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Egress cloud computing with big data attribution 2nd

Egress cloud computing with big data attribution

引用

2nd international conference on Communication and Computer engineering, ICOCOE 2015

作者： Banditwattanawong, Thepparit Masdisornchote, Masawee School of Information Technology Sripathum University Bangkok Thailand

ISBN: (纸本)9783319245829

To share big data stored in cloud in a distributed manner can cause so huge data transfer out of cloud that affects cloud service responsiveness, public cloud data-out monetary costs and network bandwidth consumption. Our previous works showed that deploying cloud cache with i-cloud eviction scheme is an effective solution. Through trace-driven simulations, this paper aims to analyze algorithmic factors influencing the performance of i-cloud. A main finding is that TTL attribute creates the greatest contribution in i-cloud efficiency in distributed big data sharing. In addition, this paper presents a guideline for content providers to allow the distributed sharing of their big data in an effective manner. © Springer international Publishing Switzerland 2016.

关键词： Big data

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international conference on Web engineering, ICWE 2016 and 2nd international Workshop on TEchnical and LEgal aspects of data pRIvacy and SEcurity, TELERISE 2016, 2nd international Workshop on Mining the Social Web, SoWeMine 2016, 1st international Workshop on Liquid Multi-Device Software for the Web, LiquidWS 2016, 5th Workshop on distributed User Interfaces: Distributing Interactions, DUI 2016

International Conference on Web Engineering, ICWE 2016 and 2...

引用

international conference on Web engineering, ICWE 2016 and 2nd international Workshop on TEchnical and LEgal aspects of data pRIvacy and SEcurity, TELERISE 2016, 2nd international Workshop on Mining the Social Web, SoWeMine 2016, 1st international Workshop on Liquid Multi-Device Software for the Web, LiquidWS 2016, 5th Workshop on distributed User Interfaces: Distributing Interactions, DUI 2016

ISBN: (纸本)9783319469621

The proceedings contain 20 papers. The special focus in this conference is on 2nd international Workshop on TEchnical and LEgal aspects of data pRIvacy and Security, 2nd international Workshop on Mining the Social Web, 1st international Workshop on Liquid Multi-Device Software for the Web and 5th Workshop on distributed User Interfaces. The topics include: A privacy-by-design GDPR-compliant framework with verifiable data traceability controls;evaluation of professional cloud password management tools;enhancing access control trees for cloud computing;identifying great teachers through their online presence;experimental measures of news personalization in google news;synchronizing application state using virtual DOM trees;exploiting attributes for inter-component communication;improving context-awareness in healthcare through distributed interactions;study involving two RFID tabletops with generic tangible objects;distributing interaction in responsive cross-device applications;towards user-defined cross-device interaction;virtual spatially aware shared displays;flexible distribution of existing web interfaces and an architecture involving developers and end-users.

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

引用

2nd Africa and Middle East conference on Software engineering, AMECSE 2016

ISBN: (纸本)9781450342933

The proceedings contain 11 papers. The topics discussed include: toward a specialized quality management maturity assessment model;adaptive power panel of cloud computing controlling cloud power consumption;self-management of distributed computing using hybrid-computing elements;formal verification framework for automotive UML designs;e-playground: simultaneous identification of multi-players in educational physical games using low-cost RFID;a requirements elicitation approach for cloud based software product line ERPs;building an ontology-based electronic health record system;an automated system for measuring similarity between software requirements;and case study applying agile service-oriented modeling and architecture approach for better business-services alignment.

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Improved throughput for Power Line Communication (PLC) for Smart Meters using Fog computing based Data aggregation approach 2

Improved throughput for Power Line Communication (PLC) for S...

引用

2nd IEEE international Smart Cities conference (ISC2)

作者： Nazmudeen, Mohamed Saleem Haja Wan, Au Thien Buhari, Seyed M. Univ Teknol Brunei Eledia Offshore Lab Eledia Res Ctr Mukim Gadong A Brunei King Abdulaziz Univ Jeddah Saudi Arabia

ISBN: (纸本)9781509018451

The smart grid is now being deployed in many countries to provide cleaner and greener energy to the consumers. To implement this efficiently and effectively appropriate data collecting, processing and transmitting infrastructures are needed to be in place. Smart meters are used to measure the energy consumption details and patterns and transmit to meter data management system (MDMS) with the help of data aggregation units (DAU). The sheer amount of data need to be collected from the consumers are very huge and they fall under the category of big data. Currently, all the data collected from smart meters are stored in a centralized place for processing to forecast the energy demand. This approach is becoming a bottleneck for efficient data collection due to limited bandwidth capacities of Power Line Communication (PLC). In this paper, we propose a framework for distributed data aggregation approach with the help of fog computing architecture. With this approach the amount of data sent to the centralized storage space is limited and, therefore, the capacity of PLC is virtually improved without compromising the functionality.

