Jianhua Wang, Haozhan Wang, Fujian Xu, Jun Liu, Lianglun Cheng
High efficiency video coding (HEVC) employs quadtree coding tree unit (CTU) structure to improve its coding efficiency, but at the same time, it also requires a very high computational complexity due to its exhaustive search processes for an optimal coding unit (CU) partition. With the aim of solving the problem, a fast CU size decision optimal algorithm based on neighborhood prediction is presented for HEVC in this paper. The contribution of this paper lies in the fact that we successfully use the partition information of neighborhood CUs in different depth to quickly determine the optimal partition mode for the current CU by neighborhood prediction technology, which can save much computational complexity for HEVC with negligible RD-rate (rate-distortion rate) performance loss. Specifically, in our scheme, we use the partition information of left, up, and left-up CUs to quickly predict the optimal partition mode for the current CU by neighborhood prediction technology, as a result, our proposed algorithm can effectively solve the problem above by reducing many unnecessary prediction and partition operations for HEVC. The simulation results show that our proposed fast CU size decision algorithm based on neighborhood prediction in this paper can reduce about 19.0% coding time, and only increase 0.102% BD-rate (Bjontegaard delta rate) compared with the standard reference software of HM16.1, thus improving the coding performance of HEVC.
{"title":"A Fast CU Size Decision Optimal Algorithm Based on Neighborhood Prediction for HEVC","authors":"Jianhua Wang, Haozhan Wang, Fujian Xu, Jun Liu, Lianglun Cheng","doi":"10.3745/JIPS.04.0186","DOIUrl":"https://doi.org/10.3745/JIPS.04.0186","url":null,"abstract":"High efficiency video coding (HEVC) employs quadtree coding tree unit (CTU) structure to improve its coding efficiency, but at the same time, it also requires a very high computational complexity due to its exhaustive search processes for an optimal coding unit (CU) partition. With the aim of solving the problem, a fast CU size decision optimal algorithm based on neighborhood prediction is presented for HEVC in this paper. The contribution of this paper lies in the fact that we successfully use the partition information of neighborhood CUs in different depth to quickly determine the optimal partition mode for the current CU by neighborhood prediction technology, which can save much computational complexity for HEVC with negligible RD-rate (rate-distortion rate) performance loss. Specifically, in our scheme, we use the partition information of left, up, and left-up CUs to quickly predict the optimal partition mode for the current CU by neighborhood prediction technology, as a result, our proposed algorithm can effectively solve the problem above by reducing many unnecessary prediction and partition operations for HEVC. The simulation results show that our proposed fast CU size decision algorithm based on neighborhood prediction in this paper can reduce about 19.0% coding time, and only increase 0.102% BD-rate (Bjontegaard delta rate) compared with the standard reference software of HM16.1, thus improving the coding performance of HEVC.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131131699","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}
Wei-Che Chien, Shih-Yun Huang, Chin-Feng Lai, H. Chao
With the rapid growth of network traffic, a large number of connected devices, and higher application services, the traditional network is facing several challenges. In addition to improving the current network architecture and hardware specifications, effective resource management means the development trend of 5G. Although many existing potential technologies have been proposed to solve the some of 5G challenges, such as multipleinput multiple-output (MIMO), software-defined networking (SDN), network functions virtualization (NFV), edge computing, millimeter-wave, etc., research studies in 5G continue to enrich its function and move toward B5G mobile networks. In this paper, focusing on the resource allocation issues of 5G core networks and radio access networks, we address the latest technological developments and discuss the current challenges for resource management in 5G.
