Software information sites such as Stack Overflow, Super User, and Ask Ubuntu allow users to post software-related questions, answer the questions asked by other users, and add tags to their questions. Tagging is a popular system across web communities because allowing users to classify their contents is less costly than employing an expert to categorize them. However, tagging systems suffer from the problem of the tag explosion and the tag synonym. To solve these problems, we propose a tag recommendation method using topic modeling approaches. Topic models have advantages of dimensionality reduction and document similarity. We also emphasize highest topics in calculating document similarity to retrieve more relevant documents. Our tag recommendation method considers the document similarity and the historical tag occurrence to calculate tag scores. Experiment results show that emphasizing highest topic distributions increases overall performance of tag recommendation.
{"title":"An Efficient Tag Recommendation Method using Topic Modeling Approaches","authors":"Beomseok Hong, Yanggon Kim, Sang Ho Lee","doi":"10.1145/3129676.3129709","DOIUrl":"https://doi.org/10.1145/3129676.3129709","url":null,"abstract":"Software information sites such as Stack Overflow, Super User, and Ask Ubuntu allow users to post software-related questions, answer the questions asked by other users, and add tags to their questions. Tagging is a popular system across web communities because allowing users to classify their contents is less costly than employing an expert to categorize them. However, tagging systems suffer from the problem of the tag explosion and the tag synonym. To solve these problems, we propose a tag recommendation method using topic modeling approaches. Topic models have advantages of dimensionality reduction and document similarity. We also emphasize highest topics in calculating document similarity to retrieve more relevant documents. Our tag recommendation method considers the document similarity and the historical tag occurrence to calculate tag scores. Experiment results show that emphasizing highest topic distributions increases overall performance of tag recommendation.","PeriodicalId":326100,"journal":{"name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129258429","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}
The research interest of real-time global illumination has increased due to the growing demand of graphics applications such as virtual reality. Recently, the design that combines Image-based rendering (IBR) and Ray-Tracing to create Synthetic Light Field (SLF) has been widely adopted to provide delicate visual experience for multiple viewpoints at an acceptable frame rate. However, despite its parallel characteristic, constructing a SLF is still inefficient on modern Graphics Processing Unit (GPU) due to the irregularities. For instance, the issues caused by branch divergence, early-termination and irregular memory access prolong the execution time that cannot be simply resolved by workload merging. In this paper, we proposed a Runtime design that reorganizes the execution into a pipeline-based pattern with grouping of primary rays. With this approach, the number of valid rays can be maintained at a high level with less divergence of paths. Based on the experiment on a heterogeneous system, the throughput becomes 2.48 times higher than the original on average.
{"title":"Efficient Synthetic Light Field Rendering on Heterogeneous Systems Using a Pipeline-Based Runtime Design","authors":"Chih-Chen Kao, Liang-Chi Tseng, W. Hsu","doi":"10.1145/3129676.3129677","DOIUrl":"https://doi.org/10.1145/3129676.3129677","url":null,"abstract":"The research interest of real-time global illumination has increased due to the growing demand of graphics applications such as virtual reality. Recently, the design that combines Image-based rendering (IBR) and Ray-Tracing to create Synthetic Light Field (SLF) has been widely adopted to provide delicate visual experience for multiple viewpoints at an acceptable frame rate. However, despite its parallel characteristic, constructing a SLF is still inefficient on modern Graphics Processing Unit (GPU) due to the irregularities. For instance, the issues caused by branch divergence, early-termination and irregular memory access prolong the execution time that cannot be simply resolved by workload merging. In this paper, we proposed a Runtime design that reorganizes the execution into a pipeline-based pattern with grouping of primary rays. With this approach, the number of valid rays can be maintained at a high level with less divergence of paths. Based on the experiment on a heterogeneous system, the throughput becomes 2.48 times higher than the original on average.","PeriodicalId":326100,"journal":{"name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","volume":"31 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113976383","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}
Gait is a pattern of biometric movement for human identification. Unlike other biometrics such as fingerprint, iris, face, and voice recognition, human gait can be captured with unobtrusive method. In this paper, several measurements are proposed which uses body frame information in 3D space. Body frame data is generated from depth images captured using Kinect camera. The generated body frames are used for human gait analysis. The angle of lower body parts is measured in a gait cycle. In addition, the length of body parts is measured as a feature for combination with the angle measurements. The measurements are compared to each other from 5 subjects who have similar body type. The difference from comparison of the measurements indicates that the human gait has a potential pattern for human identification.