关键词： distributed Fog computing Smart-meter Smart-grid PLC message level routing throughput

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Energy efficiency of 5G mobile networks in hybrid Fog and cloud computing environment 2

Energy efficiency of 5G mobile networks in hybrid Fog and Cl...

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2nd international conference on Recent Trends and Applications in Computer Science and Information Technology, RTA-CSIT 2016

作者： Kitanov, Stojan Janevski, Toni Mother Teresa University School of Informatics Skopje Macedonia Ss Cyril and Methodius University Faculty of Electrical Engineering and Information Technologies Skopje Macedonia

The new emerging applications in 5G network, in the context of the Internet of Everything (IoE), will introduce high mobility, high scalability, real-time, and low latency requirements that raise new challenges on the services being provided to the users. Fortunately, Fog computing and cloud computing, with their service orchestration mechanisms offer virtually unlimited dynamic resources for computation, storage and service provision, that will effectively cope with the requirements of the forthcoming services. 5G will use the benefits of centralized high performance computing cloud centers, cloud and fog RANs and distributed peer-to-peer mobile cloud that will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper evaluates a model for fog and cloud hybrid environment service orchestration mechanisms for 5G network in terms of energy efficiency per user for different payloads.

关键词： Fog computing

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2nd international conference on Computer and Communication Technologies, IC3T 2015

2nd International Conference on Computer and Communication T...

引用

2nd international conference on Computer and Communication Technologies, IC3T 2015

ISBN: (纸本)9788132225164

The proceedings contain 76 papers. The special focus in this conference is on Computer and Communication Technologies. The topics include: Medical image fusion in curvelet domain employing PCA and maximum selection rule;gigabit network intrusion detection system using extended bloom filter in reconfigurable hardware;hash-based rule mining algorithm in data-intensive homogeneous cloud environment;privacy preservation in distributed environment using RSA-CRT;segmentation of the human corpus callosum variability from t1 weighted MRI of brain;analysis of molecular single-electron transistors using silicene, graphene and germanene;identification of the plants based on leaf shape descriptors;a comparative analysis of different social network parameters derived from Facebook profiles;a fast and hardware-efficient visual cryptography scheme for images;energy-efficient modified bellman ford algorithm for grid and random network topologies;a novel approach for speaker recognition by using wavelet analysis and support vector machines;high speed network intrusion detection system using FPGA;a novel action descriptor to recognize actions from surveillance videos;protecting the augmented browser extension from mutation cross-site scripting;large-scale data management system using data de-duplication system;connectivity model for molecular communication-based nanomachines network in normal and sub-diffusive regimes;performance improvement of read operations in distributed file system through anticipated parallel processing;a novel multi-view similarity for clustering spatio-temporal data and performance evaluation of free space optical link under various weather conditions.

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cloud Adaptive parallel Simulated Annealing Genetic Algorithm in the Application of Personnel Scheduling in National Geographic Conditions Monitoring 2

Cloud Adaptive Parallel Simulated Annealing Genetic Algorith...

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2nd international conference on Fuzzy Systems and Data Mining (FSDM)

作者： Du, Juan Zhou, Xu Tao, Shu Liu, Qian Natl Geomat Ctr China 28 Lianhuachi West Rd Beijing Peoples R China

