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5th International Conference on IoT in Social, Mobile, Analytics and cloud (I-SMAC)

作者： Suneetha, Avvaru R. V. Naga Arulmurugan, A. Vignans Fdn Sci Technol & Res Dept Comp Sci & Engn Guntur 522213 Andhra Pradesh India Vignan Inst Technol & Sci Dept Comp Sci & Engn Pochampally M 508284 Telangana India

ISBN: (纸本)9781665426428

This paper is focused with currently trending machine learning algorithms (deep learning, Hierarchical temporal memory) and their application in health sensor data. Also, it captures some of the challenges faced during application of machine learning algorithms and also focuses on some advantages and limitations in applying specific machine learning algorithms in health sensor data. Lastly, it covers focuses on sensor big data and machine learning integration.

关键词： Machine Learning Trending Machine Learning Algorithms Deep Learning Hierarchical Temporal Memory distributed cloud computing Spark Health Sensor Data Mobile Health

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ECSD: Enhanced Compromised Switch Detection in an SDN-Based cloud Through Multivariate Time-Series Analysis

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IEEE ACCESS 2020年 8卷 119346-119360页

作者： Dinh, Phuc Trinh Park, Minho Soongsil Univ Dept Informat Commun Mat & Chem Convergence Techn Seoul 156743 South Korea Soongsil Univ Sch Elect Engn Seoul 156743 South Korea

Nowadays, Software-Defined Networks (SDNs) are increasingly being used in many practical settings, posing a variety of security risks, such as compromised switches. Once a switch is compromised by an attacker, the switch may be either malfunctioning or misconfigured, displaying some abnormal network behaviors, e.g., delaying, dropping, adding, or modifying the traffic. In our previous work, we proposed an efficient scheme for detecting compromised SDN switches based on chaotic analysis of network traffic using an autoregressive-integrated-moving-average model. This scheme showed good results overall;however, it still showed high false-alarm rates due to a hard-set threshold. In this paper, we propose an enhanced scheme to detect compromised SDN switches effectively and reliably. The scheme consists of two phases (online and offline), leveraging the advantages of a stochastic recurrent neural network variant of multivariate time-series-based anomaly detection. Our main idea is to capture the normal patterns of multivariate time series by learning strong representations with the key techniques, such as planar normalizing flow and stochastic variable connection, then reconstruct input data by the representations, and use the reconstruction probabilities to find anomalies. Evaluation results of our proposed scheme yield outstanding performance in comparison with our previous work and other solutions.

关键词： Network security software defined networking distributed cloud computing SDN compromised switch anomaly detection machine learning network function virtualization

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SciApps: An Automated Platform for Processing and Distribution of Plant Genomics Data

SciApps: An Automated Platform for Processing and Distributi...

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作者： Liya Wang Zhenyuan Lu Peter Van Buren Doreen Ware

SciApps is an open-source, web-based platform for processing, storing, visualizing, and distributing genomic data and analysis results. Built upon the Tapis (formerly Agave) platform, SciApps brings users TB-scale of data storage via CyVerse Data Store and over one million CPUs via the Extreme Science and Engineering Discovery Environment (XSEDE) resources at Texas Advanced computing Center (TACC). SciApps provides users ways to chain individual jobs into automated and reproducible workflows in a distributed cloud and provides a management system for data, associated metadata, individual analysis jobs, and multi-step workflows. This chapter provides examples of how to (1) submitting, managing, constructing workflows, (2) using public workflows for Bulked Segregant Analysis (BSA), (3) constructing a Data Analysis Center (DAC), and Data Coordination Center (DCC) for the plant ENCODE project. less

关键词： Genomics Gene Prediction Mutagenesis Genome Visualization Genome Annotation Data analysis Visualization Genetic variation BSA Workflow management system distributed cloud computing Plant ENCODE

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WeBrain: A web-based brainformatics platform of computational ecosystem for EEG big data analysis

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NEUROIMAGE 2021年 245卷 118713-118713页

作者： Dong, Li Li, Jianfu Zou, Qiunan Zhang, Yufan Zhao, Lingling Wen, Xin Gong, Jinnan Li, Fali Liu, Tiejun Evans, Alan C. Valdes-Sosa, Pedro A. Yao, Dezhong Univ Elect Sci & Technol China Chengdu Brain Sci Inst Sch Life Sci & Technol MOE Key Lab NeuroinformatClin Hosp Chengdu Peoples R China Chinese Acad Med Sci Res Unit NeuroInformat 2019Ru035 Chengdu Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Sichuan Inst Brain Sci & Brain Inspired Intellige Chengdu 611731 Peoples R China McGill Univ Montreal Neurol Inst Montreal PQ Canada Cuban Neurosci Ctr Ave 25Esq 15815202 Havana Cuba

The current evolution of 'cloud neuroscience' leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://***/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://***/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://***/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.

