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Methods for efficient resource utilization in statistical machine learning algorithms

Helena Kotthaus, Technische Universität Dortmund, Technische Universität Dortmund
This thesis presents methods for efficient resource utilization of statistical learning applications.  ...  In recent years, statistical machine learning has emerged as a key technique for tackling problems that elude a classic algorithmic approach.  ...  This dissertation addresses methods for efficient resource utilization of statistical machine learning applications.  ... 
doi:10.17877/de290r-18928 fatcat:dxmtldxkzzgd5oshmygvzrf6ey

Multi-Objective Scheduling using Logistic Regression for OpenStack-based Cloud

Niroop V Janagoudar, Narayan D G, Mohammed Moin Mulla
2020 Procedia Computer Science  
This work proposes for the use of machine learning classifier to classify whether host is overloaded or under loaded.  ...  This work proposes for the use of machine learning classifier to classify whether host is overloaded or under loaded.  ...  The work has made a study of such allocation techniques in order to identify an efficient method for issues encountered in resource allocation in cloud computing.  ... 
doi:10.1016/j.procs.2020.04.153 fatcat:t64vwzapnnduxjyqhigup7g5eq


Mohammad Sirajuddin, Research Scholar, Department of CSE Jawaharlal Technological University Hyderabad, Hyderabad, Telangana, India
2021 International Journal of Advanced Research in Computer Science  
Machine learning approaches are thought to be useful for developing energy-efficient routing and localization strategies.  ...  Furthermore, machine learning techniques inspire various practical ways to optimize resource utilization and hence increase the lifespan of the sensor network.  ...  Machine Learning methods, such as ANN and reinforcement learning, play a key role in increasing efficiency by reducing energy usage [8] . D.  ... 
doi:10.26483/ijarcs.v12i6.6788 fatcat:ierg7urpm5eavkofi2ce2a3ty4

Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning [article]

Xiangwei Zhou, Mingxuan Sun, Geoffrey Ye Li, Biing-Hwang Juang
2018 arXiv   pre-print
We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum- and energy-efficient communications  ...  In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication  ...  MACHINE LEARNING FOR INTELLIGENT WIRELESS COMMUNICATIONS In this section, we focus on machine learning for intelligent wireless communications.  ... 
arXiv:1710.11240v4 fatcat:elt77cgcxvappbxvspp7evb74u

Wind Resource Assessment using Machine Learning Algorithm

2020 International Journal of Engineering and Advanced Technology  
Artificial Neural Network is the most commonly used method for wind speed prediction. Recently machine learning and deep learning algorithms are widely used for forecasting applications.  ...  This enhances the significance of the accurate wind speed prediction The objective of this paper is to predict the wind speed for Tamil Nadu cities using machine learning algorithm.  ...  and the artificial intelligence and machine learning algorithm based methods and so on  ... 
doi:10.35940/ijeat.d7836.049420 fatcat:4dnf5dpzqvb5dorsvx3sljh4oy

Adaptive Prediction Models for Data Center Resources Utilization Estimation

Shuja-ur-Rehman Baig, Waheed Iqbal, Josep Lluis Berral, Abdelkarim Erradi, David Carrera
2019 IEEE Transactions on Network and Service Management  
The proposed approach trains a classifier based on statistical features of historical resources usage to decide the appropriate prediction model to use for given resource utilization observations collected  ...  Accurate estimation of future resources utilization helps in better job scheduling, workload placement, capacity planning, proactive auto-scaling, and load balancing.  ...  Efficient methods for estimating resource utilization in data centers can significantly ease self-management and usage optimization for both users and providers.  ... 
doi:10.1109/tnsm.2019.2932840 fatcat:3bbu3n6pqrbt5kuxbcqfazrufq

Forecasting Cloud Resource Provisioning System using Supervised Machine Learning

2020 International journal of recent technology and engineering  
Using of the machine learning in cloud computing leads to many benefits.  ...  This paper uses three supervised machine-learning algorithms to classify and predict CPU utilization because of their capability to keep data and predict accurate time series issues.  ...  Trained Models Firstly the model is trained with CPU utilization data set by using three machine learning method.  ... 
doi:10.35940/ijrte.f8886.038620 fatcat:skvw4vlehbha3hfrfgkf2d3ul4

Energy Management Techniques for Cloud Based Environment

Neenu Juneja, Chamkaur Singh, Krishan Tuli
2020 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
From here comes the importance of scheduling virtual resources to get the maximum utilization of physical resources.  ...  Keywords: Cloud computing, Collocated virtual machines, Live migration, Load balancing, Resource scheduling  ...  A job consolidation algorithm with DVFS technique is proposed in [28] , for efficient resource utilization in hetero-genetic Cloud Physical machines.  ... 
doi:10.32628/cseit206293 fatcat:u4dxre7eyzdlzpsnlrptdhh4mu

Toward Generating a new Video Education Lectures Dataset and Performance Comparison with Various Machine Learning Algorithms

