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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

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An Outline of Data Encryption Standard (DES) Tools and Its Facets within the Realm of Cloud computing

An Outline of Data Encryption Standard (DES) Tools and Its F...

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2023 international conference on advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Kukreti, Riya Kavitha, R. Joshi, Apoorva Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Computer Applications Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350344387

The development of Discrete Event Simulation (DES) systems for enhancing decision-making and research in cloud computing has experienced a substantial growth. The growing diversity of cloud systems makes it extremely challenging to effectively manage cloud resources and services inside data centers. The increasing prevalence of Internet of Things (IoT) gadgets, smartphone apps, and cloud services, together with the introduction of concepts like Edge and Fog computing, have made this system more complex. A significant amount of time and effort must be invested in these tasks. In intricate hyperscale computer environments, decision assistance is provided by the safe and dependable DES technology. This study aims to provide a comprehensive evaluation of DES systems made especially for cloud computing environments. The study carefully evaluates these instruments in a number of categories and is based on a thorough investigation of recent literature. This study goes beyond conventional stand-alone simulation systems by incorporating custom extensions and add-ons designed for certain cloud computing tasks. In addition to highlighting CloudSim's increasing significance as the main platform for simulated research, this also draws attention to the lack of tools made expressly for distributed execution. © 2023 IEEE.

关键词： Cloud computing Cloud Simulation Tools Data Centre Fog computing

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Slim-MY: A Lightweight Network for Power Line Defect Detection with FPGA Acceleration 29

Slim-MY: A Lightweight Network for Power Line Defect Detecti...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Zhang, Xiaoyang Chen, Wenjie Zhou, Zihao Duan, Zhuowen East China Normal University MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350330717

The power line is a crucial component of the power system. However, long-term exposure to the natural environment can lead to various defects, such as burrs, corrosion, and corona. These defects seriously threaten the safe operation of the power system. Traditional manual inspections and image processing methods are time-consuming, labor-intensive, and have lower accuracy. In light of these, we propose a lightweight network called Slim-MY. It utilizes lightweight feature extraction and fusion modules, significantly reducing computational and parameter overhead while ensuring defect detection accuracy. Furthermore, to accelerate the post-processing process, we propose the FP-NMS algorithm. It parallelizes the sorting and overlapping box removal parts of the NMS algorithm, making it more suitable for deployment on devices that support parallel computing, such as FPGA. By testing on our dataset of power line defects, the Slim-MY network achieves a detection accuracy and computation of 92.7% and 1.1 GFLOPs respectively, which outperforms most other networks. By accelerating on the KV260 FPGA, the Slim-MY network achieves an inference speed of 94 fps, and the FP-NMS algorithm's execution speed is 14.8 times that of the GTX 1080Ti. It indicates the feasibility and efficiency of the method we propose in power line defect detection tasks. © 2023 IEEE.

关键词： Deep Learning Defect Detection FPGA parallel computing

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AggrBoost: A Load-Balanced computing Engine for Boosting Aggregation in GCN Accelerators 18

AggrBoost: A Load-Balanced Computing Engine for Boosting Agg...

引用

18th IEEE international conference on Anti-Counterfeiting, Security, and Identification, ASID 2024

作者： Wang, Fan Ni, Wei Yang, Shengji Yu-Kun, Song Duo-Li, Zhang School of Microelectronics Hefei University of Technology Hefei China

ISBN: (纸本)9798331505639

The emergence of Graph Convolutional Network marks a significant breakthrough in machine learning techniques for non-Euclidean spaces. During the graph aggregation process, several challenges arise, including load imbalance, low data reuse rate, and high off-chip data traffic. This paper proposes a computing engine for aggregation phase in GCNs based on a multi-edge parallel aggregation strategy with source node partitioning. This work featuring local sharing of source node feature vectors through source node partitioning and an encoding method named CSB (Compressed Sparse Block), achieves efficient load balancing under high parallelism through the equitable distribution of edge streams. It addresses memory access conflicts during the feature update process of destination nodes by utilizing distributed storage of partial sums, and effectively eliminates the pipeline stalls within partitions by Index-Driven MAC (Multiply-Accumulate). The PE utilization can exceed 97% on several popular datasets, making it suitable for application in GCN hardware accelerators. © 2024 IEEE.

关键词： aggregation computing engine data sharing GCN load balancing

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Optimal Placement of distributed Generation for Power Quality Improvement using Hybrid ABC-CS Algorithm 14

Optimal Placement of Distributed Generation for Power Qualit...

