Pub Date : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943132
Harsh Lal, Gaurav Pahwa
Root cause analysis (RCA) is a systematic process for identifying “root causes” of problems or events and an approach for responding to them. The factor that caused a problem or defect should be permanently eliminated through process improvement. In the context of Software development process it may be used to refer to a specific module or a category of bug which in turn can be useful for tackling the problem at its root. In this paper we propose a machine learning approach for finding root cause of a newly filed software bugs which in turn would help in the faster and cleaner resolution of software bugs. This proposed approach is evaluated for feasibility study on an open source system eclipse. [7], [6]
{"title":"Root cause analysis of software bugs using machine learning techniques","authors":"Harsh Lal, Gaurav Pahwa","doi":"10.1109/CONFLUENCE.2017.7943132","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943132","url":null,"abstract":"Root cause analysis (RCA) is a systematic process for identifying “root causes” of problems or events and an approach for responding to them. The factor that caused a problem or defect should be permanently eliminated through process improvement. In the context of Software development process it may be used to refer to a specific module or a category of bug which in turn can be useful for tackling the problem at its root. In this paper we propose a machine learning approach for finding root cause of a newly filed software bugs which in turn would help in the faster and cleaner resolution of software bugs. This proposed approach is evaluated for feasibility study on an open source system eclipse. [7], [6]","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"133 1","pages":"105-111"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80451761","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943143
Arvinder Kaur, A. Agrawal
Enhancing the software by either adding new functionality or deleting some obsolete capability or fixing the errors is called software maintenance. As a result, the software may function improperly or unchanged parts of the software may be adversely affected. Testing carried out to validate that no new errors have been introduced during maintenance activity is called Regression Testing. It is acknowledged to be an expensive activity and may account for around 60–70% of the total software life cycle cost. Reducing the cost of regression testing is therefore of vital importance and has the caliber to reduce the cost of maintenance also. This paper evaluates the performance of two metaheuristic algorithms-Bat Algorithm and Cuckoo Search Algorithm for selecting test cases. Factors that we have considered for performance evaluation are the number of faults detected and the execution time. The domain of study is the flex object from the Benchmark repository — Software Artifact and Infrastructure Repository. Extensive experiments have been conducted to collect and analyze the results. A Statistical test, F-test has also been conducted to validate the research hypothesis. Results indicate that the Cuckoo Search Algorithms perform a little better than Bat Algorithm.
{"title":"A comparative study of Bat and Cuckoo search algorithm for regression test case selection","authors":"Arvinder Kaur, A. Agrawal","doi":"10.1109/CONFLUENCE.2017.7943143","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943143","url":null,"abstract":"Enhancing the software by either adding new functionality or deleting some obsolete capability or fixing the errors is called software maintenance. As a result, the software may function improperly or unchanged parts of the software may be adversely affected. Testing carried out to validate that no new errors have been introduced during maintenance activity is called Regression Testing. It is acknowledged to be an expensive activity and may account for around 60–70% of the total software life cycle cost. Reducing the cost of regression testing is therefore of vital importance and has the caliber to reduce the cost of maintenance also. This paper evaluates the performance of two metaheuristic algorithms-Bat Algorithm and Cuckoo Search Algorithm for selecting test cases. Factors that we have considered for performance evaluation are the number of faults detected and the execution time. The domain of study is the flex object from the Benchmark repository — Software Artifact and Infrastructure Repository. Extensive experiments have been conducted to collect and analyze the results. A Statistical test, F-test has also been conducted to validate the research hypothesis. Results indicate that the Cuckoo Search Algorithms perform a little better than Bat Algorithm.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"67 1","pages":"164-170"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81149443","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943229
Seema Sharma, D. Mehrotra
Non-communicable diseases have become a major health concern in India. Chronic Kidney Disease (CKD) a non-communicable disease is one of the major causes of death. Identifying CKD at early stage is better to slow down the growth of disease that also decreases the chances of other complication. The aim of this work is to develop CBR application for diagnosis of chronic kidney disease and uses similarity-based retrieval of case results. CBR is a field of artificial intelligence where one solves the problem based upon the past cases. A CKD diagnostic prototype was developed using jCOLIBIRI framework. As part of prototyping, we studied functionality and usage of the jCOLIBIRI framework.
