2022 IEEE Cloud Summit最新文献_第2页

Quantitative Evaluation of Cloud Elasticity based on Fuzzy Analytic Hierarchy Process 基于模糊层次分析法的云弹性定量评价

2022 IEEE Cloud Summit Pub Date : 2022-10-01 DOI: 10.1109/CloudSummit54781.2022.00022

Bolin Yang, Fan Zhang, S. Khan

引用次数: 0

Facial Expression Recognition System on a Distributed Edge-Cloud Infrastructure 分布式边缘云基础设施上的面部表情识别系统

2022 IEEE Cloud Summit Pub Date : 2022-10-01 DOI: 10.1109/CloudSummit54781.2022.00014

Kai Cui, Guoting Zhang, Fan Zhang, S. Khan

引用次数: 0

The Cost of Virtualizing Time in Linux Containers Linux容器虚拟化的时间成本

2022 IEEE Cloud Summit Pub Date : 2022-10-01 DOI: 10.1109/CloudSummit54781.2022.00016

X. Merino, Carlos E. Otero

引用次数: 0

Towards Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe 面向极端和可持续的图形处理在欧洲的紧迫社会挑战

2022 IEEE Cloud Summit Pub Date : 2022-10-01 DOI: 10.1109/CloudSummit54781.2022.00010

R.-C. Prodan, Dragi Kimovski, Andrea Bartolini, Michael Cochez, A. Iosup, E. Kharlamov, Jože M. Rožanec, Laurentiu A. Vasiliu, A. Varbanescu

{"title":"Towards Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe","authors":"R.-C. Prodan, Dragi Kimovski, Andrea Bartolini, Michael Cochez, A. Iosup, E. Kharlamov, Jože M. Rožanec, Laurentiu A. Vasiliu, A. Varbanescu","doi":"10.1109/CloudSummit54781.2022.00010","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00010","url":null,"abstract":"The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation uses the emerging serverless computing paradigm for efficiency and event responsiveness. Thus, it supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing. Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70% more efficient analytics than AliGraph, and 30 % improved energy awareness for extract, transform and load storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25 % lower greenhouse gas emissions for basic graph operations.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123228277","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: 8

Context-Aware Feature Selection using Denoising Auto-Encoder for Fault Detection in Cloud Environments 云环境中基于去噪自编码器的上下文感知特征选择

2022 IEEE Cloud Summit Pub Date : 2022-10-01 DOI: 10.1109/CloudSummit54781.2022.00019

Razieh Abbasi Ghalehtaki, Amin Ebrahimzadeh, R. Glitho

{"title":"Context-Aware Feature Selection using Denoising Auto-Encoder for Fault Detection in Cloud Environments","authors":"Razieh Abbasi Ghalehtaki, Amin Ebrahimzadeh, R. Glitho","doi":"10.1109/CloudSummit54781.2022.00019","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00019","url":null,"abstract":"Machine learning is expected to play an instrumental role in automating the detection of faults in next-generation cloud networks. The existing machine-learning-based fault detection methods suffer from the following drawbacks: (i) ignoring the issue of missing feature values, (ii) ignoring the impact of each feature on output prediction over other features (measurement of feature importance), and (iii) lack of calculation of the proper number of features for fault detection. To address the above challenges, in this paper, we propose a context-aware feature selection method to improve the performance of fault detection methods in the cloud environment, aiming at maximizing the $F_{1}$-score. Our proposed solution comprises Denoising Auto-Encoder (DAE) stacked with a Discriminative Model (DM). The DAE is applied to handle the missing feature values and encoding features while the DM is responsible for making predictions of system status based on the encoded features. Then, the sensitivity analysis of output prediction with respect to each input feature value is used to measure the feature importance. We compare our work with existing solutions from the literature. Our results reveal that the proposed solution can improve the $F_{1}$-score up to 47 % and 76 % in the scenario where all feature values are known and in the scenario where only 25 % of feature values are known, respectively.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116592611","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

