The combination of service virtualization and edge computing allows mobile users to enjoy low latency services, while keeping data storage and processing local. However, the network edge has limited resource availability, and when both virtualized user applications and network functions need to be supported concurrently, a natural conflict in resource usage arises. In this paper, we focus on computing and radio resources and develop a framework for resource orchestration at the edge that leverages a model-free reinforcement learning approach and a Pareto analysis, which is proved to make fair and efficient decisions. Through our testbed, we demonstrate the effectiveness of our solution in resource-limited scenarios where standard multi-agent solutions violate the system’s capacity constraints systematically, e.g., over 70% violation rate with 2 vCPUs in our testbed. Index Terms—Virtual RAN, virtualized services, resource or- chestration, machine learning, experimental testbed

VERA: Resource Orchestration for Virtualized Services at the Edge / Tripathi, Sharda; Puligheddu, Corrado; Pramanik, Somreeta; Garcia-Saavedra, Andres; Chiasserini, Carla Fabiana. - STAMPA. - (2022). ((Intervento presentato al convegno IEEE International Conference on Communications (IEEE ICC 2022) tenutosi a Seoul, Korea, Republic of nel 16-20 May 2022 [10.1109/ICC45855.2022.9838935].

VERA: Resource Orchestration for Virtualized Services at the Edge

Corrado Puligheddu;Somreeta Pramanik;Carla Fabiana Chiasserini
2022

Abstract

The combination of service virtualization and edge computing allows mobile users to enjoy low latency services, while keeping data storage and processing local. However, the network edge has limited resource availability, and when both virtualized user applications and network functions need to be supported concurrently, a natural conflict in resource usage arises. In this paper, we focus on computing and radio resources and develop a framework for resource orchestration at the edge that leverages a model-free reinforcement learning approach and a Pareto analysis, which is proved to make fair and efficient decisions. Through our testbed, we demonstrate the effectiveness of our solution in resource-limited scenarios where standard multi-agent solutions violate the system’s capacity constraints systematically, e.g., over 70% violation rate with 2 vCPUs in our testbed. Index Terms—Virtual RAN, virtualized services, resource or- chestration, machine learning, experimental testbed
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2951281