Container live migration serves as the cornerstone of maintaining containerized workloads in cloud and edge datacenters,particularly for stateful applications.However,the de facto memory pre-copy-based migration faces...Container live migration serves as the cornerstone of maintaining containerized workloads in cloud and edge datacenters,particularly for stateful applications.However,the de facto memory pre-copy-based migration faces severe performance issues for containers with dynamically changing memory dirty pages.Existing research often overlooks such dynamic nature of memory pages of various workloads and their unpredictable relationship with system-level features,causing unwise stop-and-copy iterations of container migrations.This can prolong container migrations by tens of seconds,severely degrading application performance.To address these challenges,we introduce U^(2)CMigration,a user-unaware container live migration strategy for containerized workloads.It employs a lightweight and autonomous two-phase prediction by analyzing container memory pages across various workloads.We utilize the data shift prediction for stable memory pages(phase-1).For unstable memory pages(phase-2),we develop an attention-based prediction that jointly considers the spatio-temporal characteristics of memory pages and system-level features.Guided by dirty page predictions,we further develop a container live migration strategy that judiciously decides the optimal stop-and-copy iteration with the minimum amount of memory dirty pages.We have implemented an open-source prototype of U^(2)CMigration(https://doi.org/10.57760/sciencedb.32136)based on the CRIU(checkpoint/restore in userspace)project.Extensive prototype experiments demonstrate that U^(2)CMigration reduces the container migration duration by 26.1%–47.9%and the downtime by 21.3%–32.6%compared with the state-of-the-art solutions.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No.62372184the Science and Technology Commission of Shanghai Municipality of China under Grant No.22DZ2229004the National Key Research and Development Plan of China under Grant No.2022YFB4501703.
文摘Container live migration serves as the cornerstone of maintaining containerized workloads in cloud and edge datacenters,particularly for stateful applications.However,the de facto memory pre-copy-based migration faces severe performance issues for containers with dynamically changing memory dirty pages.Existing research often overlooks such dynamic nature of memory pages of various workloads and their unpredictable relationship with system-level features,causing unwise stop-and-copy iterations of container migrations.This can prolong container migrations by tens of seconds,severely degrading application performance.To address these challenges,we introduce U^(2)CMigration,a user-unaware container live migration strategy for containerized workloads.It employs a lightweight and autonomous two-phase prediction by analyzing container memory pages across various workloads.We utilize the data shift prediction for stable memory pages(phase-1).For unstable memory pages(phase-2),we develop an attention-based prediction that jointly considers the spatio-temporal characteristics of memory pages and system-level features.Guided by dirty page predictions,we further develop a container live migration strategy that judiciously decides the optimal stop-and-copy iteration with the minimum amount of memory dirty pages.We have implemented an open-source prototype of U^(2)CMigration(https://doi.org/10.57760/sciencedb.32136)based on the CRIU(checkpoint/restore in userspace)project.Extensive prototype experiments demonstrate that U^(2)CMigration reduces the container migration duration by 26.1%–47.9%and the downtime by 21.3%–32.6%compared with the state-of-the-art solutions.