{"title":"Gait Recognition for Human Identification using Kinect","authors":"Wonjin Kim, Yanggon Kim","doi":"10.1145/3129676.3129715","DOIUrl":"https://doi.org/10.1145/3129676.3129715","url":null,"abstract":"Gait is a pattern of biometric movement for human identification. Unlike other biometrics such as fingerprint, iris, face, and voice recognition, human gait can be captured with unobtrusive method. In this paper, several measurements are proposed which uses body frame information in 3D space. Body frame data is generated from depth images captured using Kinect camera. The generated body frames are used for human gait analysis. The angle of lower body parts is measured in a gait cycle. In addition, the length of body parts is measured as a feature for combination with the angle measurements. The measurements are compared to each other from 5 subjects who have similar body type. The difference from comparison of the measurements indicates that the human gait has a potential pattern for human identification.","PeriodicalId":326100,"journal":{"name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121024021","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}
The hibernation-based Linux fast booting method is often call the function "shrink_all_memory" in the Linux kernel, and this function is not exported to the kernel module. In this study, the fast booting is designed as a kernel module, so the same kernel module can be used in different versions of Linux kernel.
{"title":"Implementing Hibernation-based Fast Booting as a Device Driver","authors":"Shiwu Lo, Yueyuan Zhang","doi":"10.1145/3129676.3129691","DOIUrl":"https://doi.org/10.1145/3129676.3129691","url":null,"abstract":"The hibernation-based Linux fast booting method is often call the function \"shrink_all_memory\" in the Linux kernel, and this function is not exported to the kernel module. In this study, the fast booting is designed as a kernel module, so the same kernel module can be used in different versions of Linux kernel.","PeriodicalId":326100,"journal":{"name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126392581","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}
M. Taheri, Dheeman Saha, Gary Hatfield, E. Byamukama, Sung Y. Shin
This paper proposes a Decision Management System to identify the white mold regions from the soybean fields using Autologistic Statistical Model (ASM) and Remote Sensing (RS) data analysis with commercially available Big Data sets as input data. In order to develop an identification model, numerous types of data need to be considered. In this study, the data that was used is satellite image pixel values, and data gathered from the field such as precipitation, yield, elevation, humidity, wind speed, wind direction and geospatial locations. The model evaluated the outcome using this information as input parameters and provided an overall estimation of the white mold region in the soybean fields. Based on the evaluation of the result, the accuracy rate of the proposed methods 84% which is a promising result due to the fact that each pixel of the satellite image is 30 by 30 meters.
{"title":"Applied Statistical Model and Remote Sensing for Decision Management System for Soybean","authors":"M. Taheri, Dheeman Saha, Gary Hatfield, E. Byamukama, Sung Y. Shin","doi":"10.1145/3129676.3129710","DOIUrl":"https://doi.org/10.1145/3129676.3129710","url":null,"abstract":"This paper proposes a Decision Management System to identify the white mold regions from the soybean fields using Autologistic Statistical Model (ASM) and Remote Sensing (RS) data analysis with commercially available Big Data sets as input data. In order to develop an identification model, numerous types of data need to be considered. In this study, the data that was used is satellite image pixel values, and data gathered from the field such as precipitation, yield, elevation, humidity, wind speed, wind direction and geospatial locations. The model evaluated the outcome using this information as input parameters and provided an overall estimation of the white mold region in the soybean fields. Based on the evaluation of the result, the accuracy rate of the proposed methods 84% which is a promising result due to the fact that each pixel of the satellite image is 30 by 30 meters.","PeriodicalId":326100,"journal":{"name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125280192","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}
Zhi-Guo Chen, Ho-Seok Kang, Shang-nan Yin, Sung-Ryul Kim
In recent cyber incidents, Ransom software (ransomware) causes a major threat to the security of computer systems. Consequently, ransomware detection has become a hot topic in computer security. Unfortunately, current signature-based and static detection model is often easily evadable by obfuscation, polymorphism, compress, and encryption. For overcoming the lack of signature-based and static ransomware detection approach, we have proposed the dynamic ransomware detection system using data mining techniques such as Random Forest (RF), Support Vector Machine (SVM), Simple Logistic (SL) and Naive Bayes (NB) algorithms for detecting known and unknown ransomware. We monitor the actual (dynamic) behaviors of software to generate API calls flow graphs (CFG) and transfer it in a feature space. Thereafter, data normalization and feature selection were applied to select informative features which are the best for discriminating between various categories of software and benign software. Finally, the data mining algorithms were used for building the detection model for judging whether the software is benign software or ransomware. Our experimental results show that our proposed system can be more effective to improve the performance for ransomware detection. Especially, the accuracy and detection rate of our proposed system with Simple Logistic (SL) algorithm can achieve to 98.2% and 97.6%, respectively. Meanwhile, the false positive rate also can be reduced to 1.2%.
