Pub Date : 2015-02-26DOI: 10.1109/RIVF.2015.7049871
Thi Huong Chu, Nguyen Quang Uy
Genetic Programming (GP) is an evolutionary algorithm inspired by the evolutionary process in biology. Although, GP has successfully applied to various problems, its major weakness lies in the slowness of the evolutionary process. This drawback may limit GP applications particularly in complex problems where the computational time required by GP often grows excessively as the problem complexity increases. In this paper, we propose a novel method to speed up GP based on a new implementation that can be implemented on the normal hardware of personal computers. The experiments were conducted on numerous regression problems drawn from UCI machine learning data set. The results were compared with standard GP (the traditional implementation) and an implementation based on subtree caching showing that the proposed method significantly reduces the computational time compared to the previous approaches, reaching a speedup of up to nearly 200 times.
{"title":"A new implementation to speed up Genetic Programming","authors":"Thi Huong Chu, Nguyen Quang Uy","doi":"10.1109/RIVF.2015.7049871","DOIUrl":"https://doi.org/10.1109/RIVF.2015.7049871","url":null,"abstract":"Genetic Programming (GP) is an evolutionary algorithm inspired by the evolutionary process in biology. Although, GP has successfully applied to various problems, its major weakness lies in the slowness of the evolutionary process. This drawback may limit GP applications particularly in complex problems where the computational time required by GP often grows excessively as the problem complexity increases. In this paper, we propose a novel method to speed up GP based on a new implementation that can be implemented on the normal hardware of personal computers. The experiments were conducted on numerous regression problems drawn from UCI machine learning data set. The results were compared with standard GP (the traditional implementation) and an implementation based on subtree caching showing that the proposed method significantly reduces the computational time compared to the previous approaches, reaching a speedup of up to nearly 200 times.","PeriodicalId":166971,"journal":{"name":"The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134311461","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 : 2015-02-26DOI: 10.1109/RIVF.2015.7049896
N. Tan, Nguyen Dinh Viet
Since sensor nodes are battery power constrained devices in wireless sensor network (WSN), so how to use the energy of sensor nodes efficiently to prolong the network lifetime is a chief challenge for designing routing protocols. To solve this problem, in this paper, we propose sleep scheduled and tree-based clustering approach routing algorithm (SSTBC) for energy-efficient in WSN. SSTBC preserves energy by turning off radio (entering sleep mode) of either impossible or unnecessary nodes, which observe almost the same information, base on their location information to remove redundant data. In addition, to further reduce energy dissipation of communication in network, we build minimum spanning tree with the root as the cluster head (CH) from active nodes in a cluster to forward data packets to base station (BS). Our simulation results show that the network lifetime with using of our proposed protocol can be improved about 250% and 23% compared to low-energy adaptive clustering hierarchy (LEACH) and power-efficient gathering in sensor information system (PEGASIS), respectively.
{"title":"SSTBC: Sleep scheduled and tree-based clustering routing protocol for energy-efficient in wireless sensor networks","authors":"N. Tan, Nguyen Dinh Viet","doi":"10.1109/RIVF.2015.7049896","DOIUrl":"https://doi.org/10.1109/RIVF.2015.7049896","url":null,"abstract":"Since sensor nodes are battery power constrained devices in wireless sensor network (WSN), so how to use the energy of sensor nodes efficiently to prolong the network lifetime is a chief challenge for designing routing protocols. To solve this problem, in this paper, we propose sleep scheduled and tree-based clustering approach routing algorithm (SSTBC) for energy-efficient in WSN. SSTBC preserves energy by turning off radio (entering sleep mode) of either impossible or unnecessary nodes, which observe almost the same information, base on their location information to remove redundant data. In addition, to further reduce energy dissipation of communication in network, we build minimum spanning tree with the root as the cluster head (CH) from active nodes in a cluster to forward data packets to base station (BS). Our simulation results show that the network lifetime with using of our proposed protocol can be improved about 250% and 23% compared to low-energy adaptive clustering hierarchy (LEACH) and power-efficient gathering in sensor information system (PEGASIS), respectively.","PeriodicalId":166971,"journal":{"name":"The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122096125","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 : 2015-02-26DOI: 10.1109/RIVF.2015.7049887
Chien D. C. Ta, Tuoi Phan Thi
Semantic and syntactic relations play an important role of applications in recent years, especially on Semantic Web, Information Retrieval, Information Extraction, and Question Answering. Semantic and syntactic relations content main ideas in the sentences or paragraphs. This paper presents our proposed algorithms for identifying semantic and syntactic relations between objects and their properties in order to enrich a domain specific ontology, namely Computing Domain Ontology, which is used in Information extraction system. We combine the methodologies of Natural Language Processing with Machine Learning in these proposed algorithms in order to extract the explicit and implicit relations. We exploit these relations from distinct resources, such as WordNet, Wikipedia and text documents of ACM Digital Libraries. We also use Natural Language Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to analyze and parse sentences. A random sample among 245 categories of ACM Categories is used to evaluate. Results generated show that our proposed approach achieves high precision.