{"title":"Resource Management in 5G Mobile Networks: Survey and Challenges","authors":"Wei-Che Chien, Shih-Yun Huang, Chin-Feng Lai, H. Chao","doi":"10.3745/JIPS.03.0143","DOIUrl":"https://doi.org/10.3745/JIPS.03.0143","url":null,"abstract":"With the rapid growth of network traffic, a large number of connected devices, and higher application services, the traditional network is facing several challenges. In addition to improving the current network architecture and hardware specifications, effective resource management means the development trend of 5G. Although many existing potential technologies have been proposed to solve the some of 5G challenges, such as multipleinput multiple-output (MIMO), software-defined networking (SDN), network functions virtualization (NFV), edge computing, millimeter-wave, etc., research studies in 5G continue to enrich its function and move toward B5G mobile networks. In this paper, focusing on the resource allocation issues of 5G core networks and radio access networks, we address the latest technological developments and discuss the current challenges for resource management in 5G.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128295667","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}
Internet of Things (IoT) is a growing technology along with artificial intelligence (AI) technology. Recently, increasing cases of developing knowledge services using information collected from sensor data have been reported. Communication is required to connect the IoT and AI, and 5G mobile networks have been widely spread recently. IoT, AI services, and 5G mobile networks can be configured and used as sensor-mobile edge-server. The sensor does not send data directly to the server. Instead, the sensor sends data to the mobile edge for quick processing. Subsequently, mobile edge enables the immediate processing of data based on AI technology or by sending data to the server for processing. 5G mobile network technology is used for this data transmission. Therefore, this study examines the challenges, opportunities, and solutions used in each type of technology. To this end, this study addresses clustering, Hyperledger Fabric, data, security, machine vision, convolutional neural network, IoT and resource of 5G mobile networks. technology, digital evidence management model based on Hyperledger Fabric, aircraft recognition using machine vision, CNN model-based voting and ensemble system, hierarchical semantic clipping and sentence extraction, N-step sliding recursion formula, routing protocol for improving the lifetime of a wireless sensor network, fault diagnosis of wind power generator blade, variant malware detection techniques, resource management in 5G mobile networks, and application of blockchain in multiple fields of financial services. Futuristic and hot-issue topics from the academe and industries are described. The work mainly aims to provide hot and trendy research to researchers rapidly. stay point spatial clustering based technology; digital evidence management model based on Hyperledger Fabric; aircraft recognition using machine vision; CNN model-based voting and ensemble system; hierarchical semantic clipping and sentence extraction; N-step sliding recursion formula; routing protocol for improving the lifetime of a wireless sensor network; fault diagnosis of wind power generator blade; variant malware detection techniques; resource management in 5G mobile networks; application of blockchain in multiple fields of financial services, and so on.
{"title":"Future Trends of IoT, 5G Mobile Networks, and AI: Challenges, Opportunities, and Solutions","authors":"Ji Su Park, J. Park","doi":"10.3745/JIPS.03.0146","DOIUrl":"https://doi.org/10.3745/JIPS.03.0146","url":null,"abstract":"Internet of Things (IoT) is a growing technology along with artificial intelligence (AI) technology. Recently, increasing cases of developing knowledge services using information collected from sensor data have been reported. Communication is required to connect the IoT and AI, and 5G mobile networks have been widely spread recently. IoT, AI services, and 5G mobile networks can be configured and used as sensor-mobile edge-server. The sensor does not send data directly to the server. Instead, the sensor sends data to the mobile edge for quick processing. Subsequently, mobile edge enables the immediate processing of data based on AI technology or by sending data to the server for processing. 5G mobile network technology is used for this data transmission. Therefore, this study examines the challenges, opportunities, and solutions used in each type of technology. To this end, this study addresses clustering, Hyperledger Fabric, data, security, machine vision, convolutional neural network, IoT and resource of 5G mobile networks. technology, digital evidence management model based on Hyperledger Fabric, aircraft recognition using machine vision, CNN model-based voting and ensemble system, hierarchical semantic clipping and sentence extraction, N-step sliding recursion formula, routing protocol for improving the lifetime of a wireless sensor network, fault diagnosis of wind power generator blade, variant malware detection techniques, resource management in 5G mobile networks, and application of blockchain in multiple fields of financial services. Futuristic and hot-issue topics from the academe and industries are described. The work mainly aims to provide hot and trendy research to researchers rapidly. stay point spatial clustering based technology; digital evidence management model based on Hyperledger Fabric; aircraft recognition using machine vision; CNN model-based voting and ensemble system; hierarchical semantic clipping and sentence extraction; N-step sliding recursion formula; routing protocol for improving the lifetime of a wireless sensor network; fault diagnosis of wind power generator blade; variant malware detection techniques; resource management in 5G mobile networks; application of blockchain in multiple fields of financial services, and so on.