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2014 International Conference on Artificial,Intelligence and Software Engineering(AISE 2014)

作者： Nipat Jongsawat Wichian Premchaiswadi

This work addresses the application of a Map Reduce based distributed computing framework, hadoop, to Bayesian network model to speed up the Bayesian update and inference processes. We present an analytical framework for understanding the transformation of Bayesian network model to Map and Reduce tasks. Computer-based Patient Case Simulation system(422 nodes) is chosen as a case study for the transformation.

关键词： hadoop MapR educe hadoop distributed file system Bayesian network Bayesian Inference

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Concentric layout, a new scientific data layout for matrix data-set in hadoop file system

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND distributed systemS 2013年第5期28卷 407-433页

作者： Wang, Jun Cheng, Lu Wang, Lizhe Univ Cent Florida Dept Elect Engn & Comp Sci 4000 Cent Florida Blvd Orlando FL 32816 USA Chinese Acad Sci Ctr Earth Observat & Digital Earth Beijing 100094 Peoples R China

Due to the explosive growth in the size of scientific data-sets, data-intensive computing and analysing are an emerging trend in computational science. In these applications, data pre-processing is widely adopted because it can optimise the data layout or format beforehand to facilitate the future data access. On the other hand, current research shows an increasing popularity of MapReduce framework for large-scale data processing. However, the data access patterns which are generally applied to scientific data-set are not supported by current MapReduce framework directly. This gap motivates us to provide support for these scientific data access patterns in MapReduce framework. In our work, we study the data access patterns in matrix files and propose a new concentric data layout solution to facilitate matrix data access and analysis in MapReduce framework. Concentric data layout is a data layout which maintains the dimensional property in chunk level. Contrary to the continuous data layout adopted in the current hadoop framework, concentric data layout stores the data from the same sub-matrix into one chunk. This layout can guarantee that the average performance of data access is optimal regardless of the various access patterns. The concentric data layout requires reorganising the data before it is being analysed or processed. Our experiments are launched on a real-world halo-finding application;the results indicate that the concentric data layout improves the overall performance by up to 38%.

关键词： data access pattern hadoop distributed file system matrix file data layout

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Securing data services: a security architecture design for private storage cloud based on HDFS

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2013年第4期4卷 242-254页

作者： Shen, Qingni Yang, Yahui Wu, Zhonghai Wang, Dandan Long, Min Peking Univ Sch Software & Microelect MoE Key Lab Network & Software Assurance Beijing 100871 Peoples R China IBM Corp IBM China Syst & Technol Lab CSTL Shanghai Peoples R China

With the growth of business, an enterprise would like to make its PSC (private storage cloud) approach an infrastructure service in a partner/public cloud. In such PSCs, there are some new data security issues, First, how to keep the data rest in the PSC isolated from internal and external attackers;second, how to make secure intra-cloud data migration within the enterprise;third, how to secure inter-cloud data migrating between the PSC and the partner/public cloud. In this paper, we propose an architecture design for enforcing data security services on the layer of HDFS in the PSC, including secure data isolation service, secure intra-cloud data migration service, and secure inter-cloud data migration service. Finally, it gives the prototype implemented as pluggable security modules in accord with our custom security policies through AOP (AspectOriented Programming) method. The time cost is given and evaluated efficiently.

关键词： private storage cloud data isolation intra-cloud data migration inter-cloud data migration security architecture HDFS hadoop distributed file system

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A novel approach to data deduplication over the engineering-oriented cloud systems

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INTEGRATED COMPUTER-AIDED ENGINEERING 2013年第1期20卷 45-57页

作者： Sun, Zhe Shen, Jun Yong, Jianming Univ Wollongong Sch Informat Syst & Technol Wollongong NSW Australia Huaneng Shandong Shidao Bay Nucl Power Co Ltd Informat Management Ctr Longcheng Shandong Peoples R China Univ So Queensland Sch Informat Syst Toowoomba Qld 4350 Australia

