结合容器编排和云中的机器学习:一个系统的映射研究

Nikolas Naydenov, Stela Ruseva
{"title":"结合容器编排和云中的机器学习:一个系统的映射研究","authors":"Nikolas Naydenov, Stela Ruseva","doi":"10.1109/INFOTEH53737.2022.9751317","DOIUrl":null,"url":null,"abstract":"Containerization is a virtualization technology that facilitates the deployment of applications. Container Orchestration is the process of automating the deployment, management, scaling and networking of containers. In this systematic mapping study, we are presenting the analysis of recent scientific papers that deal with containerization and container orchestration in the cloud, combined with machine learning, and how these are utilized to solve problems in different application areas. Currently new challenges arise related to the processing of big data, but also the optimized management of increasing amount of heterogeneous workloads in a cloud environment. The analysis results from the publications of recent years show the growing interest in the scientific community in these evolving technologies - container orchestration from one hand and utilizing machine learning on the other. The emphasis of the study are the trends and innovations, the orchestration technologies and strategies, the machine learning algorithms. Evaluating the relevance of the proposed solutions and ideas for future research are also outlined.","PeriodicalId":6839,"journal":{"name":"2022 21st International Symposium INFOTEH-JAHORINA (INFOTEH)","volume":"70 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Combining Container Orchestration and Machine Learning in the Cloud: a Systematic Mapping Study\",\"authors\":\"Nikolas Naydenov, Stela Ruseva\",\"doi\":\"10.1109/INFOTEH53737.2022.9751317\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Containerization is a virtualization technology that facilitates the deployment of applications. Container Orchestration is the process of automating the deployment, management, scaling and networking of containers. In this systematic mapping study, we are presenting the analysis of recent scientific papers that deal with containerization and container orchestration in the cloud, combined with machine learning, and how these are utilized to solve problems in different application areas. Currently new challenges arise related to the processing of big data, but also the optimized management of increasing amount of heterogeneous workloads in a cloud environment. The analysis results from the publications of recent years show the growing interest in the scientific community in these evolving technologies - container orchestration from one hand and utilizing machine learning on the other. The emphasis of the study are the trends and innovations, the orchestration technologies and strategies, the machine learning algorithms. Evaluating the relevance of the proposed solutions and ideas for future research are also outlined.\",\"PeriodicalId\":6839,\"journal\":{\"name\":\"2022 21st International Symposium INFOTEH-JAHORINA (INFOTEH)\",\"volume\":\"70 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 21st International Symposium INFOTEH-JAHORINA (INFOTEH)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INFOTEH53737.2022.9751317\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 21st International Symposium INFOTEH-JAHORINA (INFOTEH)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INFOTEH53737.2022.9751317","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

容器化是一种便于应用程序部署的虚拟化技术。容器编排是自动化容器的部署、管理、扩展和联网的过程。在这个系统的映射研究中,我们将展示对最近的科学论文的分析,这些论文涉及云中的容器化和容器编排,结合机器学习,以及如何利用这些来解决不同应用领域的问题。目前出现了与大数据处理相关的新挑战,以及云环境中不断增加的异构工作负载的优化管理。近年来出版物的分析结果表明,科学界对这些不断发展的技术越来越感兴趣——一方面是容器编排,另一方面是利用机器学习。研究的重点是趋势和创新,编排技术和策略,机器学习算法。评估提出的解决方案的相关性和未来研究的想法也被概述。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Combining Container Orchestration and Machine Learning in the Cloud: a Systematic Mapping Study
Containerization is a virtualization technology that facilitates the deployment of applications. Container Orchestration is the process of automating the deployment, management, scaling and networking of containers. In this systematic mapping study, we are presenting the analysis of recent scientific papers that deal with containerization and container orchestration in the cloud, combined with machine learning, and how these are utilized to solve problems in different application areas. Currently new challenges arise related to the processing of big data, but also the optimized management of increasing amount of heterogeneous workloads in a cloud environment. The analysis results from the publications of recent years show the growing interest in the scientific community in these evolving technologies - container orchestration from one hand and utilizing machine learning on the other. The emphasis of the study are the trends and innovations, the orchestration technologies and strategies, the machine learning algorithms. Evaluating the relevance of the proposed solutions and ideas for future research are also outlined.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
PV system site selection using PVGIS and Fuzzy AHP Face Mask Detection Based on Machine Learning and Edge Computing Smart Production Systems: Methods and Application Analyzing the Effects of Abnormal Resonance Voltages using Artificial Neural Networks Real-Time Data Processing Techniques for a Scalable Spatial and Temporal Dimension Reduction
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1