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Effectively Using Remote I/O For Work Composition in distributed Workflows 8

Effectively Using Remote I/O For Work Composition in Distrib...

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8th IEEE international conference on Big Data (Big Data)

作者： Friese, Ryan D. Mutlu, Burcu O. Tallent, Nathan R. Suetterlein, Joshua Strube, Jan Pacific Northwest Natl Lab Richland WA 99352 USA

ISBN: (纸本)9781728162515

distributed scientific workflows are becoming more important with the interest in incorporating Al into their loops. A critical programming and performance question is how to compose workflow tasks when data is produced on one system but must be consumed on another. Since the dominant technique is composition with remote I/O, this paper explores its performance expectations. We describe BigFlowSim, a workflow I/O simulator that captures key implementation choices for remote I/O. including intensity, reuse, locality, access pattern, and data movement. With BigFlowSim, we generate a synthetic benchmark. We quantify the effects of each parameter with a performance sensitivity study. We explain trends in terms of data movement reduction and show that, under certain conditions, it is possible to establish a total order among most parameters.

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Intelligent Resource Allocation in Dynamic Fog computing Environments 8

Intelligent Resource Allocation in Dynamic Fog Computing Env...

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8th IEEE international conference on Cloud networking (CloudNet)

作者： SMeddi, Amina Jaafar, Wael Elbiaze, Halima Ajib, Wessam Univ Quebec Montreal Montreal PQ Canada Carleton Univ Ottawa ON Canada

ISBN: (纸本)9781728148328

Fog computing emerged as a new paradigm that pushes cloud applications to the network edge. the fog infrastructure contains mainly distributed and heterogeneous fog nodes that are characterized by their complex distribution, high mobility and sporadic resources availability. this dynamic fog nodes behavior triggers new challenges in the resource management process, such as resources coordination for continuous quality-of-service satisfaction. In this paper, we propose a smart online resource allocation approach adapted for dynamic fog computing environments, aiming at maximizing the number of satisfied user requests within a predefined delay threshold. We model the fog computing environment as a Markov discrete process, where dynamic fog node behavior / mobility and resources availability are considered. then, we present our smart deep-reinforcement-learning resource allocation algorithm. Considering real-world mobility data sets, the near-optimal performance of the proposed solution is illustrated through simulations, and its superiority over heuristic state-of-the-art approaches is exposed.

关键词： Fog computing resource allocation deep reinforcement learning

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distributed Incremental Quasi-Newton Algorithm for Power System State Estimation

Distributed Incremental Quasi-Newton Algorithm for Power Sys...

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international conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Yu Bai Wenling Li Bin Zhang School of Automation Science and Electrical Engineering Beihang University (BUAA) Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665402460

In this paper, we propose a distributed incremental quais-Newton (D-IQN) algorithm for multi-area power system state estimation (MASE). Maximum correntropy criterion (MCC) is used in objective function in order to address non-Gaussian noise. Incremental quais-Newton (IQN) is applied to solve state estimation in each area. In the inter-area communication networks, consensus+innovation strategy is adopted to form a distributed pattern. In this way, each area carries out a local state estimation with limited information exchange with its neighboring areas. As a fully distributed algorithm, no central coordinator is needed here. Based on this peer-to-peer communication paradigm, accurate estimation results are obtained and the privacy of each area remains well-preserved. Numerical experiments are carried out on 118-bus systems. the results show that the algorithm is effective for non-Gaussian noise and outperforms other methods such as distributed Broyden-Fletcher-Goldfarb-Shanno (BFGS), Gauss-Newton and WLS method.

关键词： Privacy Linear programming Power systems Peer-to-peer computing Communication networks State estimation distributed algorithms

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Predicting Mental Health of Scholars Using Contextual Word Embedding

Predicting Mental Health of Scholars Using Contextual Word E...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Aditi Chaurasia Suhani Vinod Prajapati Priya A. Tiru Shobhan Kumar Riya Gupta Arun Chauhan IIIT Dharwad Hubli-Dharwad India

Frustration, anger and mental fatigue are some of the most prevalent issues faced by scholars these days which are often due to the cut-throat competition and peer pressure among adolescents increasing day by day. the objective of the work was to predict the mental health of scholars by tracking strongly negative words or offensive slangs used very deliberately by them in their tweets. the data to analyze the sentiments exhibited by them has been used from their Twitter timelines. the most common topics they talked about and the common keywords for each of these topics were identified and thereafter using BERT, the word embedding and cosine similarities had been found between these keywords and a bag of words that contained a number of strongly negative emotion words collected manually. In this paper, contextual word embedding has been done on the twitter data of frustrated individuals to analyze their temperament and behavior exhibited by them. Also, we have found if there was any correlation between frequently used negative words and frustrated individuals or not. We then found that there was a correlation and individuals using such words had negative emotions prevalent among them.