{"title":"Building CBR based diagnosis system using jCOLIBRI","authors":"Seema Sharma, D. Mehrotra","doi":"10.1109/CONFLUENCE.2017.7943229","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943229","url":null,"abstract":"Non-communicable diseases have become a major health concern in India. Chronic Kidney Disease (CKD) a non-communicable disease is one of the major causes of death. Identifying CKD at early stage is better to slow down the growth of disease that also decreases the chances of other complication. The aim of this work is to develop CBR application for diagnosis of chronic kidney disease and uses similarity-based retrieval of case results. CBR is a field of artificial intelligence where one solves the problem based upon the past cases. A CKD diagnostic prototype was developed using jCOLIBIRI framework. As part of prototyping, we studied functionality and usage of the jCOLIBIRI framework.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"35 1","pages":"634-638"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86556267","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943173
Kenn Mark K. Escaran, Robert R. Roxas
This paper presents a Visual Programming Environment that uses the Cyber-Film approach in programming some computational tasks. The film for the multistage network algorithm was developed as one of the films together with its template code in assembly language format. The generated executable code was run, and it was verified to run perfectly. The results show that Cyber-Film is a very promising approach in solving computational problems.
{"title":"Rendering the multistage network algorithm in cyber-film format and its code generation","authors":"Kenn Mark K. Escaran, Robert R. Roxas","doi":"10.1109/CONFLUENCE.2017.7943173","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943173","url":null,"abstract":"This paper presents a Visual Programming Environment that uses the Cyber-Film approach in programming some computational tasks. The film for the multistage network algorithm was developed as one of the films together with its template code in assembly language format. The generated executable code was run, and it was verified to run perfectly. The results show that Cyber-Film is a very promising approach in solving computational problems.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"8 1","pages":"344-349"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90075146","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943166
G. Khan, S. Sengupta, A. Sarkar
Cloud computing refers to a distributive model that deliver web services over the network dynamically so that the consumers can access the services from anywhere on demand basis depending on the Quality of Service (QoS) requirements. Nowadays, Agent based Cloud computing technology plays a vital role in modeling of roles, collaborations and interactions among the cloud components and their services. Enterprise Cloud Bus System (ECBS) is such an Agent-based Cloud computing model that helps to deliver services in a virtualized manner by optimizing the QoS parameters. This model also helps the enterprise software applications to be more reliable and robust in recent days. Design and Modeling of Multi-Agent based Cloud System dynamics has become a challenging domain in recent trends. In our previous work, we have modeled the system dynamics using UML 2.0 which focus on interactions and collaborations of cloud bus components. But, the interactions and collaborations of cloud bus components using UML extension cannot be considered as semantically rich conceptual and formalized model for behavioral analysis and design of such system. To conceptualize the dynamic facets of ECBS this paper deals with modeling of roles, interactions and collaborations of Multi-agent based Inter-cloud bus components. In this paper, a graph semantic based approach called Multi-Agent Cloud Bus Architecture Graph (MACBAG) has been proposed for effective modeling and design of dynamic aspects of such MAS based Inter-cloud architecture.
云计算指的是一种分布式模型，它通过网络动态地交付web服务，这样消费者就可以根据服务质量(QoS)需求从任何地方按需访问服务。目前，基于Agent的云计算技术在云组件及其服务之间的角色建模、协作和交互方面起着至关重要的作用。企业云总线系统(Enterprise Cloud Bus System, ECBS)就是这样一种基于代理的云计算模型，它通过优化QoS参数，以虚拟化的方式提供服务。该模型还帮助企业软件应用程序在最近变得更加可靠和健壮。基于多智能体的云系统动力学设计与建模已成为一个具有挑战性的领域。在我们之前的工作中，我们已经使用UML 2.0建模了系统动力学，它关注于云总线组件的交互和协作。但是，使用UML扩展的云总线组件之间的交互和协作不能被认为是语义丰富的概念性和形式化的模型，用于此类系统的行为分析和设计。为了概念化ECBS的动态方面，本文讨论了基于多代理的云间总线组件的角色、交互和协作的建模。本文提出了一种基于多代理云总线架构图(MACBAG)的基于图语义的方法，用于有效地建模和设计这种基于MAS的云间架构的动态方面。
{"title":"A graph semantic based approach for modeling of enterprise cloud bus system dynamics","authors":"G. Khan, S. Sengupta, A. Sarkar","doi":"10.1109/CONFLUENCE.2017.7943166","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943166","url":null,"abstract":"Cloud computing refers to a distributive model that deliver web services over the network dynamically so that the consumers can access the services from anywhere on demand basis depending on the Quality of Service (QoS) requirements. Nowadays, Agent based Cloud computing technology plays a vital role in modeling of roles, collaborations and interactions among the cloud components and their services. Enterprise Cloud Bus System (ECBS) is such an Agent-based Cloud computing model that helps to deliver services in a virtualized manner by optimizing the QoS parameters. This model also helps the enterprise software applications to be more reliable and robust in recent days. Design and Modeling of Multi-Agent based Cloud System dynamics has become a challenging domain in recent trends. In our previous work, we have modeled the system dynamics using UML 2.0 which focus on interactions and collaborations of cloud bus components. But, the interactions and collaborations of cloud bus components using UML extension cannot be considered as semantically rich conceptual and formalized model for behavioral analysis and design of such system. To conceptualize the dynamic facets of ECBS this paper deals with modeling of roles, interactions and collaborations of Multi-agent based Inter-cloud bus components. In this paper, a graph semantic based approach called Multi-Agent Cloud Bus Architecture Graph (MACBAG) has been proposed for effective modeling and design of dynamic aspects of such MAS based Inter-cloud architecture.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"1 1","pages":"298-305"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86267950","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}
