DDRM: An SLO-aware Deep Dynamic Resource Management Framework for Microservices

Loosely coupled microservice architectures have been widely adopted in cloud-native applications due to their inherent advantages in modularity, development agility, and scalability. However, the resulting complex and dynamic service topologies introduce intricate inter-service dependencies, which o...

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Veröffentlicht in:	Proceedings / IEEE International Conference on Cluster Computing S. 1 - 12
Hauptverfasser:	Tang, Liangping, Wang, Jin, Wang, Wanyou, Shi, Gaotao, Li, Zhijun
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 02.09.2025
Schlagworte:	Adaptation models Cloud computing Collaboration Deep learning deep learning for systems Dynamic scheduling Microservice architectures microservices resource efficiency resource man-agement Resource management Scalability Stability analysis Thermal stability
ISSN:	2168-9253
Online-Zugang:	Volltext
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