关键词： WeBrain Brainformatics EEG big data distributed cloud computing Computational ecosystem

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Data Centers Selection for Moving Geo-distributed Big Data to cloud

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JOURNAL OF INTERNET TECHNOLOGY 2019年第1期20卷 111-122页

作者： Zhang, Jiangtao Yuan, Qiang Chen, Shi Huang, Hejiao Wang, Xuan Shenzhen Jingyi Smart Technol Co Ltd Shenzhen Peoples R China Harbin Inst Technol Shenzhen Grad Sch Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China Shenzhen Key Lab Internet Informat Collaborat Shenzhen Peoples R China Shenzhen Appl Technol Engn Lab Internet Multimedi Shenzhen Peoples R China

Because of the distributed networking and coexistent abundant computation and storage resources, cloud computing has become a preferred platform for big data analytics, especially for the geo-distributed data across the world. The precondition for data processing is to move the data to the cloud. Due to the large volume of data, high transmission cost across continents and even specific legal prohibition, it is not always feasible to move all data to one data center. Appropriate data centers should be selected while keeping fast data access and low cost. In this paper, four criteria of the problem are explored. A tight 3-approximation algorithm is proposed to address the former two criteria. It can be simplified when the underlying bipartite graph is complete. The latter two criteria are addressed by a heuristic. Comparing to the optimal method and other schemes, extensive simulations demonstrate that the proposed algorithms can find rather good solutions with less time, and hence are more appropriate for large scale applications.

关键词： Big data Data centers selection distributed cloud computing Cost minimization

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Towards extending the SWITCH platform for time-critical, cloud-based CUDA applications: Job scheduling parameters influencing performance

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 100卷 542-556页

作者： Knight, Louise Stefanic, Polona Cigale, Matej Jones, Andrew C. Taylor, Ian Cardiff Univ Sch Comp Sci & Informat Queens Bldg5 Parade Cardiff CF24 3AA S Glam Wales

SWITCH (Software Workbench for Interactive, Time Critical and Highly self-adaptive cloud applications) allows for the development and deployment of real-time applications in the cloud, but it does not yet support instances backed by Graphics Processing Units (GPUs). Wanting to explore how SWITCH might support CUDA (a GPU architecture) in the future, we have undertaken a review of time-critical CUDA applications, discovering that run-time requirements (which we call 'wall time') are in many cases regarded as the most important. We have performed experiments to investigate which parameters have the greatest impact on wall time when running multiple Amazon Web Services GPU-backed instances. Although a maximum of 8 single-GPU instances can be launched in a single Amazon Region, launching just 2 instances rather than 1 gives a 42% decrease in wall time. Also, instances are often wasted doing nothing, and there is a moderately-strong relationship between how problems are distributed across instances and wall time. These findings can be used to enhance the SWITCH provision for specifying Non-Functional Requirements (NFRs);in the future, GPU-backed instances could be supported. These findings can also be used more generally, to optimise the balance between the computational resources needed and the resulting wall time to obtain results. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Time-critical applications CUDA distributed cloud computing

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SeArch: A Collaborative and Intelligent NIDS Architecture for SDN-Based cloud IoT Networks

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IEEE ACCESS 2019年 7卷 107678-107694页

作者： Tri Gia Nguyen Phan, Trung, V Nguyen, Binh T. So-In, Chakchai Baig, Zubair Ahmed Sanguanpong, Surasak Duy Tan Univ Fac Informat Technol Da Nang 50206 Vietnam Tech Univ Chemnitz Chair Commun Networks D-09126 Chemnitz Germany Hanoi Univ Sci & Technol Sect Int Educ Hanoi 11615 Vietnam Victoria Univ Wellington VUW TT Informat Technol Serv Wellington New Zealand Khon Kaen Univ Fac Sci Dept Comp Sci Appl Network Technol Lab Khon Kaen 40002 Thailand Deakin Univ Sch Informat Technol Geelong Vic 3220 Australia Kasetsart Univ Fac Engn Dept Comp Engn Appl Network Res Lab Bangkok 10900 Thailand

The explosive rise of intelligent devices with ubiquitous connectivity have dramatically increased Internet of Things (IoT) traffic in the cloud environment and created potential attack surfaces for cyber-attacks. Traditional security approaches are insufficient and inefficient to address security threats in cloud-based IoT networks. In this vein, software defined networking (SDN), network function virtualization (NFV), and machine learning techniques introduce numerous advantages that can effectively resolve cybersecurity matters for cloud-based IoT systems. In this paper, we propose a collaborative and intelligent network-based intrusion detection system (NIDS) architecture, namely SeArch for SDN-based cloud IoT networks. It composes a hierarchical layer of intelligent IDS nodes working in collaboration to detect anomalies and formulate policy into the SDN-based IoT gateway devices to stop malicious traffic as fast as possible. We first describe a new NIDS architecture with a comprehensive analysis in terms of the system resource and path selection optimizations. Next, the system process logic is extensively investigated through main consecutive procedures, including initialization, runtime operation, and database update. Afterward, we conduct a detailed implementation of the proposed solution in an SDN-based environment and perform a variety of experiments. Finally, evaluation results of the SeArch architecture yield outstanding performance in anomaly detection and mitigation as well as bottleneck problem handling in the SDN-based cloud IoT networks in comparison with existing solutions.

