Pub Date : 2020-06-01DOI: 10.1109/ecti-con49241.2020.9158127
C. Khin, May Zin Oo, A. Kyaw
this paper proposes a mechanism that handles the packet-in messages to reduce the bottlenecks of controller in SDN/OpenFlow network. In SDN networks, the Openflow switches send many packet-in messages to forward every new flow of data. Then, the controller responds the flow rule for each packet-in message. Therefore, the bottlenecks can occur between controller and switches for sending three packets (one flooding and two packet-in messages) to add a new flow. The proposed system can reduce this bottleneck effect by adding a couple flow rules only with a single packet-in message. It is implemented using Mininet emulator to create a network topology and Ryu OpenFlow controller to manage the forwarding plane by the controller. The evaluation indicates that the proposed system is able to reduce one-third of packet overhead compared to original OpenFlow.
{"title":"Packet-in Messages Handling Scheme to Reduce Controller Bottlenecks in OpenFlow Networks","authors":"C. Khin, May Zin Oo, A. Kyaw","doi":"10.1109/ecti-con49241.2020.9158127","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158127","url":null,"abstract":"this paper proposes a mechanism that handles the packet-in messages to reduce the bottlenecks of controller in SDN/OpenFlow network. In SDN networks, the Openflow switches send many packet-in messages to forward every new flow of data. Then, the controller responds the flow rule for each packet-in message. Therefore, the bottlenecks can occur between controller and switches for sending three packets (one flooding and two packet-in messages) to add a new flow. The proposed system can reduce this bottleneck effect by adding a couple flow rules only with a single packet-in message. It is implemented using Mininet emulator to create a network topology and Ryu OpenFlow controller to manage the forwarding plane by the controller. The evaluation indicates that the proposed system is able to reduce one-third of packet overhead compared to original OpenFlow.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130613675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents the study of lead acid battery for charging process using Hydrogen (H) and Oxygen (O2) gas release condition. The battery of 12V/100 Ah is selected to study from the purposed. The battery charging methodology is combined from the basically charging with constant current, constant voltage and gas analysis method for finding fully charging condition. The measurement data from the charging process show gas leases with relate the H and O2 gas varies from the battery voltage. The O2 and H gas release can be defined into 2 sections. The second section the O2 gas release has high changed in more than H gas release that point the H gas will be linearized trend. Theses point of the second section can define the fully charging process of the LA battery with 15.5 V of battery voltage. Therefore, this method can be defined as maximum voltage for charging the LA battery and adapted in the future.
{"title":"Novel Battery Charging Method using Hydrogen and Oxygen Gas Release Condition for Lead Acid Battery","authors":"Nirutti Nilkeaw, Chairat Sornchai, Boonyang Plungklang","doi":"10.1109/ecti-con49241.2020.9158269","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158269","url":null,"abstract":"This paper presents the study of lead acid battery for charging process using Hydrogen (H) and Oxygen (O2) gas release condition. The battery of 12V/100 Ah is selected to study from the purposed. The battery charging methodology is combined from the basically charging with constant current, constant voltage and gas analysis method for finding fully charging condition. The measurement data from the charging process show gas leases with relate the H and O2 gas varies from the battery voltage. The O2 and H gas release can be defined into 2 sections. The second section the O2 gas release has high changed in more than H gas release that point the H gas will be linearized trend. Theses point of the second section can define the fully charging process of the LA battery with 15.5 V of battery voltage. Therefore, this method can be defined as maximum voltage for charging the LA battery and adapted in the future.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116651925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ecti-con49241.2020.9158258
Wipha Thomyamongkol, E. Nantajeewarawat, Pattravadee Ploykitikoon, Paramet Tanwanont
Risk adjusted measurements can be used to measure risk and volatility that are involved in investment returns. They are of great interest to investors. This paper focuses on evaluation of the risk adjusted measurements and aims to find the measurements that are most suitable for evaluating the performance of Thai mutual funds. Three popular risk adjusted measurements, i.e., Sharpe ratio, Treynor ratio, and Jensen’s alpha ratio, are used. Data from all funds in the bond asset class, the real estate asset class, and the equity asset class, from January 2013 to the end of December 2018 are used for the evaluation. The analysis shows that Jensen’s alpha ratio outperforms the other measurements in all the three asset classes.
