2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)最新文献_第2页

Distributed and Dependable Software-Defined Storage Control Plane for HPC 面向高性能计算的分布式可靠软件定义存储控制平面

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00071

Mariana Miranda

引用次数: 0

Information Fusion-based Cybersecurity Threat Detection for Intelligent Transportation System 基于信息融合的智能交通系统网络安全威胁检测

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00029

Abdullahi Chowdhury, R. Naha, Shahriar Kaisar, M. Khoshkholghi, Kamran Ali, A. Galletta

{"title":"Information Fusion-based Cybersecurity Threat Detection for Intelligent Transportation System","authors":"Abdullahi Chowdhury, R. Naha, Shahriar Kaisar, M. Khoshkholghi, Kamran Ali, A. Galletta","doi":"10.1109/CCGridW59191.2023.00029","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00029","url":null,"abstract":"Intelligent Transportation Systems (ITS) are sophisticated systems that leverage various technologies to increase the safety, efficiency, and sustainability of transportation. By relying on wireless communication and data collected from diverse sensors, ITS is vulnerable to cybersecurity threats. With the increasing number of attacks on ITS worldwide, detecting and addressing cybersecurity threats has become critically important. This need will only intensify with the impending arrival of autonomous vehicles. One of the primary challenges is identifying critical ITS assets that require protection and understanding the vulnerabilities that cyber attackers can exploit. Additionally, creating a standard profile for ITS is challenging due to the dynamic traffic pattern, which exhibits changes in the movement of vehicles over time. To address these challenges, this paper proposes an information fusion-based cybersecurity threat detection method. Specffically, we employ the Kalman filter for noise reduction, Dempster-Shafer decision theory and Shannon’s entropy for assessing the probabilities of traffic conditions being normal, intruded, and uncertain. We utilised Simulation of Urban Mobility (SUMO) to simulate the Melbourne CBD map and historical traffic data from the Victorian transport authority. Our simulation results reveal that information fusion with three sensor data is more effective in detecting normal traffic conditions. On the other hand, for detecting anomalies, information fusion with two sensor data is more efficient.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126062887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating Kubernetes at the Edge for Fault Tolerant Multi-Camera Computer Vision Applications 评估边缘的Kubernetes在容错多相机计算机视觉应用中的应用

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00054

Owen Heckmann, A. Ravindran

{"title":"Evaluating Kubernetes at the Edge for Fault Tolerant Multi-Camera Computer Vision Applications","authors":"Owen Heckmann, A. Ravindran","doi":"10.1109/CCGridW59191.2023.00054","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00054","url":null,"abstract":"The rise of AI-powered computer vision algorithms offers the possibility of visual sensing of the environment in IoT applications through the widespread use of low-cost video cameras. The need for low latency, bandwidth limitations, and privacy concerns associated with video data motivates the use of edge computing for computer vision applications. However, unlike cloud computing with almost unbounded resources, the edge is characterized by compute nodes of limited capacity and power budget. Additionally, fault tolerance is limited due to replication costs at the edge.In this poster, we present our initial work on evaluating the performance of an edge-specific version of Kubernetes on a Raspberry Pi4 cluster for multi-camera computer vision applications. Kubernetes enables automated deployment and management of containerized distributed applications to run at scale across a cluster of compute and storage nodes, while handling node failures. However, existing literature has not characterized the resource consumption and latency impact of Kubernetes for computer vision applications on realistic edge clusters. Our experimental results indicate that while Kubernetes can deliver fault tolerance at the edge, the choices made in the design of containers pods significantly affects the observed tail latency on a low power edge cluster.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130591952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transformer Inference Acceleration in Edge Computing Environment 边缘计算环境下变压器推理加速

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00030

Mingchu Li, Wenteng Zhang, Dexin Xia

{"title":"Transformer Inference Acceleration in Edge Computing Environment","authors":"Mingchu Li, Wenteng Zhang, Dexin Xia","doi":"10.1109/CCGridW59191.2023.00030","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00030","url":null,"abstract":"The rapid development of deep neural networks (DNNs) has provided a strong foundation for the popularization of intelligent applications. However, the limited computing power of IoT devices cannot support the massive computing load of DNNs. Traditional cloud computing solutions suffer from distance and bandwidth constraints, and cannot meet latency-sensitive requirements. Prior research on DNN acceleration has predominantly focused on convolutional neural networks (CNNs), Transformer has recently gained significant popularity due to its outstanding performance in natural language processing, image processing, and other domains. In this context, we have explored the acceleration of Transformer in edge environments. To model Vision Transformer, we have employed the design concept of a multi-branch network and proposed an optimization strategy to accelerate Transformer inference in the edge environment. We have evaluated our approach on three publicly available datasets and demonstrated its superior performance in challenging network conditions when compared to existing mainstream DNN collaborative acceleration inference techniques.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125816365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Design for Smart Environment Using Raspberry Pi with Blockchain and IoT (BRIoT) 利用树莓派与区块链和物联网(BRIoT)高效设计智能环境

