Pub Date : 2011-10-13DOI: 10.1109/CCIS.2011.6045063
X. Ren, Rongheng Lin, Hua Zou
Because of the elastic service capability of cloud computing platform, more and more applications are moved here, which makes efficient load balancing into a bottleneck. Considering the unique features of long-connectivity applications which are increasingly popular nowadays, an improved algorithm is proposed based on the weighted least connection algorithm. In the new algorithm, load and processing power are quantified, and single exponential smoothing forecasting mechanism is added. Finally, the article proves by experiments that the new algorithm can reduce the server load tilt, and improve client service quality effectively.
{"title":"A dynamic load balancing strategy for cloud computing platform based on exponential smoothing forecast","authors":"X. Ren, Rongheng Lin, Hua Zou","doi":"10.1109/CCIS.2011.6045063","DOIUrl":"https://doi.org/10.1109/CCIS.2011.6045063","url":null,"abstract":"Because of the elastic service capability of cloud computing platform, more and more applications are moved here, which makes efficient load balancing into a bottleneck. Considering the unique features of long-connectivity applications which are increasingly popular nowadays, an improved algorithm is proposed based on the weighted least connection algorithm. In the new algorithm, load and processing power are quantified, and single exponential smoothing forecasting mechanism is added. Finally, the article proves by experiments that the new algorithm can reduce the server load tilt, and improve client service quality effectively.","PeriodicalId":128504,"journal":{"name":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132111430","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 : 2011-10-13DOI: 10.1109/CCIS.2011.6045027
Yuguang Huang, Lei Li
Naive Bayes algorithm is one of the most effective methods in the field of text classification, but only in the large training sample set can it get a more accurate result. The requirement of a large number of samples not only brings heavy work for previous manual classification, but also puts forward a higher request for storage and computing resources during the computer post-processing. This paper mainly studies Naïve Bayes classification algorithm based on Poisson distribution model, and the experimental results show that this method keeps high classification accuracy even in small sample set.
{"title":"Naive Bayes classification algorithm based on small sample set","authors":"Yuguang Huang, Lei Li","doi":"10.1109/CCIS.2011.6045027","DOIUrl":"https://doi.org/10.1109/CCIS.2011.6045027","url":null,"abstract":"Naive Bayes algorithm is one of the most effective methods in the field of text classification, but only in the large training sample set can it get a more accurate result. The requirement of a large number of samples not only brings heavy work for previous manual classification, but also puts forward a higher request for storage and computing resources during the computer post-processing. This paper mainly studies Naïve Bayes classification algorithm based on Poisson distribution model, and the experimental results show that this method keeps high classification accuracy even in small sample set.","PeriodicalId":128504,"journal":{"name":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124211642","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, there is appearing a multi Infrastructure as a Service (IaaS) cloud site environment. It makes server users/administrators annoying because each cloud site is managed separately by each cloud owner and they have to make use of cloud sites individually. In this paper, we propose the Multi Cloud Management Platform that locates between cloud users and cloud sites and provides unified cloud services. It can decrease workloads of server users/administrators under a multi IaaS cloud site by a service catalog federation, a collaborative management, and an application virtual server migration services. We implement a prototype system, and show our approach is feasible.
{"title":"Multi Cloud Management for unified cloud services across cloud sites","authors":"Tiancheng Liu, Yasuharu Katsuno, Kewei Sun, Ying Li, T. Kushida, Ying Chen, Mayumi Itakura","doi":"10.1109/CCIS.2011.6045053","DOIUrl":"https://doi.org/10.1109/CCIS.2011.6045053","url":null,"abstract":"Recently, there is appearing a multi Infrastructure as a Service (IaaS) cloud site environment. It makes server users/administrators annoying because each cloud site is managed separately by each cloud owner and they have to make use of cloud sites individually. In this paper, we propose the Multi Cloud Management Platform that locates between cloud users and cloud sites and provides unified cloud services. It can decrease workloads of server users/administrators under a multi IaaS cloud site by a service catalog federation, a collaborative management, and an application virtual server migration services. We implement a prototype system, and show our approach is feasible.","PeriodicalId":128504,"journal":{"name":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124235044","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 : 2011-10-13DOI: 10.1109/CCIS.2011.6045043
Kansheng Shi, Lemin Li, Haitao Liu, Jie He, Naitong Zhang, Wentao Song
Text classification has gained booming interest over the past few years. As a simple, effective and nonparametric classification method, KNN method is widely used in document classification. However, the uneven distribution in training set will affect the KNN classified result negatively. Moreover, the uneven distribution phenomenon of text is very common in documents on the Web. To tackling on this, this paper proposes an improved KNN method denoted by DBKNN. Experimental results show that the DBKNN algorithm can better serve classification requests for large sets of unevenly distributed documents.