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14th international conference on advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Reddy, Gopal K. Parathasarathy, L. Shakunthala, C. Sowmyashree, K.S. Vidyavardhaka College of Engineering Karnataka Mysuru India ATME College of Engineering Karnataka Mysuru India

Distribution generation (DG) is a term that refers to the production of electricity near the consumption place. On a local basis, there are opportunities for electric utilities to use DG to reduce peak loads, provide auxiliary services such as reactive power and voltage support, and improve power quality. In many cases, distributed generators can provide lower-cost electricity and higher power reliability and security with fewer environmental consequences. Hence, the Dg is placed in the power system to reduce the power loss and increase the power quality. This paper presents a new hybrid optimization algorithm to determine the optimal DG location and size to minimize the total power loss, total harmonic distortion (THD), and voltage sag index improvement. The proposed hybrid optimization algorithm combines the behavior of the Artificial Bee Colony and Cuckoo Search (ABC-CS) algorithm. The DG units are optimally placed by considering the objectives like voltage profile and power loss. The implementation results suggest that the proposed system for the DG placement produces better performance, so that power system quality can be achieved. © Grenze Scientific Society, 2023.

关键词： Power quality

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A Brief Study on Cloud Security 14

A Brief Study on Cloud Security

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Ajith, Abhay Mathew, Adharsh S Remya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350335095

distributed, parallel, and grid computing are all used in a kind of computing known as cloud computing. It serves as a flexible, affordable, and tried-and-true online delivery platform for IT services marketed to businesses or consumers. The outsourcing of critical services to external entities has become a common practice, and this trend has led to various challenges in ensuring the security and privacy of data, maintaining data availability and recovery, and ensuring compliance. Cloud computing, in particular, has added a new layer of complexity to these issues and has introduced additional risks. Cloud computing makes use of many different technologies. Additionally, it carries along its inherent security flaws. Customer access to networks, storage, servers, services, and applications is made possible by cloud technology thanks to how well the pay-per-use model's services function. Our main goal is to locate and connect these systems' weaknesses, threats, and remedies. © 2023 IEEE.

关键词： Cryptography

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PAKSUPERCOMPUTER: AN OPEN-SOURCE, SCALABLE, AND HETEROGENEOUS SUPERcomputing PLATFORM

PAKSUPERCOMPUTER: AN OPEN-SOURCE, SCALABLE, AND HETEROGENEOU...

引用

2025 international conference on Energy, Power, Environment, Control and computing, ICEPECC 2025

作者： Hussain, Tassadaq Haider, Amna Centre for AI and Big Data Namal University Mianwali Mianwali Pakistan Pakistan Supercomputing Barcelona Supercomputing Center Spain

ISBN: (纸本)9781837243235

The recent advances in large vision and language models, along with generative artificial intelligence, have led to a growing demand for compute resources such as increased processing capacity, faster memory, and larger storage. In the current scenario, supercomputing is the only domain capable of solving complex and compute-intensive algorithms for large datasets. Thus, supercomputers are considered the backbone of all fields of academia, research, innovation, and commercialization. In this work, we propose and develop a Scalable Heterogeneous Supercomputer called PakSupercomputer. The PakSupercomputer uses heterogeneous processing cores, including multiple CPUs, Tensor Processing Units (TPUs), and Field Programmable Gate Arrays (FPGAs). An Elastic parallel Programming framework is deployed to provide an easy-to-use programming environment. This programming environment assists high-performance computing developers in writing applications without dealing with the complexities of heterogeneous distributed hardware architectures. The results confirm that when testing benchmarks with larger-scale, complex problems and cloud computing applications, the PakSupercomputer proves to be a low-power, Quadrillion Floating-Point Operations Per Second (FLOPS) supercomputer. © The Institution of Engineering & Technology 2025.

关键词： Cluster computing

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Large-scale taxi trajectory data processing based on spark distributed computing 3

Large-scale taxi trajectory data processing based on spark d...

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3rd international conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Sun, Kai Song, Changxin Science and Technology Department Shanghai Urban Construction Vocational College Shanghai201415 China School of Artificial Intelligence Application Shanghai Urban Construction Vocational College Shanghai201415 China

