Docker Container Deployment in Distributed Fog Infrastructures with Checkpoint/Restart

Ahmed, Arif; Mohan, Apoorve; Cooperman, Gene; Pierre, Guillaume

doi:10.1109/mobilecloud48802.2020.00016

Cited by 14 publications

(3 citation statements)

References 15 publications

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“…Last but not least, a number of works use a similar approach to ours which exploits the layered file system structure of Docker containers. Ahmed et al checkpoint a container's state and its layered file system structure, but use the checkpoint to restart multiple identical containers rather than migrate the checkpointed container [22]. Ma et al propose to download the container's read-only image files in the target before migrating the container [23].…”

Section: Related Workmentioning

confidence: 99%

Stateful Container Migration in Geo-Distributed Environments

Souza

Miorandi

Pierre

2020

2020 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

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Container migration is an essential functionality in large-scale geo-distributed platforms such as fog computing infrastructures. Contrary to migration within a single data center, long-distance migration requires that the container's disk state should be migrated together with the container itself. However, this state may be arbitrarily large, so its transfer may create long periods of unavailability for the container. We propose to exploit the layered structure provided by the OverlayFS file system to transparently snapshot the volumes' contents and transfer them prior to the actual container migration. We implemented this mechanism within Kubernetes. Our evaluations based on a real fog computing test-bed show that our techniques reduce the container's downtime during migration by a factor 4 compared to a baseline with no volume checkpoint.

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Section: Related Workmentioning

confidence: 99%

Stateful Container Migration in Geo-Distributed Environments

Souza

Miorandi

Pierre

2020

2020 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

Self Cite

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“…This helps in the improved performance of the Internet of Things (IoT) devices. The fog architecture is usually split into multiple presence-of-points to achieve improved performance [10].…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization-based Light Weight Container (ACO-LWC) Algorithm for Efficient Load Balancing

Aruna¹,

Pradeep²

2022

Intelligent Automation &Amp; Soft Computing

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Container technology is the latest lightweight virtualization technology which is an alternate solution for virtual machines. Docker is the most popular container technology for creating and managing Linux containers. Containers appear to be the most suitable medium for use in dynamic development, packaging, shipping and many other information technology environments. The portability of the software through the movement of containers is appreciated by businesses and IT professionals. In the docker container, one or more processes may run simultaneously. The main objective of this work is to propose a new algorithm called Ant Colony Optimization-based Light Weight Container (ACO-LWC) load balancing scheduling algorithm for scheduling various process requests. This algorithm is designed such that it shows best performance in terms of load balancing. The proposed algorithm is validated by comparison with two existing load balancing scheduling algorithms namely, least connection algorithm and round robin algorithm. The proposed algorithm is validated using metrics like response time (ms), mean square error (MSE), node load, largest Transactions Per Second (TPS) of cluster (fetches/sec), average response time for each request (ms) and run time (s). Quantitative analysis show that the proposed ACO-LWC scheme achieves best performance in terms of all the metrics compared to the existing algorithms. In particular, the response time for least connection, round robin and the proposed ACO-LWC algorithm are 58, 60 and 48 ms respectively when 95% requests are finished. Similarly, the error for scheduling 120 requests using least connection, round robin and the proposed ACO-LWC algorithm are 0.15, 0.11 and 0.06 respectively.

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“…This helps in the improved performance of the IoT devices. The fog architecture is usually split into multiple presence-of-points to achieve improved performance [10].…”

Section: Introductionmentioning

confidence: 99%

Development And Analysis of Ant Colony Optimization-Based Light Weight Container (ACO-LWC) Algorithm For Efficient Load Balancing

Aruna

Pradeep

2021

Preprint

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Container technology is the latest lightweight virtualization technology which is an alternate solution for virtual machines. Docker is the most popular container technology for creating and managing Linux containers. Containers appear to be the most suitable medium for use in dynamic development, packaging, shipping and many other information technology environments. The portability of the software through the movement of containers is appreciated by businesses and IT professionals. In the docker container, one or more processes may run simultaneously. The main objective of this work is to propose a new algorithm called Ant Colony Optimization-based Light Weight Container (ACO-LWC) load balancing scheduling algorithm for scheduling various process requests. This algorithm is designed such that it shows best performance in terms of load balancing. The proposed algorithm is validated by comparison with two existing load balancing scheduling algorithms namely, least connection algorithm and round robin algorithm. The proposed algorithm is validated using metrics like response time (ms), mean square error (MSE), node load, largest TPS of cluster (fetches/sec), average response time for each request (ms) and run time (s). Quantitative analysis show that the proposed ACO-LWC scheme achieves best performance in terms of all the metrics compared to the existing algorithms. In particular, the response time for least connection, round robin and the proposed ACO-LWC algorithm are 58ms, 60ms and 48ms respectively when 95% requests are finished. Similarly, the error for scheduling 120 requests using least connection, round robin and the proposed ACO-LWC algorithm are 0.15, 0.11 and 0.06 respectively.

show abstract

Docker Container Deployment in Distributed Fog Infrastructures with Checkpoint/Restart

Cited by 14 publications

References 15 publications

Stateful Container Migration in Geo-Distributed Environments

Stateful Container Migration in Geo-Distributed Environments

Ant Colony Optimization-based Light Weight Container (ACO-LWC) Algorithm for Efficient Load Balancing

Development And Analysis of Ant Colony Optimization-Based Light Weight Container (ACO-LWC) Algorithm For Efficient Load Balancing

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