{"title":"An Evaluation of Risk Adjusted Measurements for Thai Mutual Funds","authors":"Wipha Thomyamongkol, E. Nantajeewarawat, Pattravadee Ploykitikoon, Paramet Tanwanont","doi":"10.1109/ecti-con49241.2020.9158258","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158258","url":null,"abstract":"Risk adjusted measurements can be used to measure risk and volatility that are involved in investment returns. They are of great interest to investors. This paper focuses on evaluation of the risk adjusted measurements and aims to find the measurements that are most suitable for evaluating the performance of Thai mutual funds. Three popular risk adjusted measurements, i.e., Sharpe ratio, Treynor ratio, and Jensen’s alpha ratio, are used. Data from all funds in the bond asset class, the real estate asset class, and the equity asset class, from January 2013 to the end of December 2018 are used for the evaluation. The analysis shows that Jensen’s alpha ratio outperforms the other measurements in all the three asset classes.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"136 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131212573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ECTI-CON49241.2020.9158060
Phattaleeya Mabpa, Piyasawat Navaratana Na Ayudhya, J. Kunthong
Clogged sewer pipelines are one of the main problems that cause Sanitary Sewer Overflow ( SSO) which leads to serious environmental issues and property damage. This work presented clogged pipe detection and monitoring methods based on acoustic analysis to identify pipe clogged occurrence and degree of blockage that can be mitigating the risks from SSO’s problems. The technique is to attach the vibration speaker on the pipe as an acoustic source. The clogged by blockage will be detected by reading the change in the pipe resonance frequency via the microphone installed on the other side of the pipeline. The resonance frequency of the measured signals was characterized by Fast Fourier Transforms (FFT). Compensation based line was used to normalize the frequency responses for easier acoustic analysis. The experiments have indicated the resonance frequency shifting down and Sound Pressure Level (SPL) decreasing when pipe clogged. Moreover, this paper provided the technique that offers small and noncomplex data which any classification method can identify pipe clogged easier in future work.
{"title":"Clogged Pipe Detection and Monitoring by Using Acoustic Analysis Methodology","authors":"Phattaleeya Mabpa, Piyasawat Navaratana Na Ayudhya, J. Kunthong","doi":"10.1109/ECTI-CON49241.2020.9158060","DOIUrl":"https://doi.org/10.1109/ECTI-CON49241.2020.9158060","url":null,"abstract":"Clogged sewer pipelines are one of the main problems that cause Sanitary Sewer Overflow ( SSO) which leads to serious environmental issues and property damage. This work presented clogged pipe detection and monitoring methods based on acoustic analysis to identify pipe clogged occurrence and degree of blockage that can be mitigating the risks from SSO’s problems. The technique is to attach the vibration speaker on the pipe as an acoustic source. The clogged by blockage will be detected by reading the change in the pipe resonance frequency via the microphone installed on the other side of the pipeline. The resonance frequency of the measured signals was characterized by Fast Fourier Transforms (FFT). Compensation based line was used to normalize the frequency responses for easier acoustic analysis. The experiments have indicated the resonance frequency shifting down and Sound Pressure Level (SPL) decreasing when pipe clogged. Moreover, this paper provided the technique that offers small and noncomplex data which any classification method can identify pipe clogged easier in future work.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131566080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ecti-con49241.2020.9158120
J. Mongkolnavin, Widakorn Saewong
Call center is a department that is most relevant to audio data usage. One of its major tasks is to monitor customers’ anguish because it has a negative impact on the organization. One challenging task is to develop a model that can predict whether a customer is getting angry in the next turn of conversation. Such model can assist agents in taking appropriate action(s) to prevent the incidents. In this study, we investigate an approach to build an anger prediction model from customers’ voice in call center dialogs. To create the model requires 5 processes: (1) Customer’s turn extraction (2) Emotion annotation (3) Voice feature selection (4) Data pre-processing for long short-term memory networks, and (5) Anger prediction modeling. Five long short-term memory networks were built with the time series data sets of 1, 2, 3, 4, and 5 consecutive turns. The experimental results showed that the long short-term memory network built with the 3-consecutive turn data has promising performance in aspect of Average Precision and False Negative Rate when compared to the random and good guess benchmarks.
