What is it about?
In research on energy efficiency in cloud data centers, linear models which are based on the lowest-and highest-power data points (referred to here as the “Power Endpoints Model” - PEM) and the simple linear regression (SLR) model are the most used. In this paper, a unified classification and evaluation for these Linear power models are developed and evaluated, under unified setup, benchmarking applications, and error formula with the main goal being to achieve an objective comparison. A new power model is proposed, named Locally Corrected Multiple Linear Regression (LC-MLR), in order to increase prediction accuracy. A simulation framework for a cloud energy-aware scheduler is introduced.
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Why is it important?
We propose a simulation framework for energy-efficiency, which combines the Energy-Aware Task Scheduling on Cloud Virtual Machines (EATSVM) with the Locally Corrected Multiple Linear Regression (LC-MLR). It facilitates performance measurement for cloud data centers. The scheduler (EATSVM-LC-MLR) increases energy efficiency without degrading the qualities of service of the system.
Perspectives
It is a great pleasure working on this paper with Prof. Eyad Abed with whom I have long-standing collaboration. This article instigates research groups on energy for cloud computing contacting me for collaboration on energy for cloud-based big data systems.
Prof. Dr. Leila Ismail
United Arab Emirates University
Read the Original
This page is a summary of: Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and Evaluation, IEEE Access, January 2019, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/access.2019.2956881.
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Resources
Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and Evaluation
Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and Evaluation Leila Ismail and Eyad H. Abed
Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and Evaluation
L. Ismail and E. H. Abed, "Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and Evaluation," in IEEE Access, vol. 7, pp. 175003-175019, 2019, doi: 10.1109/ACCESS.2019.2956881.
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