关键词： Correlation Social networking (online) Blogs Bit error rate Support vector machine classification Mental health Forestry

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An Enhanced Twitter Sentiment Analysis Model using Negation Scope Identification Methods

An Enhanced Twitter Sentiment Analysis Model using Negation ...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Monir Yahya Ali Salmony Arman Rasool Faridi Aligarh Muslim University of Aligarh India Aligarh Muslim University Aligarh India

ISBN: (纸本)9789380544434;9781728195469

Sentiment Analysis (SA), which is also known as Opinion Mining, is a hot-fastest growing research area, making it challenging to follow all its activities. It intends to study peoples' thoughts, feelings, and attitudes about topics, events, issues, entities, individuals, and attributes in social media (e.g., social networking sites, forums, blogs, etc.) expressed by either text comments or tweets. Twitter is one of the world's largest online microblogging platforms that allows its users to freely post texts called tweets. It offers a wealth of information, therefore utilizing SA to analyze this information into positive or negative will assist organizations' and customers' decision-making that will have a significant impact on daily life. SA draws the attention of scientific research in the Natural Language Processing community due to the text structure challenges that may contain negation. Negation is a widespread linguistic structure that changes the text meaning to the opposite and affects text polarity. therefore, it needs to be considered in sentiment analysis systems. In this paper, supervised machine learning models have been used as a baseline to categorize the sentiment of a Twitter dataset using (Bag of Words) and (Term Frequency Inverse Document Frequency) feature representation methods. then we applied negation scope identification methods to find negated tokens and investigate how embedding these tokens can raise SA classifiers' accuracy. the results of the sentiment classification task show an improvement once considering these tokens.

关键词： Sentiment analysis Analytical models Social networking (online) Computational modeling Blogs Decision making Organizations

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Traffic-Aware Dynamic Functional Split for 5G Cloud Radio Access Networks

Traffic-Aware Dynamic Functional Split for 5G Cloud Radio Ac...

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IEEE conference on Network Softwarization (NetSoft)

作者： Himank Gupta A Antony Franklin Mayank Kumar Bheemarjuna Reddy Tamma Indian Institute of Technology Hyderabad India Indian Institute of Technology Bhilai India

ISBN: (数字)9781665406949

ISBN: (纸本)9781665406956

the recent adaption of virtualization technologies in the next generation mobile network enables 5G base station to be segregated into a Radio Unit (RU), a distributed Unit (DU), and a Central Unit (CU) to support Cloud based Radio Access Networks (C-RAN). RU and DU are connected through a fronthaul link. In contrast, CU and DU are connected through a midhaul link. Although virtualization of CU gives benefits of centralization to the operators, there are other issues to be solved such as optimization of midhaul bandwidth and computing resources at edge cloud and central cloud where the DUs and CUs are deployed, respectively. In this paper, we propose a dynamic functional split selection for the DUs in 5G C-RAN by adopting to traffic heterogeneity where the midhaul bandwidth is limited. We propose an optimization problem that maximizes the centralization of the C-RAN system by operating more number of DUs on split Option-7 by changing the channel bandwidth of the DUs. the dynamic selection of split options among each CU-DU pair gives 90% centralization over the static functional split for a given midhaul bandwidth.

关键词： Cloud computing 5G mobile communication Bandwidth Telecommunication traffic Switches Real-time systems Virtualization

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A Variable Neighborhood Heuristic for Facility Locations in Fog computing 1

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8th international conference on Variable Neighborhood Search, ICVNS 2021

作者： Alves de Queiroz, thiago Canali, Claudia Iori, Manuel Lancellotti, Riccardo Institute of Mathematics and Technology Federal University of Catalão CatalãoGO75704-020 Brazil Department of Engineering "Enzo Ferrari" University of Modena and Reggio Emilia Modena41125 Italy Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Reggio Emilia42122 Italy

ISBN: (数字)9783030696252

ISBN: (纸本)9783030696245

the current trend of the modern smart cities applications towards a continuous increase in the volume of produced data and the concurrent need for low and predictable latency in the response time has motivated the shift from a cloud to a fog computing approach. A fog computing architecture is likely to represent a preferable solution to reduce the application latency and the risk of network congestion by decreasing the volume of data transferred to cloud data centers. However, the design of a fog infrastructure opens new issues concerning not only how to allocate the data flow coming from sensors to fog nodes and from there to cloud data centers, but also the choice of the number and the location of the fog nodes to be activated among a list of potential candidates. We model this facility location issue through a multi-objective optimization problem. We propose a heuristic based on the variable neighborhood search, where neighborhood structures are based on swap and move operations. the proposed method is tested in a wide range of scenarios, considering a smart city application’s realistic setup with geographically distributed sensors. the experimental evaluation shows that our method can achieve stable and better performance concerning other literature approaches, supporting the given application. © 2021, Springer Nature Switzerland AG.