ISBN: (纸本)9781510668522

With the rapid development of urban transportation, taxi trajectory data has become an important resource. However, processing large-scale taxi trajectory data requires addressing issues such as error analysis and processing, and traditional data processing techniques are no longer sufficient to meet these requirements. Therefore, in order to efficiently process these massive amounts of data, this paper proposes a preprocessing model for massive taxi trajectory data based on Spark distributed computing. This model designs a dataset based on the elastic distributed dataset RDD, which is used to record the structural information of taxi trajectory data. At the same time, the model provides SQL statements for manipulating and managing taxi trajectory data and returns the processed results in the form of a dataframe. By utilizing Spark's parallel computing power and distributed storage characteristics, this model can effectively solve the preprocessing problem of massive taxi trajectory data. The experimental results show that the model has high reliability and processing efficiency in processing massive taxi trajectory data. It lays a solid foundation for subsequent trajectory data mining and analysis. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Urban transportation

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When CPN Meets AI: Resource Provisioning for Inference Query upon computing Power Network 29

When CPN Meets AI: Resource Provisioning for Inference Query...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Ji, Mingtao Qian, Zhuzhong Ye, Baoliu Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798350330717

Performing machine learning inference at the network edge, named Edge Inference, showing benefits like low latency, reduced data traffic, and improved user privacy, has attracted massive attention. computing Power Network (CPN) creates opportunities for edge inference, but also poses multiple challenges for service providers, including computing power selections from different enterprises, the time-coupled decision for resource provisioning and the unpredictable CPN network status. To overcome these challenges, this study formulates a time-varying integer program problem whose goal is to minimize long-term costs, involving switching costs, operational costs, communication costs, and queuing costs. Then we design a group of polynomial-time online algorithms to make online decisions, by taking into account stochastic inputs. Our algorithms adaptively make control decisions by solving delicately constructed subproblems based on the inputs predicted via online learning. Specifically, we first obtain fractional solutions which are transformed into integers for deployment with expectations preserving. Furthermore, we conduct a rigorous proof and establish the competitive ratio which highlights the difference between the performance of our proposed algorithms and the offline optimum. Our comprehensive evaluations, using datasets from real systems, demonstrate that our algorithms outperform multiple alternatives, up to an average of 35% cost reduction, confirming the effectiveness of our algorithms. © 2023 IEEE.

关键词： computing Power Network Resource Provisioning

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distributed training and inference of deep learning solar energy forecasting models 31

Distributed training and inference of deep learning solar en...

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31st Euromicro international conference on parallel, distributed and Network-Based Processing, PDP 2023

作者： Campoy, Javier Prado-Rujas, Ignacio-Iker Risco-Martin, Jose L. Olcoz, Katzalin Perez, Maria S. Universidad Politecnica de Madrid Ontology Eneineering Group Etsi Informaticos. Madrid28660 Spain Universidad Complutense de Madrid Architecture and Technology of Computing Systems Madrid28040 Spain

ISBN: (纸本)9798350337631

Different accurate predictive models have been developed to forecast the amount of solar energy produced in a given area. These models are usually run in a centralized manner, considering irradiance inputs taken from a set of sensors that are deployed in that area. CAIDE is a framework that supports the deployment and analysis of solar plants following Model Based System Engineering (MBSE) and Internet of Things (IoT) methodologies. However, the current solution performs the training and inference phases of the solar energy forecasting models in a central way, not taking advantage of the distributed environment modeled by means of CAIDE. This work presents an extension of CAIDE that allows us to distribute the training and inference phases, obtaining performance improvements, and achieving a greater adaptation to the inherently distributed topology of the deployment of the sensors. © 2023 IEEE.

关键词： Solar energy

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Multimodal Emotion Recognition Using Computer Vision: A Comprehensive Approach 15

Multimodal Emotion Recognition Using Computer Vision: A Comp...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Mohan, G. Bharathi Sarrvesh, N. Hrishikeasan, N.P. Boopalamani, J. Doss, Srinath Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Faculty of Engineering and Technology Botho university Botswana

ISBN: (纸本)9798350370249

Real-time understanding and response to human emotions have become critical in today’s connected world of prevalent human-computer interaction. This study presents a novel approach to real-time emotion detection using a camera-based system that analyzes both voice and facial expressions. Our method advances the state-of-the-art by integrating deep learning techniques with parallel models for audio and visual inputs through a weighted fusion approach, a strategy not commonly employed in existing systems. Utilizing datasets such as CK+48 and the Ryerson Audio-Visual Database of Emotional Speech and Song (Ravdess), our system achieves superior performance metrics, including an accuracy of up to 92.92% and an F1 score of 92.94%. Encapsulated within a user-friendly microservice with an intuitive online interface, our solution enables seamless real-time interaction using only a webcam and microphone. Ongoing efforts focus on refining the system’s ability to recognize diverse voices and expressions, aiming to create technology that empathizes with human emotions and enhances interpersonal interactions. ©2024 IEEE.

关键词： Emotion Recognition

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