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2nd international conference on cloud computing and big data, cloudcom-asia 2015

ISBN: (纸本)9783319284293

The proceedings contain 29 papers. The special focus in this conference is on cloud Architecture, Applications, big data and Social Network. The topics include: An effective approach to isolating performance bottleneck during slow data delivery;schedule compaction and deadline constrained dag scheduling for IAAS cloud;synthesizing realistic cloud workload traces for studying dynamic resource system management;energy-efficient VM placement algorithms for cloud data center;a new analytics model for large scale multidimensional data visualization;an architecture-based autonomous engine for services configuration and deployment in hybrid clouds;dynamic load sharing to maximize resource utilization within cloud federation;real-time task scheduling algorithm for cloud computing based on particle swarm optimization;making GPU warp scheduler and memory scheduler synchronization-aware;a novel grid based k-means cluster method for traffic zone division;color image fusion researching based on S-PCNN and laplacian pyramid;rationalizing the parameters of k-nearest neighbor classification algorithm;analyzing and predicting failure in hadoop clusters using distributed hidden Markov model;emerging pragmatic patterns in large-scale RDF data;cross-correlation as tool to determine the similarity of series of measurements for big-data analysis tasks;the key technologies of real-time processing large scale microblog data stream;an efficient index method for multi-dimensional query in cloud environment;keyword search over encrypted data in cloud computing from lattices in the standard model and research of terminal transparent encryption storage mechanism for multi-cloud disks.

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cloud computing and big data: Second international conference, cloudcom-asia 2015 Huangshan, China, june 17–19, 2015 revised selected papers 2nd

Cloud computing and big data: Second international conferenc...

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2nd international conference on cloud computing and big data, cloudcom-asia 2015

作者： Qiang, Weizhong Zheng, Xianghan Hsu, Ching-Hsien School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China College of Mathematics and Computer Science Fuzhou University Fuzhou China Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan

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Keyword search over encrypted data in cloud computing from lattices in the standard model 2nd

Keyword search over encrypted data in cloud computing from l...

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2nd international conference on cloud computing and big data, cloudcom-asia 2015

作者： Gu, Chunxiang Zheng, Yonghui Kang, Fei Xin, Dan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9783319284293

As cloud computing becomes popular, more and more sensitive information are being centralized into the cloud. Hence, we need some mechanism to support operations on encrypted data in many applications in cloud. Public key encryption with keyword search (PEKS) is a mechanism for searching on encrypted data. It enables user Alice to send a secret value TW to a cloud server that will enable the server to selectively retrieve encrypted messages containing the keyword, but learn nothing else. In this paper, we propose a PEKS scheme using lattices. Lattice-based cryptosystems are becoming an increasingly popular in the research community. Our scheme can be proven secure in the standard model with the hardness of the standard Learning With Errors problem. © Springer international Publishing Switzerland 2015.

关键词： cloud computing

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Challenges of cloud computing & big data Analytics 2

Challenges of Cloud Computing & Big Data Analytics

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2nd international conference on computing for Sustainable Global Development (IndIACom)

作者： Gupta, Anita Mehrotra, Abhay Khan, P. M. Integral Univ Dept CSE Lucknow Uttar Pradesh India Concentrix IT Applicat Enterprise Grp Gurgaon India AMU Ctr Comp Aligarh Uttar Pradesh India

ISBN: (纸本)9789380544168

Now-a-days for most of the organizations across the globe, two important IT initiatives are: big data Analytics and cloud computing. big data Analytics can provide valuables insight that can create competitiveness and generate increased revenues. cloud computing can enhance productivity and efficiencies thus reducing cost. cloud computing offers groups of servers, storages and various networking resources. It enables environment of big data to processes voluminous, high velocity and varied formats of big data.

关键词： big data Hadoop HDFS Honey Pot Nodes Map Reduce cloud computing AaaS

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big data and cloud computing: Pitfalls and Advantages in data Management 2

Big Data and Cloud Computing: Pitfalls and Advantages in Dat...

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2nd international conference on computing for Sustainable Global Development (IndIACom)

作者： Shah, Neepa K. Gujarat Vidyapith Dept Comp Sci Ahmadabad Gujarat India

ISBN: (纸本)9789380544168

Scalable database management system works for both online transaction processing system and decision support system. big data in current era play a critical role against RDBMS to provide fast solution to manage data. cloud computing had also played a good role to do transformation of traditional database management system but now big data services needs to be provide in cloud. cloud computing is another dimension for data processing in big data. This paper presents challenges that need to be addressed for having successful big data application in the cloud environment. Developer and designer of cloud service provider have to handle the issues of big data with on-line transaction support and decision support ad-hoc query processing. In concluding section paper will propose various big data models with functionalities of data management that lead to the bridge to be developed for big data and cloud computing to boost performance of large data in reduced cost.

