Learning-based power prediction for geo-distributed Data Centers: weather parameter analysis

Abstract

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. The significant growth of the DC market has led to its rapid growth of power consumption as well as cost. By 2025, the DC market is predicted to account 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%.

Fuzzy Models for Short Term Power Forecasting in Palestine

“…Taheri et al (2020) [22] analyzed a distributed DC power patterns with their weather parameters to forecast the power consumption of each collaborating DC in a cloud. The obtained results show that the proposed prediction model reaches the accuracy of 87.2%.…”

Hybrid Deep Learning Framework for Privacy Preservation in Geo-Distributed Data Centre

“…Taheri et al [16] analysed the relation between GDDC power patterns using their weather parameter (according to distinct DC infrastructure and situations) and extracts a group of significant features. Next, the attained features are employed for providing an energy utilization predicting method which forecasts the power patterns of every cooperating DCs in a cloud.…”

“…At present, with big data and further data generation, it is increasingly important to process and store largescale data in real-time that has led to the placement of cloud computing [1]. The rapid development of the Data Centre (DC) markets leads to significant development of energy utilization and cost [2].…”

An efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis

“…Parameter selection has been revealed as one of the most significant challenges especially in big data analysis tasks [25][26][27][28]. Manual fine-tuning of the parameters is inevitably time consuming and cannot achieve high accuracy in capricious industrial applications.…”