This paper presents a duplication-less storage system over the engineering-oriented cloud computing platforms. Our deduplication storage system, which manages data and duplication over the cloud system, consists of two major components, a front-end deduplication application and a mass storage system as back-end. hadoop distributed file system (HDFS) is a common distribution file system on the cloud, which is used with hadoop database (HBase). We use HDFS to build up a mass storage system and employ HBase to build up a fast indexing system. With a deduplication application, a scalable and parallel deduplicated cloud storage system can be effectively built up. We further use VMware to generate a simulated cloud environment. The simulation results demonstrate that our deduplication storage system is sufficiently accurate and efficient for distributed and cooperative data intensive engineering applications.

关键词： Cloud storage data deduplication hadoop distributed file system hadoop database

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A Cloud Computing Architectural Model Based on Virtual Machine Pool

A Cloud Computing Architectural Model Based on Virtual Machi...

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International Forum on Materials Science and Industrial Technology (IFMSIT 2013)

作者： Jiang, Wuxue Hu, Hui Yin, Pengfei Luo, Jianfeng Dongguan Polytech Dept Comp Engn Dongguan 523808 Guangdong Peoples R China Huizhou Univ Dept Sci & Res Guangdong 516007 Peoples R China Jishou Univ Deans Office Hunan 416000 Peoples R China

ISBN: (纸本)9783037858417

Cloud computing is a new-type of computing model. This paper deeply expounded the key technology of virtualization and cloud computing through theoretical analysis and practice research on cloud computing and its architecture. Then, it proposed a common cloud computing architecture model based on virtual environment, which consisted of layers of data center, cloud file system, computing model, data management, and application development. In addition, it realized a cloud computing platform based on hadoop.

关键词： cloud computing virtual machine pool architectural model hadoop distributed file system

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Big Data: Mining of Log file through hadoop

Big Data: Mining of Log File through Hadoop

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International Conference on Human Computer Interactions (ICHCI)

作者： Kotiyal, Bina Kumar, Ankit Pant, Bhaskar Goudar, R. H. GEU Univ Dept Comp Sci Bell Rd Dehra Dun Uttarakhand India GEU Univ Dept Informat Technol Bell Rd Dehra Dun Uttarakhand India

ISBN: (纸本)9781467357029

The unremitting increase of computational strength has produced tremendous flow of data in the past two decades. This tremendous flow of data is known as "big data". Big data is the data which cannot be processed with the aid of existing tools or techniques and if processed can result in interesting information's such as analysing the behaviour of the user, business intelligence etc. This paper discusses the difference between the traditional relational database and big data;it also shows the characteristics of big data. The paper also focuses on the distinct big data channels processes along with the various challenges and as well as on how big data is a solution to the organizations. Big data does not only focus to store and handle the large volume of data but also to analysed and extract the correct information from the data in lesser time span. At last it discusses about hadoop an open source framework that allows the distributed processing for massive datasets on cluster of computers which is shown with using the log file for extraction of information based on user query.

关键词： Big Data Web Log file hadoop hadoop distributed file system distributed Processing MapReduce

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Big Data: Issues, Challenges, Tools and Good Practices

Big Data: Issues, Challenges, Tools and Good Practices

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6th International Conference on Contemporary Computing (IC3)

作者： Katal, Avita Wazid, Mohammad Goudar, R. H. Graph Era Univ Dept CSE Dehra Dun Uttar Pradesh India

ISBN: (纸本)9781479901920

Big data is defined as large amount of data which requires new technologies and architectures so that it becomes possible to extract value from it by capturing and analysis process. Due to such large size of data it becomes very difficult to perform effective analysis using the existing traditional techniques. Big data due to its various properties like volume, velocity, variety, variability, value and complexity put forward many challenges. Since Big data is a recent upcoming technology in the market which can bring huge benefits to the business organizations, it becomes necessary that various challenges and issues associated in bringing and adapting to this technology are brought into light. This paper introduces the Big data technology along with its importance in the modern world and existing projects which are effective and important in changing the concept of science into big science and society too. The various challenges and issues in adapting and accepting Big data technology, its tools (hadoop) are also discussed in detail along with the problems hadoop is facing. The paper concludes with the Good Big data practices to be followed.