{"title":"Identifying semantic and syntactic relations from text documents","authors":"Chien D. C. Ta, Tuoi Phan Thi","doi":"10.1109/RIVF.2015.7049887","DOIUrl":"https://doi.org/10.1109/RIVF.2015.7049887","url":null,"abstract":"Semantic and syntactic relations play an important role of applications in recent years, especially on Semantic Web, Information Retrieval, Information Extraction, and Question Answering. Semantic and syntactic relations content main ideas in the sentences or paragraphs. This paper presents our proposed algorithms for identifying semantic and syntactic relations between objects and their properties in order to enrich a domain specific ontology, namely Computing Domain Ontology, which is used in Information extraction system. We combine the methodologies of Natural Language Processing with Machine Learning in these proposed algorithms in order to extract the explicit and implicit relations. We exploit these relations from distinct resources, such as WordNet, Wikipedia and text documents of ACM Digital Libraries. We also use Natural Language Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to analyze and parse sentences. A random sample among 245 categories of ACM Categories is used to evaluate. Results generated show that our proposed approach achieves high precision.","PeriodicalId":166971,"journal":{"name":"The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115477088","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 : 2015-02-26DOI: 10.1109/RIVF.2015.7049866
A. C. Tran
The population age requires assistant systems to assist the elderly to live in a familiar place as long as possible. In the wide range of the smart home applications, abnormal behaviour detection is attracting researchers due to its important benefits for the safety of the elderly people. In this research, a hybrid approach to description logic learning is proposed to learn normal behaviours of the elderly in smart homes. Negation As Failure (NAF) can be later used to detect abnormalities based on the learned rules. In addition, a methodology for generating context-awareness smart home datasets based on use cases is also proposed to evaluate the learning algorithm. The experimental results show that the proposed algorithm is suited to this problem. The learning speed and scalability of the proposed algorithm are significantly better than other description logic learning algorithms used in the comparison.
{"title":"Application of description logic learning in abnormal behaviour detection in smart homes","authors":"A. C. Tran","doi":"10.1109/RIVF.2015.7049866","DOIUrl":"https://doi.org/10.1109/RIVF.2015.7049866","url":null,"abstract":"The population age requires assistant systems to assist the elderly to live in a familiar place as long as possible. In the wide range of the smart home applications, abnormal behaviour detection is attracting researchers due to its important benefits for the safety of the elderly people. In this research, a hybrid approach to description logic learning is proposed to learn normal behaviours of the elderly in smart homes. Negation As Failure (NAF) can be later used to detect abnormalities based on the learned rules. In addition, a methodology for generating context-awareness smart home datasets based on use cases is also proposed to evaluate the learning algorithm. The experimental results show that the proposed algorithm is suited to this problem. The learning speed and scalability of the proposed algorithm are significantly better than other description logic learning algorithms used in the comparison.","PeriodicalId":166971,"journal":{"name":"The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125421632","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 : 2015-02-26DOI: 10.1109/RIVF.2015.7049898
C. Doukas, Luca Capra, Fabio Antonelli, Erinda Jaupaj, A. Tamilin, Iacopo Carreras
There is an increasing number of mobile applications used as interaction interfaces with connected objects and Internet of Things (IoT) systems. Smartphones utilize established communication techniques to interact with online services but when it comes to IoT devices, lightweight bi-directional protocols need to be used. This paper describes the development of a generic Mobile SDK that enables developers to easily integrate IoT protocols (such as WebSockets and MQTT) into their applications for communication with an IoT Cloud-based environment. Two different use cases are presented that demonstrate the usability of the SDK.