{"title":"Prediction of Forthcoming Anger of Customer in Call Center Dialogs","authors":"J. Mongkolnavin, Widakorn Saewong","doi":"10.1109/ecti-con49241.2020.9158120","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158120","url":null,"abstract":"Call center is a department that is most relevant to audio data usage. One of its major tasks is to monitor customers’ anguish because it has a negative impact on the organization. One challenging task is to develop a model that can predict whether a customer is getting angry in the next turn of conversation. Such model can assist agents in taking appropriate action(s) to prevent the incidents. In this study, we investigate an approach to build an anger prediction model from customers’ voice in call center dialogs. To create the model requires 5 processes: (1) Customer’s turn extraction (2) Emotion annotation (3) Voice feature selection (4) Data pre-processing for long short-term memory networks, and (5) Anger prediction modeling. Five long short-term memory networks were built with the time series data sets of 1, 2, 3, 4, and 5 consecutive turns. The experimental results showed that the long short-term memory network built with the 3-consecutive turn data has promising performance in aspect of Average Precision and False Negative Rate when compared to the random and good guess benchmarks.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128183278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ecti-con49241.2020.9158073
N. Supreeyatitikul, N. Teerasuttakorn
In this research, a miniaturized two-element multiple-input multiple-output (MIMO) antenna with high isolation by using metamaterial (MTM) has been presented for dual-band of millimeter-wave frequency (28 GHz and 38 GHz). The proposed MIMO antenna array has been etched on Rogers-5880 with an overall size of 23×10×0.787 mm3. The high isolation between two-element antennas was obtained by reducing the mutual coupling which employed the square split-ring resonators (S-SRRs). The S-SRRs can be achieved a low transmission coefficient of −34.56 dB and −49.85 dB at the entire operating frequency of 28 GHz and 38 GHz, respectively. The diversity performance of the proposed MIMO antenna array has been verified in order to prove the MIMO performance for mm-wave wireless communications.
{"title":"Improved Isolation of a Dual-Band MIMO Antenna Using Modified S-SRRs for Millimeter-Wave Applications","authors":"N. Supreeyatitikul, N. Teerasuttakorn","doi":"10.1109/ecti-con49241.2020.9158073","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158073","url":null,"abstract":"In this research, a miniaturized two-element multiple-input multiple-output (MIMO) antenna with high isolation by using metamaterial (MTM) has been presented for dual-band of millimeter-wave frequency (28 GHz and 38 GHz). The proposed MIMO antenna array has been etched on Rogers-5880 with an overall size of 23×10×0.787 mm3. The high isolation between two-element antennas was obtained by reducing the mutual coupling which employed the square split-ring resonators (S-SRRs). The S-SRRs can be achieved a low transmission coefficient of −34.56 dB and −49.85 dB at the entire operating frequency of 28 GHz and 38 GHz, respectively. The diversity performance of the proposed MIMO antenna array has been verified in order to prove the MIMO performance for mm-wave wireless communications.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134085160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Software development is a team-based intensive activity where various skills (e.g. technical and analysis skills) are required to deliver high quality outcomes. An effective team member assignment is thus a crucial process. In this paper, we propose to adopt the existing machine learning approach for team recommendation to recommend software team members who are suitable for a given task. The approach take both individual strength and collaborative efficiency among team members into account to give a recommendation. We evaluate the approach on the Moodle project, well-known open source software project. The evaluation results show that the adopted approach yields a better recommendation performance compared to the baseline (i.e. random assignment approach).