{"title":"Automatic Ransomware Detection and Analysis Based on Dynamic API Calls Flow Graph","authors":"Zhi-Guo Chen, Ho-Seok Kang, Shang-nan Yin, Sung-Ryul Kim","doi":"10.1145/3129676.3129704","DOIUrl":"https://doi.org/10.1145/3129676.3129704","url":null,"abstract":"In recent cyber incidents, Ransom software (ransomware) causes a major threat to the security of computer systems. Consequently, ransomware detection has become a hot topic in computer security. Unfortunately, current signature-based and static detection model is often easily evadable by obfuscation, polymorphism, compress, and encryption. For overcoming the lack of signature-based and static ransomware detection approach, we have proposed the dynamic ransomware detection system using data mining techniques such as Random Forest (RF), Support Vector Machine (SVM), Simple Logistic (SL) and Naive Bayes (NB) algorithms for detecting known and unknown ransomware. We monitor the actual (dynamic) behaviors of software to generate API calls flow graphs (CFG) and transfer it in a feature space. Thereafter, data normalization and feature selection were applied to select informative features which are the best for discriminating between various categories of software and benign software. Finally, the data mining algorithms were used for building the detection model for judging whether the software is benign software or ransomware. Our experimental results show that our proposed system can be more effective to improve the performance for ransomware detection. Especially, the accuracy and detection rate of our proposed system with Simple Logistic (SL) algorithm can achieve to 98.2% and 97.6%, respectively. Meanwhile, the false positive rate also can be reduced to 1.2%.","PeriodicalId":326100,"journal":{"name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131476727","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}
Image denoising is crucial to improve the quality of image visual, their effects, and/ or facilitating image analysis and processing. Image noise can appear in many imaging applications such as remote sensing surveillance and assistant of medical surgery. Noises are often introduced during the image acquisition process when the image acquisition sensor is being interfered. Hence, the image denoising technique is commonly used to restore the original signal through the estimation and approximation. Recently, a sparse coding technique employing the dictionary learning method has been used for image denoising. In this study, we compare a recently proposed image denoising method called Moran's I Vector Median Filter (MIVMF) with the sparse coding method and a traditional scalar median filter for the impulse noise. In these preliminary results, the sparse coding does not give satisfactory results as what we expected. Instead, the MIVMF has the best denoising results.
