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Selection Virtual Machine in Mobile Cloud computing 9

Selection Virtual Machine in Mobile Cloud Computing

引用

9th international conference on computing, Communication and networking Technologies (ICCCNT)

作者： Alakbarov, Rashid G. Alakbarov, Oktay R. Azerbaijan Natl Acad Sci Inst Informat Technol Baku Azerbaijan

ISBN: (纸本)9781538644300

the article analyzes the advantages of mobile cloud technologies and problems emerging during the use of those. the network infrastructure created based on cloudlets at the second level of mobile cloud computing with hierarchical structure is analyzed. At the same time, the article explores the issues of satisfaction of demand of mobile equipment for computing and memory resources by using these technologies. the article presents one solution for the allocation of mobile user requests in virtual machines created in cloudlets located near base stations of wireless metropolitan area networks (WMAN) in a balanced way by considering the technical capacity of those. Alongside, the article considers the solution of user problem during designated time and the issue of determining virtual machines satisfying other requirements. For this purpose, different characteristics of the stated problem, virtual machines, as well as communication channels between a user and virtual machines are considered. By using possible values determining the importance of cloudlets, conditions for loading software applications of a user to a virtual machine are explored and an appropriate method is proposed.

关键词： mobile clouds computing mobile equipment computation and memory resources cloudlet virtual machines cloud computing communication channel trustworthiness

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Iris image recognition using optimized kohonen self organizing neural network 9

Iris image recognition using optimized kohonen self organizi...

引用

9th international conference on Imaging for Crime Detection and Prevention, ICDP 2019

作者： Jenkin Winston, J. Jude Hemanth, D. Angelopoulou, A. Kapetanios, E. Department of ECE Karunya Institute of Technology and Sciences Coimbatore India Cognitive Computing Research Lab University of Westminster United Kingdom

ISBN: (纸本)9781839531095

the pursuit to develop an effective people management system has widened over the years to manage the enormous increase in population. Any management system includes identification, verification and recognition stages. Iris recognition has become notable biometrics to support the management system due to its versatility and non-invasive approach. these systems help to identify the individual with the texture information distributed around the iris region. Many classification algorithms are available to help in iris recognition. But those are very sophisticated and require heavy computation. In this paper, an improved Kohonen self-organizing neural network (KSONN) is used to boost the performance of existing KSONN. this improvement is brought by the introduction of optimization technique into the learning phase of the KSONN. the proposed method shows improved accuracy of the recognition. Moreover, it also reduces the iterations required to train the network. From the experimental results, it is observed that the proposed method achieves a maximum accuracy of 98% in 85 iterations. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Machine learning

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An Energy-efficient Scheduling Framework for Cloud Using Learning Automata 9

An Energy-efficient Scheduling Framework for Cloud Using Lea...

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9th international conference on computing, Communication and networking Technologies (ICCCNT)

作者： Sahoo, Sampa Sahoo, Bibhudatta Turuk, Ashok Kumar Natl Inst Technol Dept Comp Sci & Engn Rourkela India

ISBN: (纸本)9781538644300

Cloud computing is an emerging paradigm which helps to realize scalability, high availability, and cost-efficiency. Various applications like financial transactions, health care system, video streaming, IoT applications need on-demand provisioning of cloud resources to assure timeliness and high availability. However, the exponential growth of data generated by these applications causes issues like guaranteeing a timely response, minimizing energy consumption while ensuring high resource utilization. Moreover, low resource utilization also causes wastage of energy. Task scheduling plays a significant role in efficient resource utilization and thus reducing energy consumption in the cloud system. In this paper, we present a learning automata-based framework and algorithm for real-time task execution in the cloud. the learning automata framework guide the scheduler in scheduling a task, realizing optimized energy consumption. the effectiveness of the proposed approach is validated by conducting a set of rigorous evaluations. Simulation results indicate the suitability of proposed algorithm against other existing algorithms.

关键词： Cloud computing Task analysis Scheduling Energy consumption Resource management Real-time systems Processor scheduling

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the Relevance of Container Monitoring towards Container Intelligence 9

The Relevance of Container Monitoring towards Container Inte...