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114262609","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 order to effectively evaluate the urban water security, the study investigates a novel system to assess factors that impact urban water security and builds an urban water poverty evaluation index system. Based on the contribution rates of Resource, Access, Capacity, Use, and Environment, the study adopts the Water Poverty Index (WPI) model to evaluate the water poverty levels of 14 cities in Gansu during 2011–2018 and uses the least variance method to evaluate water poverty space drive types. The case study results show that the water poverty space drive types of 14 cites fall into four categories. The first category is the dual factor dominant type driven by environment and resources, which includes Lanzhou, Qingyang, Jiuquan, and Jiayuguan. The second category is the three-factor dominant type driven by Access, Use, and Capability, which includes Longnan, Linxia, and Gannan. The third category is the four-factor dominant type driven by Resource, Access, Capability, and Environment, which includes Jinchang, Pingliang, Wuwei, Baiyin, and Zhangye. The fourth category is the five-factor dominant type, which includes Tianshui and Dingxi. The driven types impacting the urban water security factors reflected by the WPI and its model are clear and accurate. The divisions of the urban water security level supply a reliable theoretical and numerical basis for an urban water security early warning mechanism.
{"title":"Type Drive Analysis of Urban Water Security Factors","authors":"L. Gong, Hong Wang, C. Jin, Lili Lu, Menghan Ma","doi":"10.3745/JIPS.04.0180","DOIUrl":"https://doi.org/10.3745/JIPS.04.0180","url":null,"abstract":"In order to effectively evaluate the urban water security, the study investigates a novel system to assess factors that impact urban water security and builds an urban water poverty evaluation index system. Based on the contribution rates of Resource, Access, Capacity, Use, and Environment, the study adopts the Water Poverty Index (WPI) model to evaluate the water poverty levels of 14 cities in Gansu during 2011–2018 and uses the least variance method to evaluate water poverty space drive types. The case study results show that the water poverty space drive types of 14 cites fall into four categories. The first category is the dual factor dominant type driven by environment and resources, which includes Lanzhou, Qingyang, Jiuquan, and Jiayuguan. The second category is the three-factor dominant type driven by Access, Use, and Capability, which includes Longnan, Linxia, and Gannan. The third category is the four-factor dominant type driven by Resource, Access, Capability, and Environment, which includes Jinchang, Pingliang, Wuwei, Baiyin, and Zhangye. The fourth category is the five-factor dominant type, which includes Tianshui and Dingxi. The driven types impacting the urban water security factors reflected by the WPI and its model are clear and accurate. The divisions of the urban water security level supply a reliable theoretical and numerical basis for an urban water security early warning mechanism.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115891692","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}
{"title":"A Survey of the Application of Blockchain in Multiple Fields of Financial Services","authors":"Yiran Wang, Dae-Kyoo Kim, Dongwon Jeong","doi":"10.3745/JIPS.04.0185","DOIUrl":"https://doi.org/10.3745/JIPS.04.0185","url":null,"abstract":"","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124215676","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 refactoring is a process to restructure an existing software code while keeping its external behavior the same. Currently, various refactoring techniques are being used to develop more readable and less complex codes by improving the non-functional attributes of software. Refactoring can further improve code maintainability by applying various techniques to the source code, which in turn preserves the behavior of code. Refactoring facilitates bug removal and extends the capabilities of the program. In this paper, an exhaustive review is conducted regarding bad smells present in source code, applications of specific refactoring methods to remove that bad smell and its effect on software quality. A total of 68 studies belonging to 32 journals, 31 conferences, and 5 other sources that were published between the years 2001 and 2019 were shortlisted. The studies were analyzed based on of bad smells identified, refactoring techniques used, and their effects on software metrics. We found that “long method”, “feature envy”, and “data class” bad smells were identified or corrected in the majority of studies. “Feature envy” smell was detected in 36.66% of the total shortlisted studies. Extract class refactoring approach was used in 38.77% of the total studies, followed by the move method and extract method techniques that were used in 34.69% and 30.61% of the total studies, respectively. The effects of refactoring on complexity and coupling metrics of software were also analyzed in the majority of studies, i.e., 29 studies each. Interestingly, the majority of selected studies (41%) used large open source datasets written in Java language instead of proprietary software. At the end, this study provides future guidelines for conducting research in the field of code refactoring.