{"title":"Providing generic support for IoT and M2M for mobile devices","authors":"C. Doukas, Luca Capra, Fabio Antonelli, Erinda Jaupaj, A. Tamilin, Iacopo Carreras","doi":"10.1109/RIVF.2015.7049898","DOIUrl":"https://doi.org/10.1109/RIVF.2015.7049898","url":null,"abstract":"There is an increasing number of mobile applications used as interaction interfaces with connected objects and Internet of Things (IoT) systems. Smartphones utilize established communication techniques to interact with online services but when it comes to IoT devices, lightweight bi-directional protocols need to be used. This paper describes the development of a generic Mobile SDK that enables developers to easily integrate IoT protocols (such as WebSockets and MQTT) into their applications for communication with an IoT Cloud-based environment. Two different use cases are presented that demonstrate the usability of the SDK.","PeriodicalId":166971,"journal":{"name":"The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121296433","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 : 2015-02-26DOI: 10.1109/RIVF.2015.7049868
Jean-Daniel Zucker, Y. Chevaleyre, Dao Van Sang
Discrete linear classifier is a very sparse class of decision model that has proved useful to reduce overfitting in very high dimension learning problems. However, learning discrete linear classifier is known as a difficult problem. It requires finding a discrete linear model minimizing the classification error over a given sample. A ternary classifier is a classifier defined by a pair (w, r) where w is a vector in {-1, 0, +1}n and r is a nonnegative real capturing the threshold or offset. The goal of the learning algorithm is to find a vector of weights in {-1, 0, +1}n that minimizes the hinge loss of the linear model from the training data. This problem is NP-hard and one approach consists in exactly solving the relaxed continuous problem and to heuristically derive discrete solutions. A recent paper by the authors has introduced a randomized rounding algorithm [1] and we propose in this paper more sophisticated algorithms that improve the generalization error. These algorithms are presented and their performances are experimentally analyzed. Our results show that this kind of compact model can address the complex problem of learning predictors from bioinformatics data such as metagenomics ones where the size of samples is much smaller than the number of attributes. The new algorithms presented improve the state of the art algorithm to learn ternary classifier. The source of power of this improvement is done at the expense of time complexity.
{"title":"Experimental analysis of new algorithms for learning ternary classifiers","authors":"Jean-Daniel Zucker, Y. Chevaleyre, Dao Van Sang","doi":"10.1109/RIVF.2015.7049868","DOIUrl":"https://doi.org/10.1109/RIVF.2015.7049868","url":null,"abstract":"Discrete linear classifier is a very sparse class of decision model that has proved useful to reduce overfitting in very high dimension learning problems. However, learning discrete linear classifier is known as a difficult problem. It requires finding a discrete linear model minimizing the classification error over a given sample. A ternary classifier is a classifier defined by a pair (w, r) where w is a vector in {-1, 0, +1}n and r is a nonnegative real capturing the threshold or offset. The goal of the learning algorithm is to find a vector of weights in {-1, 0, +1}n that minimizes the hinge loss of the linear model from the training data. This problem is NP-hard and one approach consists in exactly solving the relaxed continuous problem and to heuristically derive discrete solutions. A recent paper by the authors has introduced a randomized rounding algorithm [1] and we propose in this paper more sophisticated algorithms that improve the generalization error. These algorithms are presented and their performances are experimentally analyzed. Our results show that this kind of compact model can address the complex problem of learning predictors from bioinformatics data such as metagenomics ones where the size of samples is much smaller than the number of attributes. The new algorithms presented improve the state of the art algorithm to learn ternary classifier. The source of power of this improvement is done at the expense of time complexity.","PeriodicalId":166971,"journal":{"name":"The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116135391","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 : 2015-02-26DOI: 10.1109/RIVF.2015.7049885
Bui Cong Giao, D. T. Anh
The Sort-Tile-Recursive (STR) algorithm is a simple and efficient bulk-loading method for spatial or multidimensional data management using R-tree. In this paper, we put forward an approach to improve the STR algorithm for packing R-trees in indexing time series by some strategies choosing coordinates to partition spatial objects into nodes of R-trees. Every strategy has its own method to connect ends of consecutive runs into a suboptimum space-filling curve. We will compare the proposed approach with previous works in terms of space storing the index structure and runtime for range search on R-trees. Extensive experiments are carried out on many streaming time series datasets to evaluate our improved STR methods and previous methods unbiasedly and precisely. The experimental results show that the improved STR methods outperform previous methods.
