LLRS : A Low Latency Resource Scheduling in Cloud Continuum Orchestration
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| Titel: | LLRS : A Low Latency Resource Scheduling in Cloud Continuum Orchestration |
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| Autoren: | Mukuhi, David Kule, Outtagarts, Abdelkader |
| Weitere Verfasser: | kule Mukuhi, david |
| Quelle: | 2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN). :81-87 |
| Verlagsinformationen: | IEEE, 2024. |
| Publikationsjahr: | 2024 |
| Schlagwörter: | machine learning, low latency scheduling, Distributed Orchestration, [INFO] Computer Science [cs], Distributed Orchestration In-memory database low latency scheduling machine learning, In-memory database |
| Beschreibung: | The convergence of Network Function Virtualization (NFV) and Cloud Computing marks a transformative milestone in the telecommunications domain, setting the stage for the realization of 5G/6G flagship technologies. Containers, as a rising virtualization solution, have gained significant traction in recent years due to their attributes like shared host OS, swift launch times, and portability. A central challenge in this landscape is the effective orchestration of resources within cloud continuum infrastructure, ensuring seamless service operation. The abstraction of network services through NFV and the concept of network slicing amplify challenges in resource allocation. Kubernetes and Docker Swarm have emerged as powerful orchestration platforms. However, while widely adopted in application deployment, these solutions must meet the distinctive quality-of-service (QoS) demands of the telecommunications industry like Ultra-reliable low-latency communication. This paper proposes a Low Latency Resource Scheduler (LLRS) for Cloud Continuum. This new scheduling architecture approach leverages inmemory data grid and distributed resource predictions to reduce scheduling delay. Our experiments on heterogeneous environment has shown a reduction of nearly half the time required to schedule container in docker swarm and Kubernetes |
| Publikationsart: | Article Conference object |
| Dateibeschreibung: | application/pdf |
| DOI: | 10.1109/icin60470.2024.10494439 |
| Zugangs-URL: | https://hal.science/hal-04995146v1 https://hal.science/hal-04995146v1/document https://doi.org/10.1109/icin60470.2024.10494439 |
| Rights: | STM Policy #29 |
| Dokumentencode: | edsair.doi.dedup.....3c64d00a1cfd8b9cd421c7d5755cc74f |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | The convergence of Network Function Virtualization (NFV) and Cloud Computing marks a transformative milestone in the telecommunications domain, setting the stage for the realization of 5G/6G flagship technologies. Containers, as a rising virtualization solution, have gained significant traction in recent years due to their attributes like shared host OS, swift launch times, and portability. A central challenge in this landscape is the effective orchestration of resources within cloud continuum infrastructure, ensuring seamless service operation. The abstraction of network services through NFV and the concept of network slicing amplify challenges in resource allocation. Kubernetes and Docker Swarm have emerged as powerful orchestration platforms. However, while widely adopted in application deployment, these solutions must meet the distinctive quality-of-service (QoS) demands of the telecommunications industry like Ultra-reliable low-latency communication. This paper proposes a Low Latency Resource Scheduler (LLRS) for Cloud Continuum. This new scheduling architecture approach leverages inmemory data grid and distributed resource predictions to reduce scheduling delay. Our experiments on heterogeneous environment has shown a reduction of nearly half the time required to schedule container in docker swarm and Kubernetes |
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| DOI: | 10.1109/icin60470.2024.10494439 |