{"title":"A Systematic Literature Survey of Software Metrics, Code Smells and Refactoring Techniques","authors":"Mansi Agnihotri, A. Chug","doi":"10.3745/JIPS.04.0184","DOIUrl":"https://doi.org/10.3745/JIPS.04.0184","url":null,"abstract":"Software refactoring is a process to restructure an existing software code while keeping its external behavior the same. Currently, various refactoring techniques are being used to develop more readable and less complex codes by improving the non-functional attributes of software. Refactoring can further improve code maintainability by applying various techniques to the source code, which in turn preserves the behavior of code. Refactoring facilitates bug removal and extends the capabilities of the program. In this paper, an exhaustive review is conducted regarding bad smells present in source code, applications of specific refactoring methods to remove that bad smell and its effect on software quality. A total of 68 studies belonging to 32 journals, 31 conferences, and 5 other sources that were published between the years 2001 and 2019 were shortlisted. The studies were analyzed based on of bad smells identified, refactoring techniques used, and their effects on software metrics. We found that “long method”, “feature envy”, and “data class” bad smells were identified or corrected in the majority of studies. “Feature envy” smell was detected in 36.66% of the total shortlisted studies. Extract class refactoring approach was used in 38.77% of the total studies, followed by the move method and extract method techniques that were used in 34.69% and 30.61% of the total studies, respectively. The effects of refactoring on complexity and coupling metrics of software were also analyzed in the majority of studies, i.e., 29 studies each. Interestingly, the majority of selected studies (41%) used large open source datasets written in Java language instead of proprietary software. At the end, this study provides future guidelines for conducting research in the field of code refactoring.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134609981","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}
We propose a content-based image retrieval system that uses a combination of completed local binary pattern (CLBP) and color autocorrelogram. CLBP features are extracted on a multi-resolution multi-direction filtered domain of value component. Color autocorrelogram features are extracted in two dimensions of hue and saturation components. Experiment results revealed that the proposed method yields a lot of improvement when compared with the methods that use partial features employed in the proposed method. It is also superior to the conventional CLBP, the color autocorrelogram using R, G, and B components, and the multichannel decoded local binary pattern which is one of the latest methods.
{"title":"Content-Based Image Retrieval Using Multi-Resolution Multi-Direction Filtering-Based CLBP Texture Features and Color Autocorrelogram Features","authors":"Hee-Hyung Bu, N. Kim, B. Yun, Sung-Ho Kim","doi":"10.3745/JIPS.02.0138","DOIUrl":"https://doi.org/10.3745/JIPS.02.0138","url":null,"abstract":"We propose a content-based image retrieval system that uses a combination of completed local binary pattern (CLBP) and color autocorrelogram. CLBP features are extracted on a multi-resolution multi-direction filtered domain of value component. Color autocorrelogram features are extracted in two dimensions of hue and saturation components. Experiment results revealed that the proposed method yields a lot of improvement when compared with the methods that use partial features employed in the proposed method. It is also superior to the conventional CLBP, the color autocorrelogram using R, G, and B components, and the multichannel decoded local binary pattern which is one of the latest methods.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130148642","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 amount of malware increases exponentially every day and poses a threat to networks and operating systems. Most new malware is a variant of existing malware. It is difficult to deal with numerous malware variants since they bypass the existing signature-based malware detection method. Thus, research on automated methods of detecting and processing variant malware has been continuously conducted. This report proposes a method of extracting feature data from files and detecting malware using machine learning. Feature data were extracted from 7,000 malware and 3,000 benign files using static and dynamic malware analysis tools. A malware classification model was constructed using multiple DNN, XGBoost, and RandomForest layers and the performance was analyzed. The proposed method achieved up to 96.3% accuracy.
