy consumed while maintaining required performance levels. We characterize the ability of neural network and auto-regressive linear prediction algorithms to forecast loads in cloud data center applications. In this paper, the performance of our models against two sets of data at multiple look-ahead times is also presented.
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Various challenges still prevail for different networks, including cellular networks, wireless networks, and ad hoc networks, despite widespread application of ML for load prediction and resource management in cloud data centers.
“Despite the widespread application of ML for load prediction and resource management in cloud data centers [367], various challenges still prevail for different networks, including cellular networks, wireless networks and ad hoc networks.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [367]
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- Authors
- Prevost, J. J., Nagothu, K., Kelley, B., & Jamshidi, M.
- Affiliations
- The University of Texas at San Antonio (US)
- Venue
- 2011 6th International Conference on System of Systems Engineering
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 276-281
- OpenAlex
- W2154384045
- Cited by
- 169
- References
- 11
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 5 · 2022: 8 · 2021: 24 · 2020: 23 · 2019: 16 · 2018: 18 · 2017: 11 · 2016: 14 · 2015: 12 · 2014: 14 · 2013: 11 · 2012: 3
- FWCI
- 6.73
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on System of Systems Engineering (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Keywords
- cloud data centers, load demand prediction, artificial neural networks, energy consumption, cloud resource allocation, power consumption
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