To improve the accuracy of load forecasting for large-area power grids during major weather events, a method for predicting the load variation rate of large-area power grids based on major weather events is proposed. ...
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At present, there is a notable focus on parallel and distributedcomputing (PDC) initiatives within the realm of undergraduate engineering education in India. Owing to differences in education systems across borders, ...
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ISBN:
(纸本)9798350383782;9798350383799
At present, there is a notable focus on parallel and distributedcomputing (PDC) initiatives within the realm of undergraduate engineering education in India. Owing to differences in education systems across borders, along with variations in university policies, these efforts must be curated to cater to specific stakeholders, ensuring the achievement of the desired outcomes. Understanding such scenarios is crucial for the landscape of Indian undergraduate PDC education. This paper unveils a success story of implementing PDC at the undergraduate level for the past decade and a half, offering valuable insights gathered along this extended journey. Reflecting the idea that "every master was once a beginner," the narrative unfolds to inspire and empower educators who are just starting out. Whether introducing or already incorporating PDC education into the curriculum, this account is crafted to uplift and guide. Amidst the ongoing initiatives across the country, the time has come to progress and elevate PDC education beyond its current status. This paper presents a summary of potential efforts that the PDC community in India can explore for such initiatives.
distributed clustering algorithms are employed in wireless sensor network (WSN) to improve the local data analysis. This process is carried out collaboratively with the help of nearby neighbours without a central cont...
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The health sector stands as one of the most crucial and vulnerable domains, harbouring extensive personal data. Particularly, Electronic Health Records store information in electronic media where users lack control ov...
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ISBN:
(纸本)9798350369458;9798350369441
The health sector stands as one of the most crucial and vulnerable domains, harbouring extensive personal data. Particularly, Electronic Health Records store information in electronic media where users lack control over their data. Unauthorized access to the server or record portal could lead to data manipulation across the entire database. Addressing this security concern, blockchain emerges as a potential solution, surpassing traditional systems in data storage capabilities. In the event of unauthorized access, an attacker can only manipulate a single block of data, leaving the remaining information protected. In addition, blockchain offers new opportunities and avenues for research, particularly in terms of security and scalability. The proposed system in this paper, MediRec, aims to ensure that users have complete control over their personal data by employing a decentralized database. This database securely stores and restricts access to data, allowing only the owner to share it using a secure key. MediRec facilitates patients in creating blockchain-based accounts, ensuring each individual possesses a dedicated block for storing personal data. This innovative approach enables patients to schedule appointments, and doctors can prescribe medications, with details and e -prescriptions securely stored within the patient's block. To maintain the integrity of the system, doctors can undergo verification by healthcare services, such as the National Health Service in the UK, ensuring their validation before accessing the blockchain system. This stringent validation process ensures the security and authenticity of the data stored within the blockchain. Overall, the proposed solution in this paper offers robust security and scalability in electronic data storage, providing users with control over their information through a blockchain-based system. This system presents a promising solution in preventing data breaches and safeguarding sensitive healthcare information.
MapReduce and Hadoop distributed data processing technologies are used for systematic research. The system is interactive in distributedcomputing, data mining, service response and cloud environment. At the level of ...
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How to solve the problem of excessive execution time of current image processing algorithms? In this research, the CUDA parallel architecture is used as the basis to optimize and accelerate the image processing algori...
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In the evolving Artificial Intelligence (AI) era, the need for real-time algorithm processing in marine edge environments has become a crucial challenge. Data acquisition, analysis, and processing in complex marine si...