ISBN: (纸本)9781614997221;9781614997214

Taking the irrationality of staff scheduling in China Geography Census(CGC) into consideration, this paper establishes a personnel optimization scheduling model. It is based on some assumptions according to characteristics in CGC's (or National Geographic Conditions Monitoring) production process. As optimal dispatch of personnel involves in large-scale, high dimension and nonlinear problems, the standard genetic algorithm (SGA) has drawbacks of premature and slow convergence, as well as poor local optimization ability. So this paper adopts cloud adaptive parallel simulated annealing genetic algorithm (PCASAGA), which integrates adaptiveness, cloud reasoning with simulated annealing mechanism to improve SGA's performance. And also parallel computing function is introduced. To Take Shandong Remote Sensing Technology Application Center as an example, it shows that PCASAGA is superior to SGA in convergence speed and optimization ability. It also proofs that homogeneous and heterogeneous situations have not only distinction but also connection as influence factors increase, such as number of return to modify (n), quality sampling rate (s) and error rate (e). The distinction is changes of structure's proportion, the former case shows flat or falling trends, and the latter one has no unified state. On another side, the connection is the increased optimal completion time. The findings have guiding significance for staff optimization in National Geographic Conditions Monitoring in aspects of engineering plan, cost calculation and so on.

关键词： National Geographic Conditions Monitoring personnel optimization scheduling genetic algorithm cloud reasoning simulated annealing parallel computing

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Message from the organizing chair

Message from the organizing chair

引用

IEEE international conference on Green High Performance computing (ICGHPC)

作者： Shajulin Benedict HPCC Loud Research Laboratory St. Xaviers Catholic College of Engineering Chunkankadai India

2016 2nd international conference on Green High Performance computing (26–27 February 2016) aimed at bringing together specialists and researchers who work with energy related issues that exist in HPC domains, such a... 详细信息

关键词： High performance computing Organizations engineering profession Technological innovation

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Coupled Geomechanics and Reservoir Flow Modeling on distributed Memory parallel Computers

Coupled Geomechanics and Reservoir Flow Modeling on Distribu...

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IEEE international conference on Big Data Security on cloud (BigDataSecurity), High Performance and Smart computing (HPSC) and Intelligent Data and Security (IDS)

作者： Jia Luo Kun Wang Hui Liu Zhangxin Chen Department of Chemical and Petroleum Engineering Univeristy of Calgary

A geomechanical model and a multiphase black oil model are iteratively coupled in this paper. parallel computing is employed to handle large scale problems by benefiting from its features of distributed memory storage and efficient runtime reduction. The finite element method and the finite differencemethod are employed to discretize the two models, respectively. The geomechanical model is developed with the capability of simulating the rock matrix deformation with complex constitutive laws and its effects on reservoir properties. The multiphase flow model is modified by introducing geomechanical variables in a conventional flow model. A coupling strategy is carefully proposed to enable tight and dynamic interactions between these two models, as well as improving parallel computational efficiency. Example problems are presented to demonstrate the utility and efficiency of the coupled models. Expected geomechanical phenomena are illustrated by numerical experiments and validated by commercial software. In addition, for testing the scalability behaviour, field scale problems with millions reservoir and geomechanical grid blocks are performed. The results show encouraging speedups which indicate the integrated models can be an efficient and useful tool for evaluating and analyzing oil and gas production of stress-sensitive reservoirs.

关键词： Reservoirs Mathematical model Computational modeling Solids Stress Couplings Strain

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Large-Scale Industrial Alarm Reduction and Critical Events Mining using Graph Analytics on Spark 2

Large-Scale Industrial Alarm Reduction and Critical Events M...

引用

2nd IEEE international conference on Big Data computing Service and Applications (BigDataService)

作者： Jacobs, Sam Ade Dagnino, Aldo ABB Corp Res Raleigh NC 27606 USA

ISBN: (纸本)9781509022519

In current industrial practice, thousands of industrial alarms generating millions of alarm events, are built into digital control systems typically found in power generation facilities, power grid and communication networks, oil refineries, petrochemical plants, and other manufacturing plants. Given the increasing complexity of such systems, an effective approach to managing alarms and the stream of data they generate becomes imperative. Hitherto, much of alarm management techniques are mostly rule-based with intensive engineering domain expertise requiring an understanding of the underlying physical processes and special skills on the part of the plant operator. This paper describes a novel graph-based data mining approach that can be used to analyze industrial alarm data. Our method leverages the availability of large timestamped historical alarm events datasets. Using these historical datasets, we developed a graph analytics model that identifies redundant alarms and the series of alarms associated with or leading to critical events. Our experiments using real-world (a power generation station and an oil refinery) industrial alarm datasets demonstrate that our proposed method is both scalable and efficient.

关键词： Data Analytics Industrial Alarm Management distributed computing Big Data Hadoop Spark GraphX

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