An effective Multi-Objective task scheduling algorithm using Min-Max normalization in cloud computing

Gajera, Vatsal; Shubham, .; Gupta, Rishabh; Jana, Prasanta K.

doi:10.1109/icatcct.2016.7912111

Cited by 74 publications

(23 citation statements)

References 12 publications

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“…Due to the complexity and diversity of the user's power usage behavior, the original load data may not totally or approximately satisfy the Gaussian distribution, the results of commonly used z-scores normalization will be poor [22]. Therefore, the unity-based normalization (also known as min-max normalization) is utilized in this paper [23]. This method ensures that all data is linearly mapped into the interval [0], [1].…”

Section: Basic Methodology a Data Normalizationmentioning

confidence: 99%

A Weekly Load Data Mining Approach Based on Hidden Markov Model

Lü

Liu

et al. 2019

IEEE Access

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With the development of advanced metering infrastructure, massive smart meter readings are generated and stored in smart grids, which makes it possible for detecting of tremendous social value embedded in load data. The majority of the existing load data mining works are performed on the daily time scale without adequate consideration of load information between the days. To better describe the power consumption characteristics of users, a data mining approach based on the weekly load curves is proposed in this paper. First, the piecewise aggregate approximation technique is utilized to reduce the dimensions of the raw weekly load data. Then, a Davies-Bouldin index-based adaptive k-means algorithm is proposed to cluster the studied users into several groups. Finally, a hidden Markov model describing the probabilistic transitions of different load levels is established for each cluster to extract the representative dynamic weekly load features. A feasible tool based on dynamic characteristics of load patterns is invented to evaluate the short-term load forecasting methods, which realizes the pre-check for the forecasting results without future real measurements in the forecasting horizon. Case studies on a real dataset demonstrate that the proposed method is capable of extracting weekly load characteristics of users. INDEX TERMS Weekly load profiles, dimension reduction, clustering, hidden Markov model evaluation. I. INTRODUCTION A. BACKGROUND With the development of smart grids, smart meters, the basic terminal equipment of Advanced Metering Infrastructure (AMI), has gained increasing popularity worldwide. For instance, in the US, the quantity of smart meters installed has reached 70 million by the end of 2016 [1]; while in China, more than 500 million smart meters will be installed during the 13th Five-Year Plan period (2016-2020) [1]. Consequently, massive load data is generated and stored. With a temporal measurement of 15 minutes, the annual amount of smart meter readings for China reaches 117TB. Except for the traditional electricity billing, hidden value of the massive smart meter readings is detected by a series of data mining approaches. Typical load data mining procedure includes steps of data cleaning, compression, clustering, forecasting and so on [2]. The associate editor coordinating the review of this manuscript and approving it for publication was Qilian Liang.

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Section: Basic Methodology a Data Normalizationmentioning

confidence: 99%

A Weekly Load Data Mining Approach Based on Hidden Markov Model

Lü

Liu

et al. 2019

IEEE Access

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“…The numerical values assigned by different metrics cannot be compared directly. To evaluate the causes from a comprehensive perspective, the numerical values are normalized first with the following Min-Max normalization 36…”

Section: A Comprehensive Importance Order Analysis Methodsmentioning

confidence: 99%

An importance order analysis method for causes of railway signaling system hazards based on complex networks

Liu

Zheng

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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The importance order of causes of a railway signaling system hazard is important for the arrangement of control measures for eliminating or controlling the system hazard. In order to obtain a reasonable importance order, a novel importance order analysis method based on complex networks is proposed in this article. In this article, to characterize the causes from a topological perspective, a comprehensive topology characteristic is proposed. It is built on the top of three typical topology characteristics with the weighted Euclidean distance. Then, an influence coefficient matrix is proposed to measure the causal influence between causes. Based on the proposed topology characteristic and matrix, an algorithm is designed to identify the importance order of causes. To verify the effectiveness of the proposed method, a case study on a Chinese high-speed railway signaling system hazard is performed. The results show that the proposed method is feasible for the arrangement of control measures to eliminate or control system hazards and performs better than the traditional complex network-based methods in terms of avoiding conflicting and rough results.

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“…Different kind of data normalization techniques are available, including min-max, median normalization, and Z-score decimal scaling. In this paper, we used the most popular normalization technique, min-max normalization [45].…”

Section: Data Normalizationmentioning

confidence: 99%

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

Awan

Minerva

Crespi

2020

Sensors

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Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the best path to their destinations. Accurate traffic forecasting is a basic requirement for traffic management. We propose a traffic forecasting approach that utilizes air pollution and atmospheric parameters. Air pollution levels are often associated with traffic intensity, and much work is already available in which air pollution has been predicted using road traffic. However, to the best of our knowledge, an attempt to improve forecasting road traffic using air pollution and atmospheric parameters is not yet available in the literature. In our preliminary experiments, we found out the relation between traffic intensity, air pollution, and atmospheric parameters. Therefore, we believe that addition of air pollutants and atmospheric parameters can improve the traffic forecasting. Our method uses air pollution gases, including C O , N O , N O 2 , N O x , and O 3 . We chose these gases because they are associated with road traffic. Some atmospheric parameters, including pressure, temperature, wind direction, and wind speed have also been considered, as these parameters can play an important role in the dispersion of the above-mentioned gases. Data related to traffic flow, air pollution, and the atmosphere were collected from the open data portal of Madrid, Spain. The long short-term memory (LSTM) recurrent neural network (RNN) was used in this paper to perform traffic forecasting.

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An effective Multi-Objective task scheduling algorithm using Min-Max normalization in cloud computing

Cited by 74 publications

References 12 publications

A Weekly Load Data Mining Approach Based on Hidden Markov Model

A Weekly Load Data Mining Approach Based on Hidden Markov Model

An importance order analysis method for causes of railway signaling system hazards based on complex networks

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

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