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ISBN:
(纸本)9798350363074;9798350363081
In the evolving Artificial Intelligence (AI) era, the need for real-time algorithm processing in marine edge environments has become a crucial challenge. Data acquisition, analysis, and processing in complex marine situations require sophisticated and highly efficient platforms. This study optimizes real-time operations on a containerized distributed processing platform designed for Autonomous Surface Vehicles (ASV) to help safeguard the marine environment. The primary objective is to improve the efficiency and speed of data processing by adopting a microservice management system called DataX. DataX leverages containerization to break down operations into modular units, and resource coordination is based on Kubernetes. This combination of technologies enables more efficient resource management and real-time operations optimization, contributing significantly to the success of marine missions. The platform was developed to address the unique challenges of managing data and running advanced algorithms in a marine context, which often involves limited connectivity, high latencies, and energy restrictions. Finally, as a proof of concept to justify this platform's evolution, experiments were carried out using a cluster of single-board computers equipped with GPUs, running an AI-based marine litter detection application and demonstrating the tangible benefits of this solution and its suitability for the needs of maritime missions.
Machine Learning (ML) workflows are increasingly deployed on serverless computing platforms to benefit from their elasticity and fine-grain pricing. Proper resource allocation is crucial to achieve fast and cost-effic...
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ISBN:
(纸本)9798350337662
Machine Learning (ML) workflows are increasingly deployed on serverless computing platforms to benefit from their elasticity and fine-grain pricing. Proper resource allocation is crucial to achieve fast and cost-efficient execution of serverless ML workflows (specially for hyperparameter tuning and model training). Unfortunately, existing resource allocation methods are static, treat functions equally, and rely on offline prediction, which limit their efficiency. In this paper, we introduce CE-scaling - a Cost-Efficient autoscaling framework for serverless ML workflows. During the hyperparameter tuning, CE-scaling partitions resources across stages according to their exact usage to minimize resource waste. Moreover, it incorporates an online prediction method to dynamically adjust resources during model training. We implement and evaluate CE-scaling on AWS Lambda using various ML models. Evaluation results show that compared to state-of-the-art static resource allocation methods, CE-scaling can reduce the job completion time and the monetary cost by up to 63% and 41% for hyperparameter tuning, respectively;and by up to 58% and 38% for model training.
The power system is gradually orienting to large-scale distributedcomputing, and the trend of area interconnection is becoming more and more obvious. It is still a classic prob-lem to calculate power flow by using di...
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The proceedings contain 27 papers. The special focus in this conference is on parallel and distributedcomputing. The topics include: parallelization of the k-means Algorithm in a Spectral Clustering Chain on CPU-GPU ...
ISBN:
(纸本)9783030715922
The proceedings contain 27 papers. The special focus in this conference is on parallel and distributedcomputing. The topics include: parallelization of the k-means Algorithm in a Spectral Clustering Chain on CPU-GPU Platforms;Management of Heterogeneous Cloud Resources with Use of the PPO;An Open-Source Virtualization Layer for CUDA Applications;high Performance Portable Solver for Tridiagonal Toeplitz Systems of Linear Equations;highPerMeshes – A Domain-Specific Language for Numerical Algorithms on Unstructured grids;Implementation and Evaluation of CUDA-Unified Memory in Numba;Performance Evaluation of Java/PCJ Implementation of parallel Algorithms on the Cloud;parallelizing Automatic Temporal Cognitive Tool for Large-Scale Online Learning Analytics;Experiments Using a Software-distributed Shared Memory, MPI and 0MQ over Heterogeneous computing Resources;ants-Review: A Privacy-Oriented Protocol for Incentivized Open Peer Reviews on Ethereum;on the Provenance Extraction Techniques from Large Scale Log Files: A Case Study for the Numerical Weather Prediction Models;Improving Existing WMS for Reduced Makespan of Workflows with Lambda;predicting Hard Disk Failures in Data Centers Using Temporal Convolutional Neural Networks;On the Detection of Silent Data Corruptions in HPC Applications Using Redundant Multi-threading;analysis of Genome Architecture Mapping Data with a Machine Learning and Polymer-Physics-Based Tool;A New parallel Methodology for the Network Analysis of COVID-19 Data;next Generation Blockchain-Based Financial Services;a Digital Voting System for the 21st Century;trustless, Censorship-Resilient and Scalable Votings in the Permission-Based Blockchain Model;p2T: Pay to Transport;Balanced and Compressed Coordinate Layout for the Sparse Matrix-Vector Product on GPUs.
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