Computing server power modeling in a data center: survey,taxonomy and performance evaluation

Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper

Machine learning for Internet of Things data analysis: A survey

Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this Big Data is the key to developing smart IoT applications. This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case. The key contribution of this study is presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for a more detailed exploration.Comment: Digital Communications and Networks (2017

Cascaded clustering analysis of electricity load profile based on smart metering data

13th International Conference on Electrical and Electronics Engineering, ELECO 2021Virtual, Bursa25 November 2021 through 27 November 2021 Code 176537In the operation of deregulated power systems, consumption data is used effectively by system operators. Thanks to the developing measurement and communication technologies, measurement data with high temporal resolution can be obtained from many points within the power systems. Considering the number of consumers connected to power systems, the data in question is a big data. To deal with such a large amount data clustering analyzes are effectively used to identify consumers with similar behaviors in consumption data and to represent consumers with similar behaviors with a single load profile. Success of the clustering studies is related with the compatibility of the data to the selected algorithm and the appropriateness of the adopted approaches to the application of the algorithm to the data. In this study, a cascade clustering algorithm created with the k-medoids algorithm is proposed

Big Data Analytics for Network Level Short-Term Travel Time Prediction with Hierarchical LSTM and Attention

The travel time data collected from widespread traffic monitoring sensors necessitate big data analytic tools for querying, visualization, and identifying meaningful traffic patterns. This paper utilizes a large-scale travel time dataset from Caltrans Performance Measurement System (PeMS) system that is an overflow for traditional data processing and modeling tools. To overcome the challenges of the massive amount of data, the big data analytic engines Apache Spark and Apache MXNet are applied for data wrangling and modeling. Seasonality and autocorrelation were performed to explore and visualize the trend of time-varying data. Inspired by the success of the hierarchical architecture for many Artificial Intelligent (AI) tasks, we consolidate the cell and hidden states passed from low-level to the high-level LSTM with an attention pooling similar to how the human perception system operates. The designed hierarchical LSTM model can consider the dependencies at different time scales to capture the spatial-temporal correlations of network-level travel time. Another self-attention module is then devised to connect LSTM extracted features to the fully connected layers, predicting travel time for all corridors instead of a single link/route. The comparison results show that the Hierarchical LSTM with Attention (HierLSTMat) model gives the best prediction results at 30-minute and 45-min horizons and can successfully forecast unusual congestion. The efficiency gained from big data analytic tools was evaluated by comparing them with popular data science and deep learning frameworks

Image processing of alos palsar satellite data, small unmanned aerial vehicle (UAV), and field measurement of land deformation

Pekanbaru, Indonesia is connected by four big bridges, Siak Bridge; I, II, III and IV. The quality of the Siak bridges deteriorated seriously at this time. Geological mapping for the land subsidence potency was conducted using small Unmanned Aerial Vehicle (UAV) in the Siak Bridge areas. The study of the Siak bridges are supported by the Differential Interferometric Synthetic Aperture Radar (DInSAR) analysis using ALOS PALSAR satellite data, and the deflection observation that occurs in Siak III Bridge was observed by field measurement. The results of 3D model analysis showed that there is no negative land deformation. DInSAR analysis shows the amount of positive deformation of Siak I is 81 cm, Siak II is 48 cm, Siak III is 89 cm, and Siak IV is 92. Deflection on Siak III Bridge was detected at around 25-26 cm. These models could be used as a new way of measuring the bridge deformation on a big scale

SIDeKa: The Role of Information Technology for Knowledge Creation

The advancement of big data analytics is paving the way for knowledge creation based on very huge and unstructured data. Currently, information is scattered and growth tremendously, containing many information but difficult to be interpreted. Consequently, traditional approaches are no longer suitable for unstructured data but very rich in information. This situation is different from the role of previous information technology in which information is based on structured data, stored in the local storage, and in more advanced form, information can be retrieved through internet. Meanwhile, in Indonesia data are collected by many institutions with different measurement standard. The nature of the data collection is top-down, carried out by survey which is expensive yet unreliable and stored exclusively by respective institution. SIDeKa (Sistem Informasi Desa dan Kawasan/Village and Regional Information System), which are connected nationally, is proposed as a system of data collection in the village level and prepared by local people. Using SIDeKa, data reliability and readiness can be improved at the local level. The goals of the SIDeKa is not only local people have information in their hand such as poverty level, production, commodity price, the area of cultivated land, and the outbreak of diseases in their village, but also they have information from the neighboring villages or event at the national level. For government, data reliability will improve the policy effectiveness. This paper discusses the implementation and role of SIDeKa for knowledge creation in the village level, especially for the agricultural activities which has been initiated in 2015.Keywords: big data analytics; SIDeKa;  unstructured data