期刊
SENSORS
卷 22, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/s22051755
关键词
orchestration; Cloud-Edge continuum; Edge computing; fog computing; quality of service
资金
- European Commission through the ELASTIC project [825473]
- Spanish Ministry of Science, Innovation and Universities [RTI2018-096116-B-I00]
- Basque Government through the Qualyfamm project [Elkartek KK-2020/00042]
- Basque Government [IT1324-19]
The fast growth in the amount of connected devices has enabled the emergence of Edge computing, which provides lower latencies compared to Cloud computing. Combining Cloud and Edge computing can meet the quality of service requirements for complex applications. However, orchestrating applications in the Cloud-Edge computing faces new challenges that need to be solved to fully utilize this layered infrastructure. This paper proposes an architecture that dynamically orchestrates applications in the Cloud-Edge continuum, focusing on the application's quality of service.
The fast growth in the amount of connected devices with computing capabilities in the past years has enabled the emergence of a new computing layer at the Edge. Despite being resource-constrained if compared with cloud servers, they offer lower latencies than those achievable by Cloud computing. The combination of both Cloud and Edge computing paradigms can provide a suitable infrastructure for complex applications' quality of service requirements that cannot easily be achieved with either of these paradigms alone. These requirements can be very different for each application, from achieving time sensitivity or assuring data privacy to storing and processing large amounts of data. Therefore, orchestrating these applications in the Cloud-Edge computing raises new challenges that need to be solved in order to fully take advantage of this layered infrastructure. This paper proposes an architecture that enables the dynamic orchestration of applications in the Cloud-Edge continuum. It focuses on the application's quality of service by providing the scheduler with input that is commonly used by modern scheduling algorithms. The architecture uses a distributed scheduling approach that can be customized in a per-application basis, which ensures that it can scale properly even in setups with high number of nodes and complex scheduling algorithms. This architecture has been implemented on top of Kubernetes and evaluated in order to asses its viability to enable more complex scheduling algorithms that take into account the quality of service of applications.
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