RT Journal Article
T1 DeepCog: optimizing resource provisioning in network slicing with AI-based capacity forecasting
A1 Bega, Dario
A1 Gramaglia, Marco
A1 Fiore, Marco
A1 Banchs Roca, Albert
A1 Costa-Pérez, Xavier
AB The dynamic management of network resources is both a critical and challenging task in upcoming multi-tenant mobile networks, which requires allocating capacity to individual network slices so as to accommodate future time-varying service demands. Such an anticipatory resource configuration process must be driven by suitable predictors that take into account the monetary cost associated to overprovisioning or underprovisioning of networking capacity, computational power, memory, or storage. Legacy models that aim at forecasting traffic demands fail to capture these key economic aspects of network operation. To close this gap, we present DeepCog, a deep neural network architecture inspired by advances in image processing and trained via a dedicated loss function. Unlike traditional traffic volume predictors, DeepCog returns a cost-aware capacity forecast, which can be directly used by operators to take short- and long-term reallocation decisions that maximize their revenues. Extensive performance evaluations with real-world measurement data collected in a metropolitan-scale operational mobile network demonstrate the effectiveness of our proposed solution, which can reduce resource management costs by over 50% in practical case studies.
PB IEEE
SN 0733-8716
SN 1558-0008 (online)
YR 2020
FD 2020-02
LK https://hdl.handle.net/10016/29419
UL https://hdl.handle.net/10016/29419
LA eng
NO The work of University Carlos III of Madrid was supported by H2020 5G-TOURS project (grant agreement no. 856950). The work of NEC Laboratories Europe was supported by H2020 5G-TRANSFORMER project (grant agree-ment no. 761536) and 5GROWTH project (grant agreement no. 856709).
DS e-Archivo
RD 5 jul. 2024