These days' data mining is an emerging trend, which is presently used in different areas especially in student educational and learning analytics. It is very hard and time consuming to analyze data and finding the hidden information manually. To improvise educational data mining, clustering will be used in the paper. As we need to improvise performance as well as unambiguousness of obtained models. We have used 84 under-graduate student data and grouped students according to their final marks they achieved in the course and this we have done by using clustering approach. The result which we get shows that the clarity of specific model is much better than the general model and the unambiguousness of the model is also increase.
{"title":"Educational data mining and learning analysis","authors":"Akansha Mishra, Rashi Bansal, Shailendra Narayan Singh","doi":"10.1109/CONFLUENCE.2017.7943201","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943201","url":null,"abstract":"These days' data mining is an emerging trend, which is presently used in different areas especially in student educational and learning analytics. It is very hard and time consuming to analyze data and finding the hidden information manually. To improvise educational data mining, clustering will be used in the paper. As we need to improvise performance as well as unambiguousness of obtained models. We have used 84 under-graduate student data and grouped students according to their final marks they achieved in the course and this we have done by using clustering approach. The result which we get shows that the clarity of specific model is much better than the general model and the unambiguousness of the model is also increase.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"52 1","pages":"491-494"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84863282","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943209
Triveni Mishra, G. Raj
Web Services Computing is a growing field with a vast potential for applications of business process management by the implementation of what is known as Service Oriented Architecture (SOA). Business activities can now be independently harvested and grown and later extracted from the “toolkit” of services for composition into large scale applications. This powerful architectural style has translated business logic into useful Web Services or Web APIs across the World Wide Web. This paper delves into the reason for continuously finding and implementing better Quality of Service parameters to define Quality Standards for Web Services. Apart from revisiting research in QoS parameters for Web Services, it is attempted to provide a better understanding of current techniques in Web Service selection, prediction and ranking. A research effort towards a model for Web Service Recommendation is proposed and critically analysed considering the possibility of its implementation in the future.
{"title":"QoS implementation in Web Services selection and ranking using data analysis","authors":"Triveni Mishra, G. Raj","doi":"10.1109/CONFLUENCE.2017.7943209","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943209","url":null,"abstract":"Web Services Computing is a growing field with a vast potential for applications of business process management by the implementation of what is known as Service Oriented Architecture (SOA). Business activities can now be independently harvested and grown and later extracted from the “toolkit” of services for composition into large scale applications. This powerful architectural style has translated business logic into useful Web Services or Web APIs across the World Wide Web. This paper delves into the reason for continuously finding and implementing better Quality of Service parameters to define Quality Standards for Web Services. Apart from revisiting research in QoS parameters for Web Services, it is attempted to provide a better understanding of current techniques in Web Service selection, prediction and ranking. A research effort towards a model for Web Service Recommendation is proposed and critically analysed considering the possibility of its implementation in the future.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"2 1","pages":"537-542"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87594251","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943219
Neha Agarwal, Rajat Gupta, S. Singh, V. Saxena
It is a challenging task to find video of interests on YouTube due to huge size of its repository. Multiple labels, if provided, can make search faster. This paper describes a two level automated mechanism to generate multiple labels for videos using their text based meta-data features. The first level of classification categorize videos into 5 harassment categories and then a second level generate a positive or negative label i.e. harassment or non-harassment. There has been no multi level classification of YouTube videos. Previous works have classified videos on a single level only whereas our work brings novelty to the approach by classifying videos into multi labels. Such a work can be useful for law enforcement and intelligence agencies to identify the unwanted and malicious videos on the Internet. The proposed approach has successfully generated multiple labels for unlabelled test videos.