Various Network Topologies and an Analysis Comparative Between Fat-Tree and BCube for a Data Center Network: An Overview 数据中心网络的各种网络拓扑结构及胖树与BCube的分析比较综述

2022 IEEE Cloud Summit Pub Date : 2022-10-01 DOI: 10.1109/CloudSummit54781.2022.00007

Antonio Cortés Castillo

{"title":"Various Network Topologies and an Analysis Comparative Between Fat-Tree and BCube for a Data Center Network: An Overview","authors":"Antonio Cortés Castillo","doi":"10.1109/CloudSummit54781.2022.00007","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00007","url":null,"abstract":"The exponential rise of servers in the cloud generates the need for network topologies for specialized data centers, which means that the service requirements are accompanied by the availability of massive storage and quality of services, essential aspects for handling large volumes of data in large server farms located in the DCN. In turn, the requirements for new cloud services have grown exponentially, so DCNs face new challenges related to scalability, energy efficiency, network congestion, and cost, which are directly associated with the architectures and DCN network topologies. Similarly, from existing network topologies we propose a DCN topology. In this paper, Fat-Tree and BCube network topologies are compared by considering the architectures themselves, the metrics for comparing various topology types, and two statistical functions are used such as the exponential random traffic distribution and uniform random traffic distribution. This type of comparison helps to solve the existing problem regarding the demand for new services, load balance, bandwidth, and node requirements in a DC network infrastructure.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133646588","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

PriRecT: Privacy-preserving Job Recommendation Tool for GPU Sharing PriRecT: GPU共享的隐私保护作业推荐工具

2022 IEEE Cloud Summit Pub Date : 2022-10-01 DOI: 10.1109/CloudSummit54781.2022.00021

Aritra Ray, Zhaobo Zhang, Ying Xiong, K. Chakrabarty

引用次数: 0

Machine Learning vs Deep Learning for Anomaly Detection and Categorization in Multi-cloud Environments 机器学习与深度学习在多云环境中的异常检测和分类

2022 IEEE Cloud Summit Pub Date : 2022-10-01 DOI: 10.1109/CloudSummit54781.2022.00013

J. Akoto, Tara Salman

{"title":"Machine Learning vs Deep Learning for Anomaly Detection and Categorization in Multi-cloud Environments","authors":"J. Akoto, Tara Salman","doi":"10.1109/CloudSummit54781.2022.00013","DOIUrl":"https://doi.org/10.1109/CloudSummit54781.2022.00013","url":null,"abstract":"Detecting intrusions is a critical issue in cyberse-curity. One way to overcome this issue is to build efficient and robust Network Intrusion Detection Systems (NIDS) using existing Machine Learning (ML) algorithms. Such an approach has been proposed in the literature and has been shown to perform well. However, a comparative analysis of the performance of ML and Deep Learning (DL) based NIDS for both detection and categorization of intrusions is still needed. This paper investigates the performance of ML and DL models for both intrusion detection and categorization. We use the publicly available Canadian Institute of Cybersecurity Intrusion Detection System 2017 (CICIDS-2017) dataset to train and test ML and DL models. We apply three traditional ML models, namely, Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), and three DL models − 1-D Convolutional Neural Network (ConvlD), Recurrent Neural Network (RNN), and a two-staged model that combines an unsupervised Dense Autoencoders (DAE) for pre-training and an Artificial Neural Network (ANN) for classification. Our results demonstrate that RF is the best performing ML model with a detection accuracy of 99.5% and DAE-ANN is the best performing DL model with a detection accuracy of 98.7%. We also show the advantages of using a stepwise multi-classification over a classical single-stage multi-classification. Finally, we observe that RF outperforms DAE-ANN in categorization with detection rates of 91.35 % and 84.66 %, respectively.","PeriodicalId":106553,"journal":{"name":"2022 IEEE Cloud Summit","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125946578","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