{"title":"An improved KNN text classification algorithm based on density","authors":"Kansheng Shi, Lemin Li, Haitao Liu, Jie He, Naitong Zhang, Wentao Song","doi":"10.1109/CCIS.2011.6045043","DOIUrl":"https://doi.org/10.1109/CCIS.2011.6045043","url":null,"abstract":"Text classification has gained booming interest over the past few years. As a simple, effective and nonparametric classification method, KNN method is widely used in document classification. However, the uneven distribution in training set will affect the KNN classified result negatively. Moreover, the uneven distribution phenomenon of text is very common in documents on the Web. To tackling on this, this paper proposes an improved KNN method denoted by DBKNN. Experimental results show that the DBKNN algorithm can better serve classification requests for large sets of unevenly distributed documents.","PeriodicalId":128504,"journal":{"name":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127906397","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 : 2011-10-13DOI: 10.1109/CCIS.2011.6045044
Bin Wu, Lei Qin
Business Intelligence Platform as a software platform for information analysis is increasingly considered for its applications in the enterprises. It is widely used for User Behavior Analysis, Customer Churn Prediction, etc. However, the challenges that the traditional BI platform faces includes the tremendous volume of data, high time and space complexity of algorithms and the incompatibility in the Integration to the BI tools. In this paper, we conside the design and the implement of a BI platform architecture which is extendable in the high level and can be easily customized and integrated, that we can add specified business behavior(program) into the platform according to our given scenario, which we call Business Driven. As a system, we discuss every part of the system, in the comparison of the traditional system. Furthermore, we apply the cloud computing system into an application scenario that nearly meets real-world requirements of telecom industry by employing a large volume of data obtained from the telecom operators, and the high efficiency of the system is demonstrated.
{"title":"Design and implementation of Business-Driven BI platform based on cloud computing","authors":"Bin Wu, Lei Qin","doi":"10.1109/CCIS.2011.6045044","DOIUrl":"https://doi.org/10.1109/CCIS.2011.6045044","url":null,"abstract":"Business Intelligence Platform as a software platform for information analysis is increasingly considered for its applications in the enterprises. It is widely used for User Behavior Analysis, Customer Churn Prediction, etc. However, the challenges that the traditional BI platform faces includes the tremendous volume of data, high time and space complexity of algorithms and the incompatibility in the Integration to the BI tools. In this paper, we conside the design and the implement of a BI platform architecture which is extendable in the high level and can be easily customized and integrated, that we can add specified business behavior(program) into the platform according to our given scenario, which we call Business Driven. As a system, we discuss every part of the system, in the comparison of the traditional system. Furthermore, we apply the cloud computing system into an application scenario that nearly meets real-world requirements of telecom industry by employing a large volume of data obtained from the telecom operators, and the high efficiency of the system is demonstrated.","PeriodicalId":128504,"journal":{"name":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129209113","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 : 2011-10-13DOI: 10.1109/CCIS.2011.6045064
Chaojie Cao, Zhiqiang Zhan
As more and more IT services are provided via cloud computing technologies, businesses are worried about acceptable levels of availability and performance of applications hosted in the cloud. Since services in cloud are interdependent. An infrastructure failure may cause a number of service interruptions and result in great business losses. In a word, incident management is critical in cloud environments. Traditional incident management concerns only IT performance but overlooks business performance. In this paper, an improved incident management process for cloud computing environments is proposed based on BDIM. Experimental result shows the new process improves the business performance of the cloud computing.
{"title":"Incident management process for the cloud computing environments","authors":"Chaojie Cao, Zhiqiang Zhan","doi":"10.1109/CCIS.2011.6045064","DOIUrl":"https://doi.org/10.1109/CCIS.2011.6045064","url":null,"abstract":"As more and more IT services are provided via cloud computing technologies, businesses are worried about acceptable levels of availability and performance of applications hosted in the cloud. Since services in cloud are interdependent. An infrastructure failure may cause a number of service interruptions and result in great business losses. In a word, incident management is critical in cloud environments. Traditional incident management concerns only IT performance but overlooks business performance. In this paper, an improved incident management process for cloud computing environments is proposed based on BDIM. Experimental result shows the new process improves the business performance of the cloud computing.","PeriodicalId":128504,"journal":{"name":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128075810","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 : 2011-10-13DOI: 10.1109/CCIS.2011.6045119
Qinyan Zhang, Xiaoping Li
This paper proposes an automatic facial feature point localization method that based on calculating the similarity. In a complicated illumination condition, beard interference and small angle facial tilt, this system which mentioned in this paper is still robust. It is not necessary to train the sample set which localize the facial feature points manually. The experimental results demonstrate that this system have a good performance and high accuracy.
{"title":"An automatic facial feature point localization method","authors":"Qinyan Zhang, Xiaoping Li","doi":"10.1109/CCIS.2011.6045119","DOIUrl":"https://doi.org/10.1109/CCIS.2011.6045119","url":null,"abstract":"This paper proposes an automatic facial feature point localization method that based on calculating the similarity. In a complicated illumination condition, beard interference and small angle facial tilt, this system which mentioned in this paper is still robust. It is not necessary to train the sample set which localize the facial feature points manually. The experimental results demonstrate that this system have a good performance and high accuracy.","PeriodicalId":128504,"journal":{"name":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123740237","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 : 2011-10-13DOI: 10.1109/CCIS.2011.6045057
Jifeng Cui, C. Li, Chunxiao Xing, Yong Zhang
In distributed file systems, the integral management of large and small files is very important for the performance of applications. Based on Google File System, we present a framework of distributed file system to improve the management of geospatial objects. By adopting the access locality and spatial relationships among geospatial objects, we extend the metadata in the master node and add spatial indices in the data nodes. An optimized strategy is also proposed to unify the management of spatial data from multi-sources. Our experience shows that the method is available for managing geospatial data.
