Stateful Container Migration in Geo-Distributed Environments

Souza, Paulo Sérgio Lima; Miorandi, Daniele; Pierre, Guillaume

doi:10.1109/cloudcom49646.2020.00005

Cited by 21 publications

(13 citation statements)

References 21 publications

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“…Nevertheless, the transfer of the whole container file system results in a high network overload. In order to optimize and reduce the size of the container file system that is transferred from the source to the destination node, a number of works [54,55] take advantage of the layered structure of Docker. Docker storage is formed of several layers: base image layers are read-only while upper layer is read-write.…”

Section: Storage Migrationmentioning

confidence: 99%

“…Thus, read‐only layers can be fetched before the migration from a Docker repository (e.g., public cloud repository such as Docker Hub § or self‐hosted image hubs) while the thin top writable layer is transferred from the source to the destination node. Following, Junior et al 63 go one step further and also checkpoint the current state of the read‐write container layer, which further reduces the container's migration downtime.…”

Section: Classification Of Container Migrationmentioning

confidence: 99%

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Container placement and migration strategies for cloud, fog, and edge data centers: A survey

2022

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The last decade has witnessed important development of network softwarization that has revolutionized the practice of networks. Virtualized networks bring novel and specific requirements for the control and orchestration of containerized network functions that are scattered across the network. In this regard, the migration of virtualized network functions plays a pivotal role to best meet the requirements of optimal resource utilization, load balancing and fault tolerance. The purpose of this survey is to offer a detailed overview of the progress on container migration so as to provide a better understanding of the trade-off between the benefits associated with the migration and the practical challenges. The paper includes a classification of the placement algorithms that map the containerized network functions on the virtualized infrastructure. Following, a taxonomy of the migration techniques that perform the transfer of the containerized microservices is proposed.

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Section: Storage Migrationmentioning

confidence: 99%

Section: Classification Of Container Migrationmentioning

confidence: 99%

Container placement and migration strategies for cloud, fog, and edge data centers: A survey

2022

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“…To our best knowledge, at the time of writing this paper, there were only two works that integrated stateful service migration into K8s. In the first work [12], the authors only focused on the storage state. They proposed to use the OverlayFS file system to snapshot the persistent volume to retain the state of the data inside it.…”

Section: B Current Container Stateful Migration Techniquesmentioning

confidence: 99%

“…Apart from the default K8s solution, there are only two research that integrated the stateful migration feature into K8s. The first work [12] only snapshots and transfer the volume that stores the service data. To retain the inmemory state, the application might need to be redesigned to store this in-memory state on this volume.…”

Section: Introductionmentioning

confidence: 99%

Proactive Stateful Fault-Tolerant System for Kubernetes Containerized Services

Tran

Kim

2022

IEEE Access

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Recently, the development of Kubernetes (K8s) containerization platform has enabled cloudbased, lightweight, highly scalable, and agile services in both general and telco use-cases. Ensuring high availability, reliable and continuous containerized services is a major requirement of service providers to provide fault-tolerance, transparent service experiences to end-users. To satisfy this requirement, fault prediction and proactive stateful service recovery features must be applied in cloud systems. Prior proactive failure recovery approaches mostly focused on either improving fault prediction performance based on different machine learning time series forecasting techniques or optimizing recovery service placement after fault prediction. However, a mechanism that enables stateful containerized service migration from the predicted faulty node to the healthy destination node has not been studied. Service migration in previous proactive works is only simulated or performed by virtual machine (VM) migration techniques. In this paper, we propose a proactive stateful fault-tolerant system for K8s containerized services that pipelines a Bidirectional Long Short-Term Memory (Bi-LSTM) fault prediction framework and a novel K8s stateful service migration mechanism for service recovery. Experimental results show how the Bi-LSTM model improved prediction performance against other time-series forecasting models in prior proactive works. We then combined the Bi-LSTM fault prediction framework with both the default K8s and our stateful migration mechanisms. The comparison between these two proactive systems proves our system efficiency in terms of avoiding Quality of Service (QoS) violation. INDEX TERMS Containerization, proactive fault-tolerant, Kubernetes

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“…To support a stateful container migration in a geometrically distributed fog computing environment, another work [28] used an open-source file system (OverlayFS [27]) and took snapshots of containers to reduce network transfer time when migrating.…”

Section: Related Workmentioning

confidence: 99%

Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling

Lim

2021

Applied Sciences

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As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naïve or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks.

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Stateful Container Migration in Geo-Distributed Environments

Cited by 21 publications

References 21 publications

Container placement and migration strategies for cloud, fog, and edge data centers: A survey

Container placement and migration strategies for cloud, fog, and edge data centers: A survey

Proactive Stateful Fault-Tolerant System for Kubernetes Containerized Services

Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling

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