Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities
Docker containers are the lightweight-virtualization technology prevailing today for the provision of microservices. This work raises and discusses two main challenges in Docker containers’ scheduling in cloud-fog-internet of things (IoT) networks. First, the convenience to integrate intel...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/6/1714 |
| _version_ | 1817991509129035776 |
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| author | Rocío Pérez de Prado Sebastián García-Galán José Enrique Muñoz-Expósito Adam Marchewka Nicolás Ruiz-Reyes |
| author_facet | Rocío Pérez de Prado Sebastián García-Galán José Enrique Muñoz-Expósito Adam Marchewka Nicolás Ruiz-Reyes |
| author_sort | Rocío Pérez de Prado |
| collection | DOAJ |
| description | Docker containers are the lightweight-virtualization technology prevailing today for the provision of microservices. This work raises and discusses two main challenges in Docker containers’ scheduling in cloud-fog-internet of things (IoT) networks. First, the convenience to integrate intelligent containers’ schedulers based on soft-computing in the dominant open-source containers’ management platforms: Docker Swarm, Google Kubernetes and Apache Mesos. Secondly, the need for specific intelligent containers’ schedulers for the different interfaces in cloud-fog-IoT networks: cloud-to-fog, fog-to-IoT and cloud-to-fog. The goal of this work is to support the optimal allocation of microservices provided by the main cloud service providers today and used by millions of users worldwide in applications such as smart health, content delivery networks, smart health, etc. Particularly, the improvement is studied in terms of quality of service (QoS) parameters such as latency, load balance, energy consumption and runtime, based on the analysis of previous works and implementations. Moreover, the scientific-technical impact of smart containers’ scheduling in the market is also discussed, showing the possible repercussion of the raised opportunities in the research line. |
| first_indexed | 2024-04-14T01:13:08Z |
| format | Article |
| id | doaj.art-81cadec31bd14b57a159cfb213d06ed9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T01:13:08Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-81cadec31bd14b57a159cfb213d06ed92022-12-22T02:20:57ZengMDPI AGSensors1424-82202020-03-01206171410.3390/s20061714s20061714Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and OpportunitiesRocío Pérez de Prado0Sebastián García-Galán1José Enrique Muñoz-Expósito2Adam Marchewka3Nicolás Ruiz-Reyes4Telecommunication Engineering Department, University of Jaén, Science and Technology Campus, 23700 Linares (Jaén), SpainTelecommunication Engineering Department, University of Jaén, Science and Technology Campus, 23700 Linares (Jaén), SpainTelecommunication Engineering Department, University of Jaén, Science and Technology Campus, 23700 Linares (Jaén), SpainInstitute of Telecommunications and Informatics, University of Technology and Life Sciences, Prof. S. Kaliskiego 7, 85-791 Bydgoszcz, PolandTelecommunication Engineering Department, University of Jaén, Science and Technology Campus, 23700 Linares (Jaén), SpainDocker containers are the lightweight-virtualization technology prevailing today for the provision of microservices. This work raises and discusses two main challenges in Docker containers’ scheduling in cloud-fog-internet of things (IoT) networks. First, the convenience to integrate intelligent containers’ schedulers based on soft-computing in the dominant open-source containers’ management platforms: Docker Swarm, Google Kubernetes and Apache Mesos. Secondly, the need for specific intelligent containers’ schedulers for the different interfaces in cloud-fog-IoT networks: cloud-to-fog, fog-to-IoT and cloud-to-fog. The goal of this work is to support the optimal allocation of microservices provided by the main cloud service providers today and used by millions of users worldwide in applications such as smart health, content delivery networks, smart health, etc. Particularly, the improvement is studied in terms of quality of service (QoS) parameters such as latency, load balance, energy consumption and runtime, based on the analysis of previous works and implementations. Moreover, the scientific-technical impact of smart containers’ scheduling in the market is also discussed, showing the possible repercussion of the raised opportunities in the research line.https://www.mdpi.com/1424-8220/20/6/1714fog computingiotcloud computingsoft-computingmachine learningcontainersdockermicroservicesintelligent schedulingcloud service providers |
| spellingShingle | Rocío Pérez de Prado Sebastián García-Galán José Enrique Muñoz-Expósito Adam Marchewka Nicolás Ruiz-Reyes Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities Sensors fog computing iot cloud computing soft-computing machine learning containers docker microservices intelligent scheduling cloud service providers |
| title | Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities |
| title_full | Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities |
| title_fullStr | Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities |
| title_full_unstemmed | Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities |
| title_short | Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities |
| title_sort | smart containers schedulers for microservices provision in cloud fog iot networks challenges and opportunities |
| topic | fog computing iot cloud computing soft-computing machine learning containers docker microservices intelligent scheduling cloud service providers |
| url | https://www.mdpi.com/1424-8220/20/6/1714 |
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