{"title":"Metadata based multi-labelling of YouTube videos","authors":"Neha Agarwal, Rajat Gupta, S. Singh, V. Saxena","doi":"10.1109/CONFLUENCE.2017.7943219","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943219","url":null,"abstract":"It is a challenging task to find video of interests on YouTube due to huge size of its repository. Multiple labels, if provided, can make search faster. This paper describes a two level automated mechanism to generate multiple labels for videos using their text based meta-data features. The first level of classification categorize videos into 5 harassment categories and then a second level generate a positive or negative label i.e. harassment or non-harassment. There has been no multi level classification of YouTube videos. Previous works have classified videos on a single level only whereas our work brings novelty to the approach by classifying videos into multi labels. Such a work can be useful for law enforcement and intelligence agencies to identify the unwanted and malicious videos on the Internet. The proposed approach has successfully generated multiple labels for unlabelled test videos.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"112 1","pages":"586-590"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87663308","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943126
Rupal Bhargava, Yashvardhan Sharma
Sentiment Analysis has been a keen research area for past few years. Though much of the exploration that has been done supports English language only. This paper proposes a method using which one can analyze different languages to find sentiments in them and perform sentiment analysis. The method leverages different techniques of machine learning to analyze the text. Machine translation is used in the system to provide with the feature of dealing with different languages. After the machine translation, text is processed for finding the sentiments in the text. With the advent of blogs, forums and online reviews there is substantial text present on internet that can be used to analyze the sentiment about a particular subject or an object. Hence to reduce the processing it is beneficial to extract the important text present in it. So the system proposed uses text summarization process to extract important parts of text and then uses it to analyze the sentiments about the particular subject and its aspects.
{"title":"MSATS: Multilingual sentiment analysis via text summarization","authors":"Rupal Bhargava, Yashvardhan Sharma","doi":"10.1109/CONFLUENCE.2017.7943126","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943126","url":null,"abstract":"Sentiment Analysis has been a keen research area for past few years. Though much of the exploration that has been done supports English language only. This paper proposes a method using which one can analyze different languages to find sentiments in them and perform sentiment analysis. The method leverages different techniques of machine learning to analyze the text. Machine translation is used in the system to provide with the feature of dealing with different languages. After the machine translation, text is processed for finding the sentiments in the text. With the advent of blogs, forums and online reviews there is substantial text present on internet that can be used to analyze the sentiment about a particular subject or an object. Hence to reduce the processing it is beneficial to extract the important text present in it. So the system proposed uses text summarization process to extract important parts of text and then uses it to analyze the sentiments about the particular subject and its aspects.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"23 1","pages":"71-76"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89674142","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943190
R. Tanwar, K. Gupta, S. Chowdhary, Abhishek Srivastava, M. Papoutsidakis
Now a day, traffic over network is increasing day by day as increase in population and advancement of technology. In today's world, virtualization is spreading very drastically and everyone using internet as network for downloading information in any format like video, audio, text etc, due to which network congestion is also increasing in same ratio for downloading. This congestion is due to the centralization i.e. server where the information is stored will act as a centralized server and everyone across the globe will download from the same server which result in uncountable request and become a cause of network congestion at server side. To resolve such issue, we proposed an approach in which instead of downloading from main server, the same file or information get download from the user who have already downloaded i.e. making the downloading of information distributed over the network. This approach is very helpful in case, if failure of server happens then information gets shared or gets downloaded from distributed sources. This will reduce network congestion very easily and also fasten the downloading time.
{"title":"Decentralized content downloading service: Intelligent way of traffic congestion control","authors":"R. Tanwar, K. Gupta, S. Chowdhary, Abhishek Srivastava, M. Papoutsidakis","doi":"10.1109/CONFLUENCE.2017.7943190","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943190","url":null,"abstract":"Now a day, traffic over network is increasing day by day as increase in population and advancement of technology. In today's world, virtualization is spreading very drastically and everyone using internet as network for downloading information in any format like video, audio, text etc, due to which network congestion is also increasing in same ratio for downloading. This congestion is due to the centralization i.e. server where the information is stored will act as a centralized server and everyone across the globe will download from the same server which result in uncountable request and become a cause of network congestion at server side. To resolve such issue, we proposed an approach in which instead of downloading from main server, the same file or information get download from the user who have already downloaded i.e. making the downloading of information distributed over the network. This approach is very helpful in case, if failure of server happens then information gets shared or gets downloaded from distributed sources. This will reduce network congestion very easily and also fasten the downloading time.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"41 1","pages":"437-440"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76796864","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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