关键词： Big data hadoop hadoop distributed file system MapReduce

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Solving the NP-Hard Computational Problem in Bayesian Networks using Apache hadoop MapReduce

Solving the NP-Hard Computational Problem in Bayesian Networ...

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11th International Conference on ICT and Knowledge Engineering (ICT and KE)

作者： Jongsawat, Nipat Premchaiswadi, Wichian Siam Univ Grad Sch Informat Technol Bangkok Thailand

ISBN: (纸本)9781479922574

The problem of exact probabilistic inference in an arbitrary Bayes network is NP-hard. The process is time consuming and complex. To speed up the processing, we need to run parts of the subnetwork in parallel. This work addresses the application of a MapReduce based distributed computing framework, hadoop, to Bayesian network model to speed up the Bayesian update and inference processes. We present an analytical framework for understanding the transformation of Bayesian network model to Map and Reduce tasks. Computerbased Patient Case Simulation system (422 nodes) is chosen as a case study for the transformation.

关键词： hadoop MapReduce hadoop distributed file system Bayesian network Bayesian Inference

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A Metrics Based Analysis of Cloud Resource Management Techniques

A Metrics Based Analysis of Cloud Resource Management Techni...

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International Conference on Advanced Communication Control and Computing Technologies

作者： Abishi Chowdhury Priyanka Tripathi Dept of Computer Engg & Application National Institute of Technical Teachers' Training and ResearchBhopal India

ISBN: (纸本)9781479939152

Cloud computing is getting popularity day by day. It provides different techniques for utilizing the power of resources. Most importantly, it provides a programmable infrastructure which can be modified accordingly. Due to its programmable infrastructure it is easy to scale. Cloud computing provides on demand services to the cloud user. Virtualization is the root of cloud computing. Cloud computing provides services to the users in the form of virtual resources. These resources can be the infrastructure, software, application and can be the data also. The management of these resources is very important. There are different techniques for managing these resources and allocating and taking back these resources from the users. In this paper, we have studied these techniques and provided important points of these techniques.

关键词： Virtual machine Map Reduce hadoop distributed file system Datacenters Response Time Load Balancing Private Cloud Hybrid Cloud Public Cloud

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Cyber Forensic for hadoop based Cloud system

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INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS 2012年第3期6卷 83-89页

作者： Cho, ChaeHo Chin, SungHo Chung, Kwang Sik Korea Natl Open Univ Grad Sch Dept Comp Sci Seoul South Korea LG Elect CTO Div Software Platform Lab Seoul South Korea Korea Natl Open Univ Dept Comp Sci Seoul South Korea

Cloud services are so efficient and flexible to expend and manage the service so that various cloud services are commercially implemented and provided by KT uCloud, Amazon EC2, and the other companies. As could service are quickly deployed, more security problems occurs and cloud forensic procedures for cloud systems are needed. But, in multi-users serviced cloud systems, a system suspension makes serious problems to users so that collecting evidences and analysis have to be performed in the field and live analysis is important in cloud systems. Cloud system based on hadoop distributed file system has characteristics of massive volume of data and multi-users, physically-distributed data, and multi-layered data structures. The previous forensic procedures and methodologies are not appropriate for cloud system based on hadoop distributed file system. In order to deal with those characteristics of cloud system, we propose hadoop based cloud forensic procedure that supports static analysis after live analysis and live collection without system suspension, and hadoop based cloud forensic guidelines. With our proposed hadoop based cloud forensic procedure, we can decrease the time for evidence collection and evidence volume

关键词： cloud distributed file system hadoop distributed file system cyber forensic

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