IEEE Cloud Computing最新文献_第8页

A Case Study on Five Maturity Levels of A Kubernetes Operator Kubernetes算子五个成熟度级别的案例研究

IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/IEEECloudSummit52029.2021.00008

Ruxiao Duan, Fan Zhang, S. Khan

引用次数: 1

Lightweight Apparel Classification with Residual and Inverted Residual Block based Architectures 基于残差和倒残差块结构的轻量化服装分类

IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/IEEECloudSummit52029.2021.00017

Kanishk Shah, Khushali Deulkar

引用次数: 0

Mobility-Enabling Edge Cloud Infrastructure: Testbed and Experimental Evaluation 支持移动的边缘云基础设施:测试平台和实验评估

IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/IEEECloudSummit52029.2021.00011

Daniel Fraunholz, Richard Schörghofer-Vrinssen, H. König, W. Mühlbauer, Richard Zahoranksy

引用次数: 1

CASE: CNN Acceleration for Speech-Classification in Edge-Computing 案例:边缘计算中语音分类的CNN加速

IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/IEEECloudSummit52029.2021.00018

Haris Gulzar, Muhammad Shakeel, Kenji Nishida, Katsutoshi Itoyama, K. Nakadai, H. Amano

{"title":"CASE: CNN Acceleration for Speech-Classification in Edge-Computing","authors":"Haris Gulzar, Muhammad Shakeel, Kenji Nishida, Katsutoshi Itoyama, K. Nakadai, H. Amano","doi":"10.1109/IEEECloudSummit52029.2021.00018","DOIUrl":"https://doi.org/10.1109/IEEECloudSummit52029.2021.00018","url":null,"abstract":"High performance of Machine Learning algorithms has enabled numerous applications based upon speech interface in our daily life, but most of the frameworks use computationally expensive algorithms deployed on cloud servers as speech recognition engines. With the recent surge in the number of IoT devices, a robust and scalable solution for enabling AI applications on IoT devices is inevitable in form of edge computing. In this paper, we propose the application of Systemon-Chip (SoC) powered edge computing device as accelerator for speech commands classification using Convolutional Neural Network (CNN). Different aspects affecting the CNN performance are explored and an efficient and light-weight model named as CASENet is proposed which achieves state-of-the-art performance with significantly smaller number of parameters and operations. Efficient extraction of useful features from audio signal helped to maintain high accuracy with a 6X smaller number of parameters, making CASENet the smallest CNN in comparison to similarly performing networks. Light-weight nature of the model has led to achieve 96.45% validation accuracy with a 14X smaller number of operations which makes it ideal for low-power IoT and edge devices. A CNN accelerator is designed and deployed on FPGA part of SoC equipped edge server device. The hardware accelerator helped to improve the inference latency of speech command by a 6.7X factor as compared to standard implementation. Memory, computational cost and latency are the most important metrics for selecting a model to deploy on edge computing devices, and CASENet along with the accelerator surpasses all of these requirements.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"27 1","pages":"63-68"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78514686","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: 1

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IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/ieeecloudsummit52029.2021.00001

引用次数: 0

Multilevel Security Embedded Information Retrieval and Tracking on Cloud Environments 云环境下多级安全嵌入式信息检索与跟踪

IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/IEEECloudSummit52029.2021.00012

H. Kimm, J. Ortiz

引用次数: 2

Towards Trustworthy Outsourced Deep Neural Networks 走向可信赖的外包深度神经网络

IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/IEEECloudSummit52029.2021.00021

Louay Ahmad, Boxiang Dong, B. Samanthula, Ryan Yang Wang, Bill Hui Li

引用次数: 0

Framework for Analysing a Policy-driven Multi-Tenant Kubernetes Environment 分析策略驱动的多租户Kubernetes环境的框架

IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/IEEECloudSummit52029.2021.00016