引用

9th international conference on computing, Communication and networking Technologies (ICCCNT)

作者： Madhumathi, R. Sri Ramakrishna Engn Coll Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781538644300

the Docker platform has become the leading containerization-enablement solution these days. Containers emerge as the most appropriate way for agile software development, packaging, shipping and deployment across heterogeneous IT environments (local servers, cloud servers, personal devices, etc.). the software portability being achieved through the containerization movement is being applauded by business houses and IT industry professionals. Containers are being touted as the viable alternative to virtual machines, which are verbose and bulky. the faster provisioning, the real-time scalability, and quick transmission of application containers are being pronounced as the key differentiators of the containerization paradigm. With a growing array of enabling tools from various product vendors, the realization of containerized cloud environments is picking up fast. the DevOps concept gets strengthened with the faster maturity and stability of the Docker platform. As a result, IT can ship faster and run the same application, unchanged, on laptops, data centre VMs, and any cloud. In this paper, the integration of Docker containers with the monitoring tool "Sysdig" (https://***/) is explained. this integration simplifies and streamlines the container monitoring, measurement and management.

关键词： Docker Container Virtualization Cloud computing Container Monitoring

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An Multi-client Web-based Interactive HCI for Interactive Supercomputing

An Multi-client Web-based Interactive HCI for Interactive Su...

引用

ACIS international conference on Software Engineering, Artificial Intelligence, networking, and Parallel/distributed computing (SNPD)

作者： Yuya Yamamoto Fumitaka Arakawa Ayaka Fujii Shinji Fukuma Shin-ichiro Mori Dep. of Information Science University of Fukui Bunkyo 3-9-1 Fukui JAPAN

Importance of the real-time data analysis even in the HPC area rapidly increase in the recent data intensive science. Once the real-time data analysis becomes available, the interactive feedback to the data source will be happening to make thing improve. In order to realize such an environment for scientific simulation, the framework for interactive supercomputing has been developed so far. However, user interaction of that framework was limited to a certain closed environment. In this paper, web-based ubiquitous interface that allows simultaneous sharing of on-going simulation among multiple users is proposed.

关键词： Computational modeling Human computer interaction Time factors Jitter Web servers Visualization

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Minimizing Latency due to Flow Table Overflow by Early Eviction of Flow Entries in SDN 9

Minimizing Latency due to Flow Table Overflow by Early Evict...

引用

9th international conference on computing, Communication and networking Technologies (ICCCNT)

作者： Khan, M. Khursheed Ali Sah, Vibhav K. Mudgal, Pradhumn Hegde, Saumya Natl Inst Technol Karnataka Dept Comp Sci & Engn Mangalore 575025 India

ISBN: (纸本)9781538644300

An Open vSwitch (OVS) stores flow entries in a flow table which is located on an extremely fast and efficient associative memory, Ternary Content Addressable Memory (TCAM). this TCAM is limited in size and therefore in scenarios of huge data traffic these may overflow and incur huge latency and packet loss. Our aim is to address these issues. In this paper we propose an early eviction strategy to evict the flow entries before the flow table overflows, based on analysis of couple of network parameters, namely number of hosts and packet arrival speed. We construct a relation between these parameters and eviction threshold, which is the value that decides the capacity of the flow table at which flow entries are to be evicted, to find the optimal threshold that minimizes the latency and packet loss. We have realized the results on star topology with single switch and multiple hosts, with a flow table limit of 10 entries. the simulation results show drastic decrease in latency and packet loss when flows are evicted early compared to when they are evicted after the table overflows.

关键词： Software Defined networking (SDN) Flow table management Ternary Content Addressable Memory (TCAM) Open vSwitch (OVS) OpenFlow (OF)

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Improving Data Locality in P2P-based Fog computing Platforms 9

Improving Data Locality in P2P-based Fog Computing Platforms

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9th international conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN) / 8th international conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICth)

作者： Steffenel, Luiz Angelo Pinheiro, Manuele Kirsch Univ Reims Reims France Univ Paris 1 Pantheon Sorbonne Paris France

Fog computing extends the Cloud computing paradigm to the edge of the network, relying on computing services intelligently distributed to best meet the applications needs such as low communication latency, data caching or confidentiality reinforcement. While P2P is especially prone to implement Fog computing platforms, it usually lacks important elements such as controlling where the data is stored and who will handle the computing tasks. In this paper we propose both a mapping approach for data-locality and a location-aware scheduling for P2P-based middlewares, improving the data management performance on fog environments. Experimental results comparing the data access performances demonstrate the interest of such techniques. (C) 2018 the Authors. Published by Elsevier Ltd.

关键词： fog computing P2P overlay data locality big data distributed hash table

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Deep Convolutional Autoencoders vs PCA in a Highly-Unbalanced Parkinson's Disease Dataset: A DaTSCAN Study 13th

Deep Convolutional Autoencoders vs PCA in a Highly-Unbalance...