{"title":"Improving Sort-Tile-Recusive algorithm for R-tree packing in indexing time series","authors":"Bui Cong Giao, D. T. Anh","doi":"10.1109/RIVF.2015.7049885","DOIUrl":"https://doi.org/10.1109/RIVF.2015.7049885","url":null,"abstract":"The Sort-Tile-Recursive (STR) algorithm is a simple and efficient bulk-loading method for spatial or multidimensional data management using R-tree. In this paper, we put forward an approach to improve the STR algorithm for packing R-trees in indexing time series by some strategies choosing coordinates to partition spatial objects into nodes of R-trees. Every strategy has its own method to connect ends of consecutive runs into a suboptimum space-filling curve. We will compare the proposed approach with previous works in terms of space storing the index structure and runtime for range search on R-trees. Extensive experiments are carried out on many streaming time series datasets to evaluate our improved STR methods and previous methods unbiasedly and precisely. The experimental results show that the improved STR methods outperform previous methods.","PeriodicalId":166971,"journal":{"name":"The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116502546","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 : 2015-02-26DOI: 10.1109/RIVF.2015.7049897
M. V. Moreno, A. Skarmeta
Human behavior modeling and activity interpretation have been of increasing interest in the Information Society for long time. Internet of Things technologies enhance the situational awareness or “smartness” of service providers and consumers alike. On the other hand, user-centric sensing systems are ideal candidates for ubiquitous observation purposes thanks to the indispensable role of mobile phones in everyday life. This paper presents a novel approach for mobile phone centric observation applied to indoor localization for smart buildings. The goal is to provide accurate localization data which can be used for offering customized IoT-based services in buildings. Unlike existing work which uses the intensity of magnetic field for fingerprinting, our approach uses all three components of the measured magnetic field vectors to achieve accurate results of localization. The resulting localization system does not rely on any infrastructure devices and is therefore easy to manage and deploy. Our approach covers, with test, comparison and justified selection, every tools and methods necessary to implement a genuine experiment in a real building which gives order of a few meters precision.
{"title":"An indoor localization system based on 3D magnetic fingerprints for smart buildings","authors":"M. V. Moreno, A. Skarmeta","doi":"10.1109/RIVF.2015.7049897","DOIUrl":"https://doi.org/10.1109/RIVF.2015.7049897","url":null,"abstract":"Human behavior modeling and activity interpretation have been of increasing interest in the Information Society for long time. Internet of Things technologies enhance the situational awareness or “smartness” of service providers and consumers alike. On the other hand, user-centric sensing systems are ideal candidates for ubiquitous observation purposes thanks to the indispensable role of mobile phones in everyday life. This paper presents a novel approach for mobile phone centric observation applied to indoor localization for smart buildings. The goal is to provide accurate localization data which can be used for offering customized IoT-based services in buildings. Unlike existing work which uses the intensity of magnetic field for fingerprinting, our approach uses all three components of the measured magnetic field vectors to achieve accurate results of localization. The resulting localization system does not rely on any infrastructure devices and is therefore easy to manage and deploy. Our approach covers, with test, comparison and justified selection, every tools and methods necessary to implement a genuine experiment in a real building which gives order of a few meters precision.","PeriodicalId":166971,"journal":{"name":"The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117119492","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 : 2015-02-26DOI: 10.1109/RIVF.2015.7049865
N. T. Son, Nguyễn Thụy Phương Duyên, H. Quoc, Le-Minh Nguyen
Analyzing the structure of legal sentences in legal document is an important phase to build a knowledge management system in Legal Engineering. This paper proposes a new approach to recognize logical parts in Vietnamese legal documents based on a statistic machine learning method - Conditional Random Fields. Beside linguistic features such as word features, part of speech features, we use semantic features of logical parts such as trigger features and ontology features to improve the result of the annotation system. Experiments were conducted in a Vietnamese Business Law data set and obtained 78.12% at precision and 68.72% at recall measure. Compare to state-of-the-art systems, it improves the result for recognizing some logical parts.