关键词： Internet of Things security software defined networking network function virtualization machine learning intrusion detection system distributed cloud computing

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Creating Elasticity with Enhanced Weighted Optimization Load Balancing Algorithm in cloud computing

Creating Elasticity with Enhanced Weighted Optimization Load...

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Amity International Conference on Artificial Intelligence (AICAI)

作者： Tyagi, Neha Rana, Ajay Kansal, Vineet Amity Univ Noida Campus Noida India IET Lucknow Uttar Pradesh India GL Bajaj Inst Technol & Management Greater Noida India

ISBN: (纸本)9781538693469

This paper embraces an arrangement of rules for load balancing to improvise the general execution along with viability in assorted environment of cloud computing. The paper advocates a combination set of rules in light of priority and batch rules, taking points of interest of weighted optimization algorithm and similarly spread current execution algorithms. These algorithm reflects the records of currentresource and the capability issue of Central Processing Unit to acquire the targets. cloud Analyst simulator has been used to evaluate and compare the fused algorithm with different algorithms. The results demonstrated progress in migration time, reaction time and scalability, availability & reliability of resources with energy-consumption and throughput via considering the modern-day aid records and the Central Processing Unit potential factor in comparison with different algorithms.

关键词： Virtualization Virtual Machines distributed cloud computing Load

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Distributing the cloud Towards Autonomous & Resilient 5G Networking 10

Distributing the Cloud Towards Autonomous & Resilient 5G Net...

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10th International Conference on Information and Communication Technology Convergence (ICTC) - ICT Convergence Leading the Autonomous Future

作者： Rodrigues Batista, Jose Olimpio, Jr. Mostaco, Gustavo Marques da Silva, Roberto Fray Bressan, Graca Martucci, Moacyr, Jr. Cugnasca, Carlos Eduardo Univ Sao Paulo Escola Politencn Dept Comp Engn & Digital Syst Sao Paulo Brazil

ISBN: (纸本)9781728108933

5G promises a significant increase in network performance, with the support of distributed cloud computing and other technologies. This can result in ten-fold improvements on latency and throughput compared to 4G. With the adoption of 5G and its emerging specifications, network slicing will be important for the edge, thus giving ISPs the opportunity to easily support multi-tenancy, individual customers and use cases in order to meet each slice's unique SLA. This will result in intense traffic distribution at the edge of IP networks, creating a complex and heterogeneous network fabric. 5G network slicing will drive the need for the IP network to support data, control, and management planes separation across a multi-site network fabric, which should have a central view of network state. This knowledge and intelligence should be distributed across the whole fabric and, ideally, throughout the distributed cloud using Software-Defined Networking (SDN). In this paper, we present a summarized model of autonomous network that can be adherent to the 5G requirements regarding low latency, a very important question for 5G implementation. Then, we simulated an ad-hoc network with and without SDN technology in order to validate the latency improvement, and a traditional SDN network with data communication infrastructure to verify the average latency time. It was observed that the use of SDN technology provided better results, in terms of latency and packet delivery rate, when compared to other network protocols. And the pure SDN network with data communication infrastructure presented an average latency of 0.039 ms.

关键词： 5G Autonomous Data Communication Networks distributed cloud computing Internet of Things Software-Defined Networking Real-Time

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Exploiting Heterogeneous Resources in a Multi-cloud Environment

Exploiting Heterogeneous Resources in a Multi-Cloud Environm...

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作者： Oh, Kwangsung University of Minnesota

学位级别：Ph.D.

Today, we increasingly rely upon Internet services and applications to automate many daily activities. As of 2019, for example, 118 million people enjoy watching Netflix in their free time, 150 million people find places to stay while they are traveling through AirBnB, and 75 million people rely on Uber to find a car to move around. For data locality, low latency, and availability, many applications utilize diverse cloud resources e.g., storage, network, and compute, in multiple geo-distributed data centers (DCs) of public cloud providers such as Amazon, Microsoft, and Google. In addition, applications can exploit greater cloud resource options if they consider using these cloud providers’ DCs together. Most cloud providers offer heterogeneous cloud resources e.g., memory, SSD, disk, and archival storage for cloud storage, that allow applications to choose a resource based on requirements and demands. Exploiting such heterogeneous resources, however, brings significant complexities to applications because each cloud resource option has different interfaces, data models, pricing policies, and geographical locations. The heterogeneities of cloud resources allow applications to trade off among different metrics, e.g., latency, availability, monetary cost and so on. To maximize the benefits of heterogeneous cloud resources, applications must answer the question: "what is the best cloud resource configuration (which data centers and which cloud resources) to use to achieve our goals with minimized monetary cost?". Answering this question, however, is challenging because answers are different for each application based on their goals, e.g., SLA (performance), cost budget, consistency model, degree of fault tolerance and so on. Adding to the challenges, dynamics from a multi-cloud environment e.g., network outages and bandwidth/latency fluctuation, and from applications e.g., users’ locations, demand, and data access patterns, make it near impossible to determine the best

关键词： Big Data Analytics cloud Storage Data Placement distributed cloud computing Heterogeneous Resources Multi-DC

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