Florian Wiedner, Max Helm, Alexander Daichendt, Jonas Andre, Georg Carle
{"title":"虚拟化网络环境中用于低延迟数据包处理的容器性能评估","authors":"Florian Wiedner, Max Helm, Alexander Daichendt, Jonas Andre, Georg Carle","doi":"10.1016/j.peva.2024.102442","DOIUrl":null,"url":null,"abstract":"<div><p>Packet processing in current network scenarios faces complex challenges due to the increasing prevalence of requirements such as low latency, high reliability, and resource sharing. Virtualization is a potential solution to mitigate these challenges by enabling resource sharing and on-demand provisioning; however, ensuring high reliability and ultra-low latency remains a key challenge. Since bare-metal systems are often impractical because of high cost and space usage, and the overhead of virtual machines (VMs) is substantial, we evaluate the utilization of containers as a potential lightweight solution for low-latency packet processing. Herein, we discuss the benefits and drawbacks and encourage container environments in low-latency packet processing when the degree of isolation of customer data is adequate and bare metal systems are unaffordable. Our results demonstrate that containers exhibit similar latency performance with more predictable tail-latency behavior than bare metal packet processing. Moreover, deciding which mainboard architecture to use, especially the cache division, is equally vital as containers are prone to higher latencies on more shared caches between cores especially when other optimizations cannot be used. We show that this has a higher impact on latencies within containers than on bare metal or VMs, resulting in the selection of hardware architectures following optimizations as a critical challenge. Furthermore, the results reveal that the virtualization overhead does not impact tail latencies.</p></div>","PeriodicalId":19964,"journal":{"name":"Performance Evaluation","volume":"166 ","pages":"Article 102442"},"PeriodicalIF":1.0000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166531624000476/pdfft?md5=92c046df1bfad30f8dbdb77dadbb4fd5&pid=1-s2.0-S0166531624000476-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Performance evaluation of containers for low-latency packet processing in virtualized network environments\",\"authors\":\"Florian Wiedner, Max Helm, Alexander Daichendt, Jonas Andre, Georg Carle\",\"doi\":\"10.1016/j.peva.2024.102442\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Packet processing in current network scenarios faces complex challenges due to the increasing prevalence of requirements such as low latency, high reliability, and resource sharing. Virtualization is a potential solution to mitigate these challenges by enabling resource sharing and on-demand provisioning; however, ensuring high reliability and ultra-low latency remains a key challenge. Since bare-metal systems are often impractical because of high cost and space usage, and the overhead of virtual machines (VMs) is substantial, we evaluate the utilization of containers as a potential lightweight solution for low-latency packet processing. Herein, we discuss the benefits and drawbacks and encourage container environments in low-latency packet processing when the degree of isolation of customer data is adequate and bare metal systems are unaffordable. Our results demonstrate that containers exhibit similar latency performance with more predictable tail-latency behavior than bare metal packet processing. Moreover, deciding which mainboard architecture to use, especially the cache division, is equally vital as containers are prone to higher latencies on more shared caches between cores especially when other optimizations cannot be used. We show that this has a higher impact on latencies within containers than on bare metal or VMs, resulting in the selection of hardware architectures following optimizations as a critical challenge. Furthermore, the results reveal that the virtualization overhead does not impact tail latencies.</p></div>\",\"PeriodicalId\":19964,\"journal\":{\"name\":\"Performance Evaluation\",\"volume\":\"166 \",\"pages\":\"Article 102442\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0166531624000476/pdfft?md5=92c046df1bfad30f8dbdb77dadbb4fd5&pid=1-s2.0-S0166531624000476-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Performance Evaluation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0166531624000476\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Performance Evaluation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0166531624000476","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
Performance evaluation of containers for low-latency packet processing in virtualized network environments
Packet processing in current network scenarios faces complex challenges due to the increasing prevalence of requirements such as low latency, high reliability, and resource sharing. Virtualization is a potential solution to mitigate these challenges by enabling resource sharing and on-demand provisioning; however, ensuring high reliability and ultra-low latency remains a key challenge. Since bare-metal systems are often impractical because of high cost and space usage, and the overhead of virtual machines (VMs) is substantial, we evaluate the utilization of containers as a potential lightweight solution for low-latency packet processing. Herein, we discuss the benefits and drawbacks and encourage container environments in low-latency packet processing when the degree of isolation of customer data is adequate and bare metal systems are unaffordable. Our results demonstrate that containers exhibit similar latency performance with more predictable tail-latency behavior than bare metal packet processing. Moreover, deciding which mainboard architecture to use, especially the cache division, is equally vital as containers are prone to higher latencies on more shared caches between cores especially when other optimizations cannot be used. We show that this has a higher impact on latencies within containers than on bare metal or VMs, resulting in the selection of hardware architectures following optimizations as a critical challenge. Furthermore, the results reveal that the virtualization overhead does not impact tail latencies.
期刊介绍:
Performance Evaluation functions as a leading journal in the area of modeling, measurement, and evaluation of performance aspects of computing and communication systems. As such, it aims to present a balanced and complete view of the entire Performance Evaluation profession. Hence, the journal is interested in papers that focus on one or more of the following dimensions:
-Define new performance evaluation tools, including measurement and monitoring tools as well as modeling and analytic techniques
-Provide new insights into the performance of computing and communication systems
-Introduce new application areas where performance evaluation tools can play an important role and creative new uses for performance evaluation tools.
More specifically, common application areas of interest include the performance of:
-Resource allocation and control methods and algorithms (e.g. routing and flow control in networks, bandwidth allocation, processor scheduling, memory management)
-System architecture, design and implementation
-Cognitive radio
-VANETs
-Social networks and media
-Energy efficient ICT
-Energy harvesting
-Data centers
-Data centric networks
-System reliability
-System tuning and capacity planning
-Wireless and sensor networks
-Autonomic and self-organizing systems
-Embedded systems
-Network science