{"title":"A Comparison on Sparse Coding and Moran's I Method for Image Denoising","authors":"M. Nguyen, C. Hung, Mingon Kang","doi":"10.1145/3129676.3129711","DOIUrl":"https://doi.org/10.1145/3129676.3129711","url":null,"abstract":"Image denoising is crucial to improve the quality of image visual, their effects, and/ or facilitating image analysis and processing. Image noise can appear in many imaging applications such as remote sensing surveillance and assistant of medical surgery. Noises are often introduced during the image acquisition process when the image acquisition sensor is being interfered. Hence, the image denoising technique is commonly used to restore the original signal through the estimation and approximation. Recently, a sparse coding technique employing the dictionary learning method has been used for image denoising. In this study, we compare a recently proposed image denoising method called Moran's I Vector Median Filter (MIVMF) with the sparse coding method and a traditional scalar median filter for the impulse noise. In these preliminary results, the sparse coding does not give satisfactory results as what we expected. Instead, the MIVMF has the best denoising results.","PeriodicalId":326100,"journal":{"name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132355105","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}
Alternative splicing refers to the production of multiple mRNA isoforms from a single gene due to alternative selection of exons or splice sites during pre-mRNA splicing. While canonical alternative splicing produces a linear form of RNA by joining an upstream donor site (5' splice site) with a downstream acceptor site (3' splice site), a special form of alternative splicing produces a non-coding circular form of RNA (circular RNA) by ligating a downstream donor site (5' splice site) with an upstream acceptor site (3' splice site); i.e., back-splicing. Over the past two decades, many studies have discovered this special form of alternative splicing that produces a circular form of RNA. Although these circular RNAs have garnered considerable attention in the scientific community for their biogenesis and functions, the focus of these studies has been on exonic circular RNAs (circRNAs: donor site and acceptor site are from exon boundaries) and circular intronic RNAs (ciRNAs: donor and acceptor are from a single intron). This type of approach was conducted in the relative absence of methods for searching another group of circular RNAs, or circular complex RNAs (ccRNAs: either the donor site or acceptor site is not from exon boundaries), that contains at least one exon and one or more flanking introns. Studies of ccRNAs would serve as a significant first step in filling this void. In this paper, we developed a new computational algorithm that can detect all three types of circular RNAs. We applied our algorithm on a set of RNA-seq data to examine the composition of circular RNAs in the given dataset. Surprisingly, our results showed that the new type of circular RNA (ccRNA) was the second most common type of circular RNA while circRNA was the most common type as expected.
{"title":"Circular RNA Detection from High-throughput Sequencing","authors":"Mohamed Chaabane, E. Rouchka, J. Park","doi":"10.1145/3129676.3129734","DOIUrl":"https://doi.org/10.1145/3129676.3129734","url":null,"abstract":"Alternative splicing refers to the production of multiple mRNA isoforms from a single gene due to alternative selection of exons or splice sites during pre-mRNA splicing. While canonical alternative splicing produces a linear form of RNA by joining an upstream donor site (5' splice site) with a downstream acceptor site (3' splice site), a special form of alternative splicing produces a non-coding circular form of RNA (circular RNA) by ligating a downstream donor site (5' splice site) with an upstream acceptor site (3' splice site); i.e., back-splicing. Over the past two decades, many studies have discovered this special form of alternative splicing that produces a circular form of RNA. Although these circular RNAs have garnered considerable attention in the scientific community for their biogenesis and functions, the focus of these studies has been on exonic circular RNAs (circRNAs: donor site and acceptor site are from exon boundaries) and circular intronic RNAs (ciRNAs: donor and acceptor are from a single intron). This type of approach was conducted in the relative absence of methods for searching another group of circular RNAs, or circular complex RNAs (ccRNAs: either the donor site or acceptor site is not from exon boundaries), that contains at least one exon and one or more flanking introns. Studies of ccRNAs would serve as a significant first step in filling this void. In this paper, we developed a new computational algorithm that can detect all three types of circular RNAs. We applied our algorithm on a set of RNA-seq data to examine the composition of circular RNAs in the given dataset. Surprisingly, our results showed that the new type of circular RNA (ccRNA) was the second most common type of circular RNA while circRNA was the most common type as expected.","PeriodicalId":326100,"journal":{"name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130094524","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}
Recently, the importance of velocity, one of the characteristics of big data (5V: Volume, Variety, Velocity, Veracity, and Value), has been emphasized in the data processing, which has led to several studies on the real-time stream processing, a technology for quick and accurate processing and analyses of big data. In this study, we propose a Squall framework using in-memory technology. Moreover, we provide a description of Squall framework and its operations. This Squall framework can support the real-time event stream processing and micro-batch processing, showing high performance and memory efficiency for stream processing using Go's excellent concurrency and GC (Garbage Collection) available without a virtual machine. Therefore, you can run many jobs on one machine. In addition, the data flows through the memory, the number of operation steps are incorporated to improve the performance. It provides relatively good performance compared to existing Apache Storm and spark streaming. In conclusion, it can be used as a general-purpose big data processing framework because it can overcome the drawbacks of existing Apache storm or Spark streaming by introducing the advantages of Go language.