M Maysaa H. Abdulameer, Mahmood Z. Abdullah
2019 Annals of Tropical Medicine and Public Health  
test the performance of (Video Lectures) dataset by applying a set of algorithms of machine learning for the tasks of data mining, the different classification methods, are: J48, Decision Tree, OneR,  ...  All the results of those parameters are taken for all of the described methods of classification.  ...  References Fig ( 1 ) 1 : Typical Method of ML Machine learning methods learn from examples. In figure (2)the conventional data structure and is what is common in the ML area.  ... 
doi:10.36295/asro.2019.221230 fatcat:yr22ty6porh4vawvi467kjl3ie

A Comparative Study on CPU Load Predictions in a Computational Grid using Artificial Neural Network Algorithms

Shaik Naseera, G. K. Rajini, N. Amutha Prabha, G. Abhishek
2015 Indian Journal of Science and Technology  
Application/Improvements: Job scheduling and resource selection algorithms can employ neural network algorithms to predict the load for the sharable resources connected in the network for more accurate  ...  We designed a multilayer neural network and trained with learning algorithms for the input patterns collected from the load traces and predicted the future load statistics.  ...  Second, as a means of load balancing policy in the grid, the jobs migrate from one host to another host for effective utilization of the under-utilized resources, there by improving the overall throughput  ... 
doi:10.17485/ijst/2015/v8i35/82733 fatcat:ukl27tdiz5fqjouq3f43s4k6mi

A Simulation Platform for Multi-tenant Machine Learning Services on Thousands of GPUs [article]

Ruofan Liang, Bingsheng He, Shengen Yan, Peng Sun
2022 arXiv   pre-print
In this demonstration, we present AnalySIM, a cluster simulator that allows efficient design explorations for multi-tenant machine learning services.  ...  Multi-tenant machine learning services have become emerging data-intensive workloads in data centers with heavy usage of GPU resources.  ...  Efficiently utilizing and allocating GPU resources in the cluster nowadays is another important topic in both industry and academia.  ... 
arXiv:2201.03175v1 fatcat:5u7epp5gbnanfpte5fnb7mzyii

Statistical Evaluation of Task Scheduling Algorithms in Cloud Environments

Vidya Kantale
2020 International Journal of Advanced Trends in Computer Science and Engineering  
As per the research, the machine learning based algorithms perform better in terms of overall task scheduling efficiency when compared with others.  ...  Task scheduling algorithms in cloud have come a long way, from simplistic algorithms like first come first serve, to bio-inspired & machine learning algorithms like Q-learning and genetic algorithms.  ...  resource utilization and 10% reduction in algorithm complexity.  ... 
doi:10.30534/ijatcse/2020/88922020 fatcat:mtp5o6fyarbitfm7p4szpzmtzy

Intelligent Radio: When Artificial Intelligence Meets the Radio Network

Tao Chen, Hsiao-Hwa Chen, Zheng Chang, Shiwen Mao
2020 IEEE wireless communications  
The recent advances in artificial intelligence (AI), including machine learning (ML), data mining, and big data analysis, bring significant promise for addressing hard problems in radio networks.  ...  Qin et al., introduces recent advances in ML in wireless communications. It briefly introduces deep learning applied for physical layer communications and resource allocation.  ...  hSiao-hwa Chen [F] is currently a Distinguished Professor in the Department of Engineering Science, National Cheng Kung University, Tainan City, Taiwan.  ... 
doi:10.1109/mwc.2020.9023916 fatcat:5o2yvxnb3zhy5hd6af7g7kb3ly

Machine Learning for Intelligent Authentication in 5G-and-Beyond Wireless Networks [article]

He Fang, Xianbin Wang, Stefano Tomasin
2019 arXiv   pre-print
In a nutshell, the machine learning-based intelligent authentication approaches utilize specific features in the multi-dimensional domain for achieving cost-effective, more reliable, model-free, continuous  ...  Machine learning paradigms for intelligent authentication design are presented, namely for parametric/non-parametric and supervised/unsupervised/reinforcement learning algorithms.  ...  Fig. 4 : 4 Categories of machine learning (ML) techniques for intelligent authentication.Parametric learning methods: The family of parametric learning methods [13] has become mature in the literature  ... 
arXiv:1907.00429v2 fatcat:qwd4wvenrfhwdcluzzavivdtga

Integrated multi-scale data analytics and machine learning for the distribution grid

Emma M. Stewart, Philip Top, Michael Chertkov, Deepjyoti Deka, Scott Backhaus, Andrey Lokhov, Ciaran Roberts, Val Hendrix, Sean Peisert, Anthony Florita, Thomas J. King, Matthew J. Reno
2017 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm)  
Efficient applications of analysis and the machine learning field are being considered in the loop.  ...  We consider the field of machine learning and where it is both useful, and not useful, for the distribution grid and buildings interface.  ...  Existing and recently developed machine-learning algorithms and approaches for power-distribution data problems can be divided into two categories: 1) utilizing established black box machine-learning methods  ... 
doi:10.1109/smartgridcomm.2017.8340693 dblp:conf/smartgridcomm/StewartTCDBLRHP17 fatcat:zmu4jrdptbfnrn2yne4kclksw4
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