{"title":"Towards Team Formation in Software Development: A Case Study of Moodle","authors":"Noppadol Assavakamhaenghan, Ponlakit Suwanworaboon, Waralee Tanaphantaruk, Suppawong Tuarob, Morakot Choetkiertikul","doi":"10.1109/ecti-con49241.2020.9158078","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158078","url":null,"abstract":"Software development is a team-based intensive activity where various skills (e.g. technical and analysis skills) are required to deliver high quality outcomes. An effective team member assignment is thus a crucial process. In this paper, we propose to adopt the existing machine learning approach for team recommendation to recommend software team members who are suitable for a given task. The approach take both individual strength and collaborative efficiency among team members into account to give a recommendation. We evaluate the approach on the Moodle project, well-known open source software project. The evaluation results show that the adopted approach yields a better recommendation performance compared to the baseline (i.e. random assignment approach).","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134414729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ecti-con49241.2020.9158275
Pikkanate Angaphiwatchawal, Poowasarun Phisuthsaingam, S. Chaitusaney
With the development and low-cost trend of renewable energy technologies, particularly PV rooftop systems, energy consumers can produce electricity to self-consume and/or export the surplus energy into low-voltage (LV) distribution grids. The peer-to-peer (P2P) energy trading widely allows consumers with a generation role, called prosumers, to trade their surplus energy with other prosumers. The purpose of this study is to investigate how to impose the exchanged price between two P2P participants by using the k-factor continuous double auction (CDA) algorithm to scale up/down between seller’s offers and buyer’s bids submitted in the P2P energy trading. The k-factor is set to be varied between 0 and 1. The simulation results, based on the case study, show that the approximated value of k as of 0.6445 which is such that the benefits between sellers and buyers are equal is feasible. This states that the exchanged price between two participants can be formed with a combination between 64.45% and 35.55% of the buyer’s outstanding bid and the seller’s outstanding offer, respectively.
{"title":"A k-Factor Continuous Double Auction-Based Pricing Mechanism for the P2P Energy Trading in a LV Distribution System","authors":"Pikkanate Angaphiwatchawal, Poowasarun Phisuthsaingam, S. Chaitusaney","doi":"10.1109/ecti-con49241.2020.9158275","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158275","url":null,"abstract":"With the development and low-cost trend of renewable energy technologies, particularly PV rooftop systems, energy consumers can produce electricity to self-consume and/or export the surplus energy into low-voltage (LV) distribution grids. The peer-to-peer (P2P) energy trading widely allows consumers with a generation role, called prosumers, to trade their surplus energy with other prosumers. The purpose of this study is to investigate how to impose the exchanged price between two P2P participants by using the k-factor continuous double auction (CDA) algorithm to scale up/down between seller’s offers and buyer’s bids submitted in the P2P energy trading. The k-factor is set to be varied between 0 and 1. The simulation results, based on the case study, show that the approximated value of k as of 0.6445 which is such that the benefits between sellers and buyers are equal is feasible. This states that the exchanged price between two participants can be formed with a combination between 64.45% and 35.55% of the buyer’s outstanding bid and the seller’s outstanding offer, respectively.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134430695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Myocardial Ischemia is the main cause of mortality in patients with Coronary Artery Disease (CAD). One of the methods used in screening patients with this disease is the diag-nosis of radionuclide myocardial perfusion imaging (rMPI). In this paper, we conducted a comparative study by experimenting on several machine learning models, such as Logistic Regression, Random Forest, XGBoost, etc., to classify the stenosis of coronary artery. High-level features from rMPI computed by 4D-MSPECT polar map were used to train/test the models. rMPI features of the risk group of CAD patients were obtained from a public hospital. With the hypothesis that patient characteristics (e.g., Diabetes Mellitus, Hypertension, Dyslipidemia) could improve the prediction performance of the models, this study also included patient characteristics in our experimentation as important parts of feature selection. All other processes (i.e., data cleaning, feature selection, feature engineering and feature transformation) in machine learning pipeline were also deliberately experimented in this study. For model selection, two-level validation regarding generalization and hyperparameter tuning were also performed.
