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
Nomadic Vehicular Cloud(NVC)is envisaged in this work.The predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic ...Nomadic Vehicular Cloud(NVC)is envisaged in this work.The predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road without relying on any of the static infrastructure and NVC decides the initiation time of container migration using cell transmission model(CTM).Containers are used in the place of Virtual Machines(VM),as containers’features are very apt to NVC’s dynamic environment.The specifications of 5G NR V2X PC5 interface are applied to NVC,for the feature of not relying on the network coverage.Nowa-days,the peak traffic on the road and the bottlenecks due to it are inevitable,which are seen here as the benefits for VC in terms of resource availability and residual in-network time.The speed range of high-end vehicles poses the issue of dis-connectivity among VC participants,that results the container migration failure.As the entire VC participants are on the move,to maintain proximity of the containers hosted by them,estimating their movements plays a vital role.To infer the vehicle movements on the road stretch and initiate the container migration prior enough to avoid the migration failure due to vehicles dynamicity,this paper proposes to apply the CTM to the container based and 5G NR V2X enabled NVC.The simulation results show that there is a significant increase in the success rate of vehicular cloud in terms of successful container migrations.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.
文摘Nomadic Vehicular Cloud(NVC)is envisaged in this work.The predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road without relying on any of the static infrastructure and NVC decides the initiation time of container migration using cell transmission model(CTM).Containers are used in the place of Virtual Machines(VM),as containers’features are very apt to NVC’s dynamic environment.The specifications of 5G NR V2X PC5 interface are applied to NVC,for the feature of not relying on the network coverage.Nowa-days,the peak traffic on the road and the bottlenecks due to it are inevitable,which are seen here as the benefits for VC in terms of resource availability and residual in-network time.The speed range of high-end vehicles poses the issue of dis-connectivity among VC participants,that results the container migration failure.As the entire VC participants are on the move,to maintain proximity of the containers hosted by them,estimating their movements plays a vital role.To infer the vehicle movements on the road stretch and initiate the container migration prior enough to avoid the migration failure due to vehicles dynamicity,this paper proposes to apply the CTM to the container based and 5G NR V2X enabled NVC.The simulation results show that there is a significant increase in the success rate of vehicular cloud in terms of successful container migrations.