关键词： cloud computing Aaas cloud computing big data key-value model document-oriented model column based model graph based model

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cloud Intelligence

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INFORMATION SYSTEMS 2015年 54卷 309-310页

作者： Darmont, Jerome Pedersen, Torben Bach Univ Lyon Lyon France Aalborg Univ Aalborg Denmark

The Special Issue of Journal of Information Systems, presents best papers from the 2nd international Workshop on cloud Intelligence (cloud-I 2013), which was held on August 26, 2013 in Riva del Garda, Italy, in conjunction with the 39th international conference on Very Large databases (VLDB 2013). In the paper entitled 'Bloofi: Multidimensional Bloom Filters', Adina Crainiceanu and Daniel Lemi reconsider the use of Bloom filters in federated cloud environments. With hundreds of geographically distributed clouds participating in a federation, information needs to be shared by the semi-autonomous cloud providers. In the paper entitled 'Efficient Skyline Query Processing in Spatial Hadoop', Dimitris Pertesis and Christos Doulkeridis address the problem of computing the skyline of big spatial datasets in Spatial Hadoop, an extension of Hadoop that efficiently supports spatial operations. In the paper entitled 'Tuning Small Analytics on big data: data Partitioning and Secondary Indexes in the Hadoop Ecosystem', Oscar Romero, Victor Herrero, Alberto AbelloÁ and Jaume Ferrarons propose the use of well-known query optimization techniques on top of 'brute force' MapReduce.

关键词： cloud computing ARTIFICIAL intelligence PROGRAMMING languages (Electronic computers) COMPUTER software COMPUTER architecture

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Independent Task Scheduling in cloud Environment Using big Bang-big Crunch Approach 2

Independent Task Scheduling in Cloud Environment Using Big B...

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2nd international conference on Recent Advances in Engineering

作者： Kumari, Vandna Kalra, Mala Singh, Sarbjeet NITTTR Dept CSE Chandigarh India UIET Dept CSE Chandigarh India

ISBN: (纸本)9781467382533

cloud computing is a well known computing technology that deals with large capacity of information or data using highly available geographically distributed resources that can be accessed by user via internet in terms of pay per use basis. In such a way, tasks should be scheduled efficiently such that user specified QoS constraints are satisfied. Task scheduling, which is NP-complete problem, plays an important role in cloud computing. In this research work, big Bang-big Crunch (BBBC) technique is used for scheduling of independent tasks in the cloud environment aiming to reduce makespan. The performance of the proposed approach is evaluated by comparing it against GA using cloudSim tool. Experimental results show that the BBBC approach outperforms GA approach in terms of makespan.

关键词： cloud computing big bang big crunch genetic algorithm task scheduling

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Energy-efficient VM placement algorithms for cloud data center 2nd

Energy-efficient VM placement algorithms for cloud data cent...

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2nd international conference on cloud computing and big data, cloudcom-asia 2015

作者： Lin, Xiuyan Liu, Zhanghui Guo, Wenzhong Fujian Provincial Key Lab of the Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China

ISBN: (纸本)9783319284293

cloud is the computing paradigm which provides computing resource as a service through network. The client can use computing resource in a convenient and on-demand way, just like the water and the electricity we use daily. The mapping between virtual machine and physical machine is the key of the VM scheduling problem. Nowadays we advocate low-carbon life. It calls for the green cloud computing solutions whether protecting the environment or saving the cost of cloud suppliers. The proposed VM placement algorithm is energy-efficient, and considers the multi-dimentional resource constrains, such as CPU, memory, network bandwidth, and so on. The experimental results show that the proposed algorithms not only contribute a lot to energy saving, but also try best to meet the quality of service (QoS). Therefore, we make significant savings in operating cost and make full use of various resources in the cloud data center. The algorithm has promising prospect in application. © Springer international Publishing Switzerland 2015.

关键词： Virtual machine

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A Method of Reliability Assessment Based on Neural Network and Fault data Clustering for cloud with big data 2

A Method of Reliability Assessment Based on Neural Network a...

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2nd international conference on Information Science and Security (ICISS 2015)

作者： Tamura, Yoshinobu Nobukawa, Yumi Yamada, Shigeru Yamaguchi Univ Grad Sch Sci & Engn Tokiwadai 2-16-1 Ube Yamaguchi 7558611 Japan Tottori Univ Grad Sch Engn Tottori 6808552 Japan

ISBN: (纸本)9781467386111

In the mobile clouds, the data size recorded in database software becomes large. Considering the software reliability of cloud computing with big data, it is important for the software managers to assess the relationship among the database software and cloud software, because the cloud software collaborate closely with the database software by using the internet network. In this paper, we propose a method of software reliability assessment based on the fault data clustering and neural network in cloud computing environment with big data. We perform a cluster analysis for the software fault data by using k-means clustering. Also, we propose the estimation method of the cumulative numbers of detected faults based on the neural network by using the results of cluster analysis. Moreover, we show several numerical examples of software reliability assessment in the cloud computing environment with big data.

关键词： big data cloud computing reliability fault data clustering neural network

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Research on cloud computing for Disaster Monitoring Using Massive Remote Sensing data 2

Research on Cloud Computing for Disaster Monitoring Using Ma...

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2nd IEEE international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Zou, Quan Southwest Univ Sch Comp Informat & Sci Chongqing Peoples R China

ISBN: (纸本)9781509044993

Satellite remote sensing technology can extract disaster information rapidly and accurately for disaster monitoring on a regional or national basis. However, various sensors are generating huge volumes of remote sensing data for disaster management. It is urgent to handle such massive remote sensing images. In this paper, it provides the solutions for massive remote sensing data analysis and rapid information extraction. A detailed description of the web platform offers an interoperable framework to integrate distributed data and model resources for disaster monitoring using cloud computing. A high throughput cloud computing interfaces and the design of integrated disaster rapid cloud platform are proposed in this paper. In addition, this system provides well interoperability for users. With three typical disaster applications as use case, the experiment proves that the framework is quite efficient for rapid extraction of disaster information based on massive remote sensing data.

关键词： big remote sensing data cloud computing rapid extraction of disaster information

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