Abstract
Nowadays, the fast rate of technological advances, such as cloud computing, has led to the rapid growth of the Data Center (DC) market as well as their power consumption. Therefore, DC power management has become increasingly important. While power forecasting can greatly help DC power management and reduce energy consumption and cost. Power forecasting predicts the potential energy failures or sudden fluctuations in power intake from utility grid. However, it is hard especially when variable renewable energies (RE) as well as free cooling such as air economizers are also used. Geo-distributed DCs face an even harder issue: since in addition to local conditions, the overall status of the entire system of collaborating DCs should also be considered. The conventional approach to forecast power consumption in such complicated cases is to construct a closed form formula for power. This is a tedious task that not only needs expert knowledge of how each single cooling or RE system works, but also needs to be done individually for each DC and repeated all over again for each new DC or change of equipment. One alternative is to use machine learning so as to learn over time how the system consumes power in varying conditions of weather, workload, and internal structure in multiple geo-distributed locations. However, due to the wide range of effective features as well as trade-off between the accuracy and processing overhead, one important issue is to obtain an optimal set of more influential features. In this study, we analyze the correlation among geo-distributed DC power patterns with their weather parameters (based on different DC situations and infrastructure) and extract a set of influential features. Afterward, we apply the obtained features to provide a power consumption forecasting model that predict the power pattern of each collaborating DC in a cloud. Our experimental results show that the proposed prediction model for geo-distributed DCs reaches the accuracy of 87.2%.
Highlights
Nowadays, in the age of big data and more data generation, there is a growing need to store and process large-scale data in real-time which has led to the deployment of cloud computing
We considered a model with 9 neurons in the input layer, 40 neurons in the hidden layer and 4 neurons in the output layer
Best‐fitted linear regression (LR) model with optimal set of features we evaluate the reliability of the feature extraction approach, as we considered in this paper
Summary
In the age of big data and more data generation, there is a growing need to store and process large-scale data in real-time which has led to the deployment of cloud computing. Considerations of globalization, security, and disaster recovery encourage organizations to distribute their DCs over a long geographically distance and across different regions, clearly near to cloud users. These geo-distributed DCs, which replaced the centralized one, offer solutions to deal with the high velocity and high volume of big data generated from geographically dispersed sources. Power usage effectiveness (PUE) is widely used by load balancing approaches to distribute the incoming workload among geo-distributed DCs. PUE is the ratio of the DC total power vs the amount delivered to the computing equipment.
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