RT info:eu-repo/semantics/conferenceObject
T1 STEROCEN: Simulation and Training Environment for Resource Orchestration in Cloud-Edge Networks
A1 Ferens Michalek, Mieszko Jan
A1 Hortelano Haro, Diego
A1 Miguel Jiménez, Ignacio de
A1 Durán Barroso, Ramón José
A1 Kosta, Sokol
K1 Simulator
K1 Orchestration
K1 Edge-Cloud Computing
K1 Computation Offloading
K1 Reinforcement Learning
K1 IoT
AB Large scale deployment of Internet-of-Things (IoT) devices is projected to grow in the coming years. These devices are expected to be low-cost while supporting applications with growing computational demands. To enable the necessary computations, offloading of computational tasks to Edge and Cloud nodes is a fundamental technology. However, orchestration for such networks is a complex problem which affects both the network design and the decision system. To aid in solving this problem, simulation tools are essential for predicting the performance of networks in different conditions and under different orchestration policies. In this paper, we propose STEROCEN, a Cloud-Edge network resource orchestration simulation and training tool which allows for different configurations of up-to a four-layer network composed of: (i) end-device, (ii) Close Edge, (iii) Far Edge, and (iv) Cloud layers. Our tool collects delay metrics for flexibly defined applications, especially in regard to computation in the network nodes and including uncertainty in processing times. Additionally, the tool only needs the initial configuration and an independently defined orchestrator, allowing for testing of many strategies. As an example, we provide results of testing some Deep Reinforcement Learning (DRL) algorithms using the same training and simulation environment.
PB IEEE
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/76157
UL https://uvadoc.uva.es/handle/10324/76157
LA eng
NO 15th International Conference on Network of the Future (NoF), Castelldefels, Spain, 2024, pp. 133-141
NO Producción Científica
DS UVaDOC
RD 21-jul-2025