{"title":"Squall: Stream Processing and Analysis Model Design","authors":"J. An, J. Son, Jiwoo Kang","doi":"10.1145/3129676.3129707","DOIUrl":"https://doi.org/10.1145/3129676.3129707","url":null,"abstract":"Recently, the importance of velocity, one of the characteristics of big data (5V: Volume, Variety, Velocity, Veracity, and Value), has been emphasized in the data processing, which has led to several studies on the real-time stream processing, a technology for quick and accurate processing and analyses of big data. In this study, we propose a Squall framework using in-memory technology. Moreover, we provide a description of Squall framework and its operations. This Squall framework can support the real-time event stream processing and micro-batch processing, showing high performance and memory efficiency for stream processing using Go's excellent concurrency and GC (Garbage Collection) available without a virtual machine. Therefore, you can run many jobs on one machine. In addition, the data flows through the memory, the number of operation steps are incorporated to improve the performance. It provides relatively good performance compared to existing Apache Storm and spark streaming. In conclusion, it can be used as a general-purpose big data processing framework because it can overcome the drawbacks of existing Apache storm or Spark streaming by introducing the advantages of Go language.","PeriodicalId":326100,"journal":{"name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128751891","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}
In cloud application services, data are generally transmitted by multicast-based group communications to avoid transmission of duplicate packets and reduce bandwidth waste. However, the data amount in data center networks (DCNs) has increased to the scale of tremendous data over time. Tremendous data can be processed only through DCNs by using distributed computing technologies. Consequently, a large flow of multicasts has been generated. DCNs comprise a high number of commodity servers and switches, which exhibit limited capabilities to process packets. Subsequently, the rate of multicast congestion in DCNs increases substantially, resulting in severe packet loss and transmission error. Packet loss and transmission error cause transmitting devices to retransmit data, resulting in network bandwidth waste, transmission delay, and disrupted transmission of other data. Consequently, network congestion is exacerbated, and the entire multicast tree crashes. Therefore, this paper proposes an error prevention and recovery multicast, which enables areas of errors to be effectively inferred and reduces the chain effects of errors by using repair multicast trees, thereby effectively reducing the costs of reconstructing multicast trees. Simulation results show that the proposed scheme significantly improves the costs of reconstructing multicast trees and the transmission delay.
{"title":"An Efficient Error Prevention and Recovery for Multicast Traffic in Data Center Networks","authors":"Hsueh-Wen Tseng, Ting-Ting Yang, Yu-Xiang Lan","doi":"10.1145/3129676.3129699","DOIUrl":"https://doi.org/10.1145/3129676.3129699","url":null,"abstract":"In cloud application services, data are generally transmitted by multicast-based group communications to avoid transmission of duplicate packets and reduce bandwidth waste. However, the data amount in data center networks (DCNs) has increased to the scale of tremendous data over time. Tremendous data can be processed only through DCNs by using distributed computing technologies. Consequently, a large flow of multicasts has been generated. DCNs comprise a high number of commodity servers and switches, which exhibit limited capabilities to process packets. Subsequently, the rate of multicast congestion in DCNs increases substantially, resulting in severe packet loss and transmission error. Packet loss and transmission error cause transmitting devices to retransmit data, resulting in network bandwidth waste, transmission delay, and disrupted transmission of other data. Consequently, network congestion is exacerbated, and the entire multicast tree crashes. Therefore, this paper proposes an error prevention and recovery multicast, which enables areas of errors to be effectively inferred and reduces the chain effects of errors by using repair multicast trees, thereby effectively reducing the costs of reconstructing multicast trees. Simulation results show that the proposed scheme significantly improves the costs of reconstructing multicast trees and the transmission delay.","PeriodicalId":326100,"journal":{"name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","volume":"2020 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134071274","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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