{"title":"Recognizing logical parts in Vietnamese legal texts using Conditional Random Fields","authors":"N. T. Son, Nguyễn Thụy Phương Duyên, H. Quoc, Le-Minh Nguyen","doi":"10.1109/RIVF.2015.7049865","DOIUrl":"https://doi.org/10.1109/RIVF.2015.7049865","url":null,"abstract":"Analyzing the structure of legal sentences in legal document is an important phase to build a knowledge management system in Legal Engineering. This paper proposes a new approach to recognize logical parts in Vietnamese legal documents based on a statistic machine learning method - Conditional Random Fields. Beside linguistic features such as word features, part of speech features, we use semantic features of logical parts such as trigger features and ontology features to improve the result of the annotation system. Experiments were conducted in a Vietnamese Business Law data set and obtained 78.12% at precision and 68.72% at recall measure. Compare to state-of-the-art systems, it improves the result for recognizing some logical parts.","PeriodicalId":166971,"journal":{"name":"The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)","volume":"CE-31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126545629","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 : 2015-02-26DOI: 10.1109/RIVF.2015.7049894
L. Pham, A. Tchana, D. Donsez, Vincent Zurczak, Pierre-Yves Gibello, N. D. Palma
Cloud computing is nowadays a popular technology for hosting IT services. However, deploying and reconfiguring complex applications involving multiple software components, which are distributed on many virtual machines running on single or multi-cloud platforms, is error-prone and time-consuming for human administrators. Existing deployment frameworks are most of the time either dedicated to a unique type of applica- tion (e.g. JEE applications) or address a single cloud platform (e.g. Amazon EC2). This paper presents a novel distributed application management framework for multi-cloud platforms. It provides a Domain Specific Language (DSL) which allows to describe applications and their execution environments (cloud platforms) in a hierarchical way in order to provide a fine-grained management. This framework implements an asynchronous and parallel deployment protocol which accelerates and make resilient the deployment process. A prototype has been developed to serve conducting intensive experiments with different type of applications (e.g. OSGi application and ubiquitous big data analytics for IoT) over disparate cloud models (e.g. private, hybrid, and multi-cloud), which validate the genericity of the framework. These experiments also demonstrate its efficiency comparing to existing frameworks such as Cloudify.
{"title":"An adaptable framework to deploy complex applications onto multi-cloud platforms","authors":"L. Pham, A. Tchana, D. Donsez, Vincent Zurczak, Pierre-Yves Gibello, N. D. Palma","doi":"10.1109/RIVF.2015.7049894","DOIUrl":"https://doi.org/10.1109/RIVF.2015.7049894","url":null,"abstract":"Cloud computing is nowadays a popular technology for hosting IT services. However, deploying and reconfiguring complex applications involving multiple software components, which are distributed on many virtual machines running on single or multi-cloud platforms, is error-prone and time-consuming for human administrators. Existing deployment frameworks are most of the time either dedicated to a unique type of applica- tion (e.g. JEE applications) or address a single cloud platform (e.g. Amazon EC2). This paper presents a novel distributed application management framework for multi-cloud platforms. It provides a Domain Specific Language (DSL) which allows to describe applications and their execution environments (cloud platforms) in a hierarchical way in order to provide a fine-grained management. This framework implements an asynchronous and parallel deployment protocol which accelerates and make resilient the deployment process. A prototype has been developed to serve conducting intensive experiments with different type of applications (e.g. OSGi application and ubiquitous big data analytics for IoT) over disparate cloud models (e.g. private, hybrid, and multi-cloud), which validate the genericity of the framework. These experiments also demonstrate its efficiency comparing to existing frameworks such as Cloudify.","PeriodicalId":166971,"journal":{"name":"The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128001143","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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