A variety of network management and orchestration (MANO) tasks take advantage of predictions to support anticipatory decisions. In many practical scenarios, such predictions entail two largely overlooked challenges: (i) the exact relationship between the predicted values (e.g., allocated resources) and the performance objective (e.g., quality of experience of end users) in many cases is tangled and cannot be known a priori, and (ii) multiple predictions contribute to the objective in an intertwined way (e.g., resources are limited and must be shared among competing flows). We present AutoManager, a novel meta-learning model that can support complex MANO tasks by addressing these two challenges.
Our solution learns how multiple intertwined predictions affect a common performance goal, and steers them so as to attain the correct operation point under a priori unknown loss function. We demonstrate AutoManager in practical use cases and with real-world traffic measurements, showing how it can achieve substantial gains over state-of-the-art approaches.
Telecommunication Engineering – ENSEIRB-MATMECA. Bordeaux, France Computer Sciences – Illinois Institute of Technology. Chicago, United States Now, Alan Collet is a Ph.D. student working on intent-based networking, more specifically on self-learning network intelligence.
This event will be conducted in English