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AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments

Schuler, Lucia; Jamil, Somaya; Kühl, Niklas

Abstract (englisch):
Serverless computing has emerged as a compelling new paradigm of cloud computing models in recent years.
It promises the user services at large scale and low cost while eliminating the need for infrastructure management.
On cloud provider side, flexible resource management is required to meet fluctuating demand. It can be enabled through automated provisioning and deprovisioning of resources.
A common approach among both commercial and open source serverless computing platforms is workload-based auto-scaling, where a designated algorithm scales instances according to the number of incoming requests.
In the recently evolving serverless framework Knative a request-based policy is proposed, where the algorithm scales resources by a configured maximum number of requests that can be processed in parallel per instance, the so-called concurrency.
As we show in a baseline experiment, this predefined concurrency level can strongly influence the performance of a serverless application.
However, identifying the concurrency configuration that yields the highest possible quality of service is a challenging task due to various factors, e.g. ... mehr

Zugehörige Institution(en) am KIT Karlsruhe Service Research Institute (KSRI)
Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 21.05.2021
Sprache Englisch
Identifikator KITopen-ID: 1000130396
Erschienen in STEERS Workshop of the 21th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021)
Veranstaltung 21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021), Melbourne, Australien, 10.05.2021 – 13.05.2021
Nachgewiesen in arXiv
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