Latest revision as of 21:34, 22 March 2021

Abstract

The adoption of advanced Internet of Things (IoT) technologies has impressively improved in recent years by placing such services at the extreme Edge of the network. There are, however, specific Quality of Service (QoS) trade-offs that must be considered, particularly in situations when workloads vary over time or when IoT devices are dynamically changing their geographic position. This article proposes an innovative capillary computing architecture, which benefits from mainstream Fog and Cloud computing approaches and relies on a set of new services, including an Edge/Fog/Cloud Monitoring System and a Capillary Container Orchestrator. All necessary Microservices are implemented as Docker containers, and their orchestration is performed from the Edge computing nodes up to Fog and Cloud servers in the geographic vicinity of moving IoT devices. A car equipped with a Motorhome Artificial Intelligence Communication Hardware (MACH) system as an Edge node connected to several Fog and Cloud computing servers was used for testing. Compared to using a fixed centralized Cloud provider, the service response time provided by our proposed capillary computing architecture was almost four times faster according to the 99th percentile value along with a significantly smaller standard deviation, which represents a high QoS.

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• http://dx.doi.org/10.3390/s18092938 under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.3390/s18092938 under the license http://creativecommons.org/licenses/by/3.0/

• http://europepmc.org/articles/PMC6164252 under the license https://creativecommons.org/licenses/by/4.0

• https://dx.doi.org/10.3390/s18092938

• https://www.mdpi.com/1424-8220/18/9/2938/pdf

• https://zenodo.org/record/1414352 under the license cc-by

• http://www.mdpi.com/1424-8220/18/9/2938,

https://doaj.org/toc/1424-8220 under the license http://creativecommons.org/licenses/by/4.0/legalcode

• http://www.mdpi.com/1424-8220/18/9/2938/pdf,

http://dx.doi.org/10.3390/s18092938

• https://dblp.uni-trier.de/db/journals/sensors/sensors18.html#TaherizadehSG18,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164252,

https://europepmc.org/articles/PMC6164252,

http://doi.org/10.3390/s18092938,

https://doi.org/10.3390/s18092938,

https://academic.microsoft.com/#/detail/2892258624 under the license https://creativecommons.org/licenses/by/4.0/