引用

Int Joint Conf of the 13th Int Conf on Soft Comp Models in Ind and Environm Applicat (SOCO) / 11th Int Conf on Computat Intelligence in Secur for Informat Syst (CISIS) / 9th Int Conf on European Transnat Educ (ICEUTE)

作者： Jesus Martinez-Murcia, Francisco Ortiz, Andres Manuel Gorriz, Juan Ramirez, Javier Castillo-Barnes, Diego Salas-Gonzalez, Diego Segovia, Fermin Univ Granada Dept Signal Theory Networking & Commun Granada 18071 Spain Univ Malaga Dept Commun Engn Malaga 29071 Spain

ISBN: (纸本)9783319941202;9783319941196

the automated analysis of medical imaging, especially brain imaging, is a challenging high-dimensional task. Computer Aided Diagnosis (CAD) tools often require the images to be spatially normalized and then perform feature extraction to be able to avoid the small sample size problem. However, the spatial normalization often introduces artefacts, especially in functional imaging. Furthermore, variance-based decomposition techniques like PCA, which are extensively used in CAD tools, often perform poorly in highly-unbalanced dataset. To overcome these two problems, we propose a deep Convolutional Autoencoder (CAE) architecture that performs image decomposition -or encoding- in images that were not spatially normalized. A CAD system that used CAE for feature extraction and a Support Vector Machine Classifier (SVC) for classification was tested on a strongly imbalanced (5.69/1) Parkinson's Disease (PD) neuroimaging dataset from the Parkinson's Progression Markers Initiative (PPMI), achieving more than 93% accuracy in detecting PD with DaTSCAN imaging, and a area under the ROC curve higher than 0.96. this system paves the way for new deep learning decompositions that bypass the common spatial normalization step and are able to extract relevant information in highly-imbalanced datasets.

关键词： Autoencoder Convolutional neural networks Deep learning Parkinson's Disease DaTSCAN

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Neural embeddings for idiolect identification 9

Neural embeddings for idiolect identification

引用

9th international conference on Information, Intelligence, Systems and Applications (IISA)

作者： Perifanos, Konstantinos Florou, Eirini Goutsos, Dionysis Natl & Kapodistrian Univ Athens Dept Linguist Athens Greece

ISBN: (纸本)9781538681619

the term idiolect refers to the unique and distinctive use of language of an individual and it is the theoretical foundation of Authorship Attribution. In this paper we are focusing on learning neural distributed representations (embeddings) of social media users that reflect their writing style. these representations can be considered as stylistic fingerprints of the authors. We are exploring the performance of the two main flavours of distributed representations, namely embeddings produced by Neural Probabilistic Language models and more specifically word2vec.

关键词： Vocabulary Linguistics Social networking (online) Writing Blogs Focusing Estimation

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COMPARATIVE STUDY OF thE DETECTION OF MALICIOUS URLS USING SHALLOW AND DEEP NETWORKS 9

COMPARATIVE STUDY OF THE DETECTION OF MALICIOUS URLS USING S...

引用

9th international conference on computing, Communication and networking Technologies (ICCCNT)

作者： Vazhayil, Anu Vinayakumar, R. Soman, K. P. Amrita Vishwa Vidyapeetham Amrita Sch Engn Ctr Computat Engn & Networking CEN Coimbatore Tamil Nadu India

ISBN: (纸本)9781538644300

In the modern epoch, all information is easily accessible through websites and due to this reason people rely completely on online resources. On the contrary to its advantages, privacy and security in online media are the main concern worldwide because of the rise in phishing attacks launched online. the number of phishing websites increases every month targeting more than 450 brands, as per the reports published by anti-phishing working groups(APWG). Traditionally blacklists are used to detect the URL attacks. But with the exponential increase in the number of phishing websites, this method has its own limitations and it also fails to detect newly generated phishing URLs which can be solved using machine learning or deep learning techniques. Here we present a comparative study between classical machine learning technique - logistic regression using bigram, deep learning techniques like convolution neural network(CNN) and CNN long short-term memory(CNN-LSTM) as architectures used to detect malicious URLs. On comparison CNN-LSTM gave the best accuracy of about 98% for the classification of phishing URLs.

关键词： Phishing uniform resource locator(URL) machine learning deep learning convolutional neural network(CNN) convolutional neural network long short-term memory(CNN-LSTM)

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