Over-the-Top Resource Broker System for Split Computing: An Approach to Distribute Cloud Computing Infrastructure

6G network architectures will usher in a wave of innovative services and capabilities, introducing concepts like split computing and dynamic processing nodes. This implicates a paradigm where accessing resources seamlessly aligns with diverse processing node characteristics, ensuring a uniform interface. In this landscape, the identity of the operator becomes inconsequential, paving the way for a collaborative ecosystem where multiple providers contribute to a shared pool of resources. At the core of this vision is the guarantee of specific performance parameters, precisely tailored to the location and service requirements. A consistent layer, as the abstraction of the complexities of different infrastructure providers, is needed to simplify service deployment. One promising approach is the introduction of an over-the-top broker for resource allocation, which streamlines the integration of these services into the network and cloud infrastructure of the future. This paper explores the role of the broker in two split computing scenarios. By abstracting the complexities of various infrastructures, the broker proves to be a versatile solution applicable not only to cloud environments but also to networks and beyond. Additionally, a detailed discussion of a proof-of-concept implementation provides insights into the broker's actual architectural framework.

Efficient Federated Learning Tiny Language Models for Mobile Network Feature Prediction

In telecommunications, Autonomous Networks (ANs) automatically adjust configurations based on specific requirements (e.g., bandwidth) and available resources. These networks rely on continuous monitoring and intelligent mechanisms for self-optimization, self-repair, and self-protection, nowadays enhanced by Neural Networks (NNs) to enable predictive modeling and pattern recognition. Here, Federated Learning (FL) allows multiple AN cells - each equipped with NNs - to collaboratively train models while preserving data privacy. However, FL requires frequent transmission of large neural data and thus an efficient, standardized compression strategy for reliable communication. To address this, we investigate NNCodec, a Fraunhofer implementation of the ISO/IEC Neural Network Coding (NNC) standard, within a novel FL framework that integrates tiny language models (TLMs) for various mobile network feature prediction (e.g., ping, SNR or band frequency). Our experimental results on the Berlin V2X dataset demonstrate that NNCodec achieves transparent compression (i.e., negligible performance loss) while reducing communication overhead to below 1%, showing the effectiveness of combining NNC with FL in collaboratively learned autonomous mobile networks.