A smoothing based task scheduling algorithm for heterogeneous multi-cloud environment

Panda, Sanjaya Kumar; Nag, Subhrajit; Jana, Prasanta K.

doi:10.1109/pdgc.2014.7030716

Cited by 24 publications

(5 citation statements)

References 21 publications

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“…Before the establishment of a multiple regression model, the data should be normalized. The normalization method used in this study was linear normalization (Max-Min) [33][34][35][36].…”

Section: Multiple Linear Regression Analysismentioning

confidence: 99%

Analysis of Spatial and Temporal Variation in Water Coverage in the Sub-Lakes of Poyang Lake Based on Multi-Source Remote Sensing

Wang

Xie

et al. 2023

Remote Sensing

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As the largest freshwater lake in China, Poyang Lake is an internationally important wetland and the largest migratory bird habitat in Asia. Many sub-lakes distributed in the lake basin are seasonal lakes, which have a significant impact on hydro-ecological processes and are susceptible to various changes. In this study, using multi-source remote sensing data, a continuous time-series construction method of water coverage suitable in Poyang Lake was developed. That method combined the downscaling of the MNDWI (modified normalized difference water index) with the ISODATA (iterative self-organizing data analysis technique algorithm), and its accuracy can be up to 97% in the months when Landsat 8 is available or 87% when it is unavailable. Based on that method, the increasing variation in water coverage was observed in the sub-lakes of Poyang Lake during 2013–2020 to be within a range of 200–690 km2 normally. The center of the sub-lakes always remained inundated (>80% inundation frequency), while the surrounding areas were probably kept dry for seven months (except for June to September). The dominant influencing factors of water coverage variations were different in different hydrological periods (wet season and dry–wet season: discharge; dry season: temperature and wind speed; wet–dry season: temperature and precipitation). In addition, “returning farmland to lakes” affected the increase in the water area in the sub-lakes. This study is helpful for the management of water resources and the protection of migratory birds in the Poyang Lake region.

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“…Before the establishment of a multiple regression model, the data should be normalized. The normalization method used in this study was linear normalization (Max-Min) [33][34][35][36].…”

Section: Multiple Linear Regression Analysismentioning

confidence: 99%

Analysis of Spatial and Temporal Variation in Water Coverage in the Sub-Lakes of Poyang Lake Based on Multi-Source Remote Sensing

Wang

Xie

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Even in the case a linear output function, is used, it is also stated that normalizing the inputs as well as the outputs has many advantages. Some of them avoid computational problems, so all data input must be normalized from 0 to 1 by using Max-Min normalization equation [19][20][21][22][23].…”

Section: B1 the Preprocessing Of Datamentioning

confidence: 99%

Utilizing Neural Networks for Stocks Prices Prediction in Stocks Markets

Mahedy¹,

Abdelsalam²,

Mohamed³

et al. 2020

IJITCS

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The neural networks, AI applications, are effective prediction methods. Therefore, in the current research a prediction system was proposed using these neural networks. It studied the technical share indices, viewing price not only as a function of time, but also as a function depending on several indices among which were the opening and closing, top and bottom trading session prices or trading volume. The above technical indices of a number of Egyptian stock market shares during the period from 2007 to 2017, which can be used for training the proposed system, were collected and used as follows: The data were divided into two sets. The first one contained 67% of the total data and was used for training neural networks and the second contained 33% and was used for testing the proposed system. The training set was segmented into subsets used for training a number of neural networks. The output of such networks was used for training another network hierarchically. The system was, then, tested using the rest of the data.

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“…The study has also presented an optimization www.ijacsa.thesai.org principle to reduce the storage cost as well as the memory of VM. Panda et al [16] have discussed an algorithm that targets multiple environments of cloud based on the smoothening concept. The evaluation of the study was carried out using a bigger dataset of heterogeneous types.…”

Section: A Backgroundmentioning

confidence: 99%

Framework of Resource Management using Server Consolidation to Minimize Live Migration and Load Balancing

Ngenzi¹,

Selvarani²,

Suchithra³

2016

ijacsa

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Abstract-Live Migration is one of the essential operations that require more attention to addressing its high variability problems with virtual machines. We review the existing techniques of resource management to find that there are less modeling to solve this problem. The present paper introduces a novel framework that mainly targets to achieve a computational effective resource management technique. The technique uses the stochastic approach in modelling to design a new traffic management scheme that considers multiple traffic possibilities over VMs along with its switching states. Supported by an analytical modelling approach, the proposed technique offers an efficient placement of virtual machine to the physical server, performs the computation of blocks, and explores reduced resource usage. The study outcome was found to possess potential reduction in live migration, more extent of VM mapping with physical servers, and increased level of capacity.

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A smoothing based task scheduling algorithm for heterogeneous multi-cloud environment

Cited by 24 publications

References 21 publications

Analysis of Spatial and Temporal Variation in Water Coverage in the Sub-Lakes of Poyang Lake Based on Multi-Source Remote Sensing

Analysis of Spatial and Temporal Variation in Water Coverage in the Sub-Lakes of Poyang Lake Based on Multi-Source Remote Sensing

Utilizing Neural Networks for Stocks Prices Prediction in Stocks Markets

Framework of Resource Management using Server Consolidation to Minimize Live Migration and Load Balancing

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