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00026

Sunil K. Ponugumati, Kamran Ali, A. Lasebae, Zaid Zahoor, A. T. Kiyani, Ali Khoshkholghi, M. Latha

{"title":"Efficient Design for Smart Environment Using Raspberry Pi with Blockchain and IoT (BRIoT)","authors":"Sunil K. Ponugumati, Kamran Ali, A. Lasebae, Zaid Zahoor, A. T. Kiyani, Ali Khoshkholghi, M. Latha","doi":"10.1109/CCGridW59191.2023.00026","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00026","url":null,"abstract":"Internet of Things (IoT) is reshaping digital world day by day by integrating several technologies to provide smart services. However, intrinsic features of IoT resulting in a number of challenges, such as decentralization, poor interoperability, privacy, confidentiality, and security vulnerabilities. Several security techniques like encryption, third-party software’s are in use currently to protect users data. Blockchain was initially established for digital crypto currencies with a Proof of Work (PoW) consensus process and the advantage of smart contracts, which enabled distributed trust without the involvement of a third party. Its distributed trust concept paved the way for many other developments, such as the development of new consensus mechanisms such as Proof of Stake (PoS) and Proof of Authority (PoA), which aided in the adoption of Blockchain with low computation machines into sectors such as smart industry and smart transportation. Blockchain implementation in IoT can address the security issue, here we proposed a design using Raspberry Pi as edge node (BRIoT).","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124092499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Next Generation Financial Services: Role of Blockchain enabled Federated Learning and Metaverse 下一代金融服务:区块链在联邦学习和元宇宙中的作用

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00025

Pushpita Chatterjee, Debashis Das, D. Rawat

{"title":"Next Generation Financial Services: Role of Blockchain enabled Federated Learning and Metaverse","authors":"Pushpita Chatterjee, Debashis Das, D. Rawat","doi":"10.1109/CCGridW59191.2023.00025","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00025","url":null,"abstract":"Due to the COVID-19 pandemic and rapid digital transformation in financial institutions, cyberattacks, and data breaches are spiking and becoming more targeted and prevalent. According to media sources, banks received the most malware in 2019. Ransomware, phishing assaults, credit card fraud, and bank account hacks pose the highest risk to the financial sector. The growing number of ways cyber and physical security risks overlap is worrying. Digitalized operational and security solutions are becoming standard across sectors. This paper presents how blockchain technology and Federated Learning (FL) influence financial services to improve the privacy, security, and trust of integrated cyber-physical systems (CPS) in the financial sector. First, an overview of financial services applications is provided in detail. Then, how federated learning (FL) can incorporate blockchain for financial services is presented. It has also been pointed out how the metaverse and digital twin can help improve the financial services ecosystem. Finally, some future research challenges have been included to provide some insights to the researchers and industries about how to enhance the security, trust, and privacy of CPS integrated into financial services using blockchain, FL, and Metaverse.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132885565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved PBFT-Based Consensus Protocol for Industrial IoT 基于pbft的工业物联网改进共识协议

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00068

Roshan Singh, Sukumar Nandi

引用次数: 0

Disease Prediction using Chest X-ray Images in Serverless Data pipeline Framework 基于无服务器数据管道框架的胸部x线图像疾病预测

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00041

Vikas Singh, Neha Singh, Mainak Adhikari

{"title":"Disease Prediction using Chest X-ray Images in Serverless Data pipeline Framework","authors":"Vikas Singh, Neha Singh, Mainak Adhikari","doi":"10.1109/CCGridW59191.2023.00041","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00041","url":null,"abstract":"Serverless architecture is a rapidly emerging trend in the field of cloud computing that promises increased flexibility, scalability, and cost-effectiveness compared to traditional server-based approaches. Leveraging machines to automatically analyze and predict the disease using image data such as chest X-ray images is becoming a challenging task for various contemporary applications. Serverless computing is a cloud computing execution model that provides and manages resources based on the requirements of the users/applications. Besides that, modern data-intensive applications require the power to manage the flow of data between different components in a serverless platform. Motivated by that, in this paper, we develop a new serverless data pipeline framework for predicting disease using chest Xray images. The system utilizes Deep Learning (DL)-based image classification models hosted on Google serverless platform for COVID-19 diagnosis. For disease prediction, we incorporate a transfer learning technique over three popular DL models, namely VGG-16, DenseNet121, and ResNet50. The experimental analysis demonstrates that the proposed serverless data pipeline framework achieves high accuracy, reliability, and speed during COVID-19 disease diagnosis. As per the simulation results, the VGG-16 model outperforms the existing DL models and achieves 97.66% accuracy.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133039247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Query Latency Optimization by Resource-Aware Task Placement in Fog 基于雾环境下资源感知任务布局的查询延迟优化

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00062

Fatima Abdullah, Limei Peng, Byungchul Tak

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SioTec Committee Members SioTec委员会成员

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/ccgridw59191.2023.00009

引用次数: 0