A Taxonomy and Survey on Container Migration Techniques in Cloud Computing

Singh, Gursharan; Singh, Parminder

doi:10.1007/978-981-15-9554-7_36

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…Among general purpose surveys, most surveys specifically deal with VMs for edge or fog [14][15][16] computing. Only few recent surveys [8][9][10] specifically concern the container-enabled NFV migration and each focuses either on edge [8], fog [9] or cloud migration [10], With mobile edge computing [8], the user mobility is the key aspect that is considered. In [9], a detailed analysis outlines the trade-off that is made between the benefit associated with migrating a service (e.g., QoS improvement) and the related cost, which also brings out architectural design and implementation.…”

Section: Related Surveysmentioning

confidence: 99%

“…With the virtualization gaining attention, many surveys on VM (e.g., previous studies [2][3][4][5][6][7][8] ) and container (e.g., literature [9][10][11] ) attempt summarizing the different virtual machine (VM) and container migration techniques and their challenges, from a general perspective, without special emphasis on their usage to support network softwarization. In addition, some surveys [12][13][14] provide a comprehensive state of the art on NFV solutions, introducing the basic concepts and principles associated with network slicing and virtualization considering in majority VM-enabled virtualization.…”

Section: Related Surveysmentioning

confidence: 99%

“…Among general purpose surveys, most surveys specifically deal with VMs for edge or fog [15][16][17] computing. Only few recent surveys [9][10][11] specifically address the container-enabled NFV migration and each focuses either on edge, 9 fog, 10 or cloud migration. 11 With mobile edge computing, 9 the user mobility is the key aspect that is considered.…”

Section: Related Surveysmentioning

confidence: 99%

“…In Rejiba et al, 10 a detailed analysis outlines the trade‐off that is made between the benefit associated with migrating a service (e.g., QoS improvement) and the related cost, which also brings out architectural design and implementation. In Singh and Singh, 11 authors classify various techniques for cloud and container‐based migration on the basis of the following taxonomy: (i) architecture, (ii) tools, (iii) purpose, (iv) scope, (v) migration technique, and (vi) evaluation.…”

Section: Introductionmentioning

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: Related Surveysmentioning

confidence: 99%

Section: Related Surveysmentioning

confidence: 99%

Section: Related Surveysmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Research on device mobility has been considered in the context of migration. Migration on the edge has been investigated in the literature, more specifically by exploring VM migration [6] and container migration [7], [8]. However, migration in edge-based FL is minimally considered.…”

Section: Introductionmentioning

confidence: 99%

FedFly: Toward Migration in Edge-Based Distributed Federated Learning

Ullah

Harvey

et al. 2022

IEEE Commun. Mag.

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Federated learning (FL) is a privacy-preserving distributed machine learning technique that trains models while keeping all the original data generated on devices locally. Since devices may be resource constrained, offloading can be used to improve FL performance by transferring computational workload from devices to edge servers. However, due to mobility, devices participating in FL may leave the network during training and need to connect to a different edge server. This is challenging because the offloaded computations from edge server need to be migrated. In line with this assertion, we present FedFly, which is, to the best of our knowledge, the first work to migrate a deep neural network (DNN) when devices move between edge servers during FL training. Our empirical results on the CIFAR-10 dataset, with both balanced and imbalanced data distribution, support our claims that FedFly can reduce training time by up to 33% when a device moves after 50% of the training is completed, and by up to 45% when 90% of the training is completed when compared to state-of-the-art offloading approach in FL. FedFly has negligible overhead of up to two seconds and does not compromise accuracy. Finally, we highlight a number of open research issues for further investigation. FedFly can be downloaded from https://github.com/qub-blesson/FedFly.

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