{"title":"A Study on Variant Malware Detection Techniques Using Static and Dynamic Features","authors":"Jinsu Kang, Yoojae Won","doi":"10.3745/JIPS.03.0145","DOIUrl":"https://doi.org/10.3745/JIPS.03.0145","url":null,"abstract":"The amount of malware increases exponentially every day and poses a threat to networks and operating systems. Most new malware is a variant of existing malware. It is difficult to deal with numerous malware variants since they bypass the existing signature-based malware detection method. Thus, research on automated methods of detecting and processing variant malware has been continuously conducted. This report proposes a method of extracting feature data from files and detecting malware using machine learning. Feature data were extracted from 7,000 malware and 3,000 benign files using static and dynamic malware analysis tools. A malware classification model was constructed using multiple DNN, XGBoost, and RandomForest layers and the performance was analyzed. The proposed method achieved up to 96.3% accuracy.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114469394","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 degree of dispersion of a random variable can be described by the variance, which reflects the distance of the random variable from its mean. However, the time complexity of the traditional variance calculation algorithm is O(n), which results from full calculation of all samples. When the number of samples increases or on the occasion of high speed signal processing, algorithms with O(n) time complexity will cost huge amount of time and that may results in performance degradation of the whole system. A novel multi-step recursive algorithm for variance calculation of the time-varying data series with O(1) time complexity (constant time) is proposed in this paper. Numerical simulation and experiments of the algorithm is presented and the results demonstrate that the proposed multi-step recursive algorithm can effectively decrease computing time and hence significantly improve the variance calculation efficiency for time-varying data, which demonstrates the potential value for time-consumption data analysis or high speed signal processing.
{"title":"N-Step Sliding Recursion Formula of Variance and Its Implementation","authors":"Lang Yu, Gang He, Ahmad Khwaja Mutahir","doi":"10.3745/JIPS.04.0182","DOIUrl":"https://doi.org/10.3745/JIPS.04.0182","url":null,"abstract":"The degree of dispersion of a random variable can be described by the variance, which reflects the distance of the random variable from its mean. However, the time complexity of the traditional variance calculation algorithm is O(n), which results from full calculation of all samples. When the number of samples increases or on the occasion of high speed signal processing, algorithms with O(n) time complexity will cost huge amount of time and that may results in performance degradation of the whole system. A novel multi-step recursive algorithm for variance calculation of the time-varying data series with O(1) time complexity (constant time) is proposed in this paper. Numerical simulation and experiments of the algorithm is presented and the results demonstrate that the proposed multi-step recursive algorithm can effectively decrease computing time and hence significantly improve the variance calculation efficiency for time-varying data, which demonstrates the potential value for time-consumption data analysis or high speed signal processing.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116899712","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 introduces page-level rewrite interval prediction (PRWIP). By recording and analyzing the memory access history at page-level, we are able to predict the future memory accesses to any pages. Leveraging this information, this paper proposes a faster incremental checkpoint design by overlapping checkpoint phase with computing phase and thus achieves higher performance. Experimental results show that our new incremental checkpoint design can achieve averagely 22% speedup over traditional incremental checkpoint and 14% over the previous state-of-the-art work.
{"title":"Fast Incremental Checkpoint Based on Page-Level Rewrite Interval Prediction","authors":"Yulei Huang","doi":"10.3745/JIPS.01.0058","DOIUrl":"https://doi.org/10.3745/JIPS.01.0058","url":null,"abstract":"This paper introduces page-level rewrite interval prediction (PRWIP). By recording and analyzing the memory access history at page-level, we are able to predict the future memory accesses to any pages. Leveraging this information, this paper proposes a faster incremental checkpoint design by overlapping checkpoint phase with computing phase and thus achieves higher performance. Experimental results show that our new incremental checkpoint design can achieve averagely 22% speedup over traditional incremental checkpoint and 14% over the previous state-of-the-art work.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128626419","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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