Angel Beltre, Pankaj Saha, M. Govindaraju

{"title":"Framework for Analysing a Policy-driven Multi-Tenant Kubernetes Environment","authors":"Angel Beltre, Pankaj Saha, M. Govindaraju","doi":"10.1109/IEEECloudSummit52029.2021.00016","DOIUrl":"https://doi.org/10.1109/IEEECloudSummit52029.2021.00016","url":null,"abstract":"Kubernetes (K8s) is gaining adoption in cloud computing for container management, deployment automation, and resource scheduling. As K8s matures, with increased stability and scalability, it is important to study how it can be effectively customized for use in different application scenarios. The focus of our work is on studying one of its main core components, kube-scheduler, which is in charge of scheduling pods on worker nodes. The K8s default scheduler implements the First Come First Serve (FCFS) algorithm as the pods are ordered and sequenced for execution based on the timestamp of when tasks arrive, when no priority is set to the pods. In this paper, we present a Policy-driven Multi-Tenant K8s (PMK) framework to study how policies of multiple tenants on resource requests and job arrivals affect fairness for the tenants individually in terms of makespan, average waiting time, and average turnaround time. PMK allows re-sequencing of tasks, submitted by multiple tenants, via well-known or customized scheduling algorithms before they enter the K8s scheduling queue. Our evaluation uses well-known algorithms such as Round Robin (RR), FCFS and Dominant Resource Fairness (DRF). In addition, we introduce a Cluster-Based Fairness (CBF) scheduling algorithm, which is a variation of DRF. CBF considers overall cluster utilization and resource availability to determine which task to choose from new requests. Our analysis shows that PMK can provide insights to cluster and cloud infrastructure managers on the factors affecting fairness and accordingly in some cases obtain 61.0% improvement in average waiting time for tenants with homogeneous individual demands. In addition, our customized CBF scheduling policy, when used with with PMK on K8s, can reduce overall cluster average waiting time by up to 4%.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"34 1","pages":"49-56"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89791400","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

Short and Sweet Checkpoints for C-RAN MEC C-RAN MEC的短和甜检查点

IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/IEEECloudSummit52029.2021.00019

Saidur Rahman, Apostolos Kalatzis, Mike P. Wittie, A. Elmokashfi, Laura M. Stanley, S. Patterson, David L. Millman

引用次数: 1

A Cloud-based Framework for COVID-19 Media Classification, Information Extraction, and Trends Analysis 基于云的COVID-19媒体分类、信息提取和趋势分析框架

IEEE Cloud Computing Pub Date : 2021-10-01 DOI: 10.1109/IEEECloudSummit52029.2021.00009

H. El-Kassabi, M. Serhani, Khaled Khalil, A. Benharref

{"title":"A Cloud-based Framework for COVID-19 Media Classification, Information Extraction, and Trends Analysis","authors":"H. El-Kassabi, M. Serhani, Khaled Khalil, A. Benharref","doi":"10.1109/IEEECloudSummit52029.2021.00009","DOIUrl":"https://doi.org/10.1109/IEEECloudSummit52029.2021.00009","url":null,"abstract":"The coronavirus COVID-19 pandemic has become the center of concern worldwide and hence the focus of media attention. Checking the coronavirus-related news and updates has become a daily routine of everyone. Hence, news processing and analytics become key solutions to harvest the real value of this massive amount of news. This conscious growth of published news about COVID-19 makes it hard for a variety of audiences to navigate through, analyze, and select the most important news (e.g., relevant information about the pandemic, its evolution, the vital precautions, and the necessary interventions). This can be realized using current and emerging technologies including Cloud computing, Artificial Intelligence (AI) and Deep Learning (DL). In this paper, we propose a framework to analyze the massive amount of public Covid-19 media reports over the Cloud. This framework encompasses four modules, including text preprocessing, deep learning, and machine learning-based news information extraction, and recommendation. We conducted experiments to evaluate three modules of our framework and the results we have obtained prove that combining derived information from the news reports provides the policymakers, health authorities, and the public, a complete picture of the way this virus is proliferating. Analyzing this data swiftly is a powerful tool to provide imperative answers to questions that are relevant to public health.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"54 1","pages":"7-12"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80859381","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