关键词： Fog

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Energy Efficient Wireless Body Area Networks: Proximity-based Clustering in Medical Implants 8

Energy Efficient Wireless Body Area Networks: Proximity-base...

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8th international conference on Communications and networking, ComNet2020

作者： Usman, Muhammad Qaraqe, Marwa Asghar, Muhammad Rizwan Ansari, Imran Shafique Division of Information and Computing Technology Education City Doha34110 Qatar University of Auckland Department of Computer Science Auckland1142 New Zealand James Watt School of Engineering University of Glasgow G128QQ United Kingdom

ISBN: (纸本)9781728153209

the rapid development in the field of low power circuits and biosensors has created a new area in Wireless Sensor Networks (WSNs), called Wireless Body Area Networks (WBANs). Medical implants are an integral part of a WBAN to measure, monitor, and control various medical conditions. these implants are generally injected inside the human body through an invasive surgical procedure with very limited energy resources available. In case the battery is depleted, the patient potentially needs to go for another surgery to replace the implant. Subsequently, it becomes imperative to propose energy efficient protocols to save energy in the implants. this work proposes a clustering mechanism in medical implants wherein the implants in the immediate proximity form clusters. In particular, the performance of the customized version of Low-Energy Adaptive Clustering Hierarchy (LEACH), modified for the implants' network, is analyzed. the results are compared with the contention-based Medium Access Control (MAC) layer protocol such as pure ALOHA. In addition, a mathematical model to capture the energy consumption details of an implant network is presented. the simulation results demonstrate that a significant amount of energy can be saved using the proposed model. More precisely, the customized LEACH protocol consumes around 10 times less energy as compared to pure ALOHA. © 2020 IEEE.

关键词： Energy efficiency

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Multi-Core Parallel Processing Technique to Prepare the Time Series Data for the Early Detection of DDoS Flooding Attacks

Multi-Core Parallel Processing Technique to Prepare the Time...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： S. Ratan Kumar V. Valli Kumari K. V. S. V. N. Raju Anil Neerukonda Institute of Technology and Sciences Visakhapatnam India Andhra University Visakhapatnam India Gayatri Vidya Parishad College for Degree and PG Courses Visakhapatnam India

distributed Denial of Service (DDoS) attacks pose a considerable threat to Cloud computing, Internet of things (IoT) and other services offered on the Internet. the victim server receives terabytes of data per second during the DDoS attack. It may take hours to examine them to detect a potential threat, leading to denial of service to legitimate users. Processing vast volumes of traffic to mitigate the attack is a challenging task for network administrators. High-performance techniques are more suited for processing DDoS attack traffic compared to Sequential Processing Techniques. this paper proposes a Multi-Core Parallel Processing Technique to prepare the time series data for the early detection of DDoS flooding attacks. Different time series analysis methods are suggested to detect the attack early on. Producing time series data using parallel processing saves time and further speeds up the detection of the attack. the proposed method is applied to the benchmark data set CICDDoS2019 for generating four different time series to detect TCP-based flooding attacks, namely TCP-SYN, TCP-SYN-ACK, TCP-ACK, and TCP-RST. the implementation results show that the proposed method can give a speedup of 2.3 times for processing attack traffic compared to sequential processing.

关键词： Cloud computing Time series analysis distributed databases Parallel processing Denial-of-service attack Floods Internet of things

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Suicide Trend Analysis and Prediction in India using Facebook Prophet

Suicide Trend Analysis and Prediction in India using Faceboo...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Kashvi Taunk Pulkit Singh Rajat Kumar Behera School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India

Suicide analysis is an area of vital importance to the National Institute of Mental Health and various other agencies working in the field of suicide prevention. Studying on this aspect helps to analyze the suicide pattern and trends that suicides follow over the years. this paper explores time-series data of the suicides that occurred in India to find whether there is a notable change in trend after a certain time point. A predictive approach is applied to forecast into the future of the suicide trend. the paper applies Facebook Prophet, a time-series prediction algorithm for drawing inferences and conclusions. the paper also suggests an inflection point algorithm that highlights the suicide trend between two points in time. Additionally, the model is also capable of predicting the trend for “n” number of years to come. We have used MAPE and SMAPE error techniques for accurate measurement. the mean absolute percentage error (MAPE) is a predictive accuracy measure while the symmetric mean absolute percentage error (SMAPE) is a percentage (or relative) error-dependent accuracy measure. the values of MAPE and SMAPE were found to be in the range of 0.1-0.2 and less than 12 respectively. the conclusion derived is that the result is an increasing nature in the current year and there is a need for utmost attention.

关键词： Social networking (online) Current measurement Measurement uncertainty Estimation Mental health Predictive models Market research

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