{"title":"The framework of a distributed file system for geospatial data management","authors":"Jifeng Cui, C. Li, Chunxiao Xing, Yong Zhang","doi":"10.1109/CCIS.2011.6045057","DOIUrl":"https://doi.org/10.1109/CCIS.2011.6045057","url":null,"abstract":"In distributed file systems, the integral management of large and small files is very important for the performance of applications. Based on Google File System, we present a framework of distributed file system to improve the management of geospatial objects. By adopting the access locality and spatial relationships among geospatial objects, we extend the metadata in the master node and add spatial indices in the data nodes. An optimized strategy is also proposed to unify the management of spatial data from multi-sources. Our experience shows that the method is available for managing geospatial data.","PeriodicalId":128504,"journal":{"name":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","volume":"23 49","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120926157","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 : 2011-10-13DOI: 10.1109/CCIS.2011.6045136
Lei Zhang, Zhixiong Zhao, Bin Wu, Juan Yang
In the last decade, a large number of graph mining algorithms have been proposed. But there are only a few descriptions about community structure. The communities in different network have different structure, and even in the same network the communities may have different community structure. If we can't describe the community structure reasonably, it is difficult to use the communities which are gotten from the community detection algorithms. Many community detection algorithms may have no meaning. In this paper, the community structure would be described from four different aspects. They are inside properties which describe the community in terms of the community itself, outside properties which describe the community in terms of relationship between communities, level properties which describe community in terms of relationship between the large community and the small communities which compose to the large community at different level, and dynamic properties which describe the evolution information of the communities in different time. Futher, a description algorithm based on the statistic is proposed. In this description algorithm, the community structure information can be descriped in detail and can be used for futher analysis. Also, the community structure can be described in different levels by choosing different statistic rules. A data structure is also proposed to save the community structure information for the purpose of searching it quickly.
{"title":"A description algorithm for community structure","authors":"Lei Zhang, Zhixiong Zhao, Bin Wu, Juan Yang","doi":"10.1109/CCIS.2011.6045136","DOIUrl":"https://doi.org/10.1109/CCIS.2011.6045136","url":null,"abstract":"In the last decade, a large number of graph mining algorithms have been proposed. But there are only a few descriptions about community structure. The communities in different network have different structure, and even in the same network the communities may have different community structure. If we can't describe the community structure reasonably, it is difficult to use the communities which are gotten from the community detection algorithms. Many community detection algorithms may have no meaning. In this paper, the community structure would be described from four different aspects. They are inside properties which describe the community in terms of the community itself, outside properties which describe the community in terms of relationship between communities, level properties which describe community in terms of relationship between the large community and the small communities which compose to the large community at different level, and dynamic properties which describe the evolution information of the communities in different time. Futher, a description algorithm based on the statistic is proposed. In this description algorithm, the community structure information can be descriped in detail and can be used for futher analysis. Also, the community structure can be described in different levels by choosing different statistic rules. A data structure is also proposed to save the community structure information for the purpose of searching it quickly.","PeriodicalId":128504,"journal":{"name":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123339827","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 : 2011-10-13DOI: 10.1109/CCIS.2011.6045067
Wenjun Luo, Guojing Bai
Along with variant advantages, the cloud storage gained great attention from both industry and academics since 2007. However, it also brings new challenges in creating a secure and reliable data storage and access facility over insecure or unreliable service providers. The integrity of data stored in the cloud is one of the challenges to be addressed before the novel storage model is applied widely. In this paper, we propose a remote data integrity checking protocol based on HLAs and RSA signature with the support public verifiability. The support of public verifiability makes the protocol very flexible, since the user can commission the data possession to check the TPA.
{"title":"Ensuring the data integrity in cloud data storage","authors":"Wenjun Luo, Guojing Bai","doi":"10.1109/CCIS.2011.6045067","DOIUrl":"https://doi.org/10.1109/CCIS.2011.6045067","url":null,"abstract":"Along with variant advantages, the cloud storage gained great attention from both industry and academics since 2007. However, it also brings new challenges in creating a secure and reliable data storage and access facility over insecure or unreliable service providers. The integrity of data stored in the cloud is one of the challenges to be addressed before the novel storage model is applied widely. In this paper, we propose a remote data integrity checking protocol based on HLAs and RSA signature with the support public verifiability. The support of public verifiability makes the protocol very flexible, since the user can commission the data possession to check the TPA.","PeriodicalId":128504,"journal":{"name":"2011 IEEE International Conference on Cloud Computing and Intelligence Systems","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114845045","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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