{"title":"Comparing Classifiers for the Prediction of the Stenosis of Coronary Artery","authors":"Hataichanok Aakkara, Atumporn Aaisueb, Aeerapong Aeelanupab","doi":"10.1109/ecti-con49241.2020.9158312","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158312","url":null,"abstract":"Myocardial Ischemia is the main cause of mortality in patients with Coronary Artery Disease (CAD). One of the methods used in screening patients with this disease is the diag-nosis of radionuclide myocardial perfusion imaging (rMPI). In this paper, we conducted a comparative study by experimenting on several machine learning models, such as Logistic Regression, Random Forest, XGBoost, etc., to classify the stenosis of coronary artery. High-level features from rMPI computed by 4D-MSPECT polar map were used to train/test the models. rMPI features of the risk group of CAD patients were obtained from a public hospital. With the hypothesis that patient characteristics (e.g., Diabetes Mellitus, Hypertension, Dyslipidemia) could improve the prediction performance of the models, this study also included patient characteristics in our experimentation as important parts of feature selection. All other processes (i.e., data cleaning, feature selection, feature engineering and feature transformation) in machine learning pipeline were also deliberately experimented in this study. For model selection, two-level validation regarding generalization and hyperparameter tuning were also performed.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133405056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ECTI-CON49241.2020.9158095
Thandar Htay, S. Phyu
Apache Hadoop is a widely used open-source distributed platform towards big data processing and provides YARN based distributed parallel processing framework on low cost commodity machines. However, YARN adopts static resource management (that is, the number of containers available per node and the size of each container are static in nature) depending on pre-configured default resource units called containers leading to poor performance to deal with various sort of MapReduce applications. In addition, during the last wave of a job, many available resources occur frequently being idle because YARN does not consider the wave behavior in tasks of MapReduce applications. To take advantage of idle resources resulting in performance improvement, the important parameter, the number of map tasks is needed to optimize based on the available resources and governed by split size. Therefore, this parameter is optimized through the split size tuning based on the available resources. To address the drawback of static resource management of yarn in Hadoop, the numbers of concurrent containers per machine are tuned to optimize the node performance for running each MapReduce application. As per experimental results, the proposed system that optimizes the selected parameter on optimized concurrent containers can achieve the performance gains of MapReduce applications while reducing the optimization overheads.
{"title":"Towards Performance Optimization for Hadoop MapReduce Applications","authors":"Thandar Htay, S. Phyu","doi":"10.1109/ECTI-CON49241.2020.9158095","DOIUrl":"https://doi.org/10.1109/ECTI-CON49241.2020.9158095","url":null,"abstract":"Apache Hadoop is a widely used open-source distributed platform towards big data processing and provides YARN based distributed parallel processing framework on low cost commodity machines. However, YARN adopts static resource management (that is, the number of containers available per node and the size of each container are static in nature) depending on pre-configured default resource units called containers leading to poor performance to deal with various sort of MapReduce applications. In addition, during the last wave of a job, many available resources occur frequently being idle because YARN does not consider the wave behavior in tasks of MapReduce applications. To take advantage of idle resources resulting in performance improvement, the important parameter, the number of map tasks is needed to optimize based on the available resources and governed by split size. Therefore, this parameter is optimized through the split size tuning based on the available resources. To address the drawback of static resource management of yarn in Hadoop, the numbers of concurrent containers per machine are tuned to optimize the node performance for running each MapReduce application. As per experimental results, the proposed system that optimizes the selected parameter on optimized concurrent containers can achieve the performance gains of MapReduce applications while reducing the optimization overheads.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131832228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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