Machine Learning-Powered Management Architectures for Edge Services in 5G Networks

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An RL approach to radio resource management in heterogeneous virtual RANs

Proceedings of: 2021 16th Annual Conference on Wireless On-demand Network Systems and Services Conference (WONS), 9-11 March 2021, Klosters, Switzerland.5G networks are primarily designed to support a wide range of services characterized by diverse key performance indicators (KPIs). A fundamental component of 5G networks, and a pivotal factor to the fulfillment of the services KPIs, is the virtual radio access network (RAN), which allows high flexibility on the control of the radio link. However, to fully exploit the potentiality of virtual RANs in non-stationary environments, an efficient mapping of the rapidly varying context to radio control decisions is not only essential, but also challenging owing to the non-trivial interdependence of network and channel conditions. In this paper, we propose CAREM, an RL framework for dynamic radio resource allocation, which selects the best link and modulation and coding scheme (MCS) for packet transmission, so as to meet the KPI requirements in heterogeneous virtual RANs. To show its effectiveness in real-world conditions, we provide a proof-of-concept through actual testbed implementation. Experimental results demonstrate that CAREM enables an efficient radio resource allocation, for any of the considered time periodicity of the decision-making process.This work has been supported by the EC H2020 5GPPP 5GROWTH project (Grant No. 856709.

A Context-aware Radio Resource Management in Heterogeneous Virtual RANs

New-generation wireless networks are designed to support a wide range of services with diverse key performance indicators (KPIs) requirements. A fundamental component of such networks, and a pivotal factor to the fulfillment of the services target KPIs, is the virtual radio access network (vRAN), which allows high flexibility on the control of the radio link. However, to fully exploit the potentiality of vRANs in non- stationary environments, an efficient mapping of the rapidly varying context to radio control decisions is not only essential, but also challenging owing to the interdependence of user traffic demand, channel conditions, and resource allocation. In this paper, we propose CAREM, a reinforcement learning framework for dynamic radio resource allocation in heterogeneous vRANs, which selects the best available link and transmission parameters for packet transfer, so as to meet the KPI requirements. To show its effectiveness in real-world conditions, we provide a proof-of- concept through a testbed implementation. Experimental results demonstrate that CAREM enables an efficient radio resource allocation, for different time periodicity of the decision-making process as well as under different settings and traffic demand. Furthermore, the results show that CAREM outperforms state- of-the-art solutions as well as standard cellular technologies: when compared to the closest existing scheme based on neural network and the standard LTE, it exhibits an improvement of about one order of magnitude in both packet loss and latency, while it provides a 65% latency improvement with respect to relatively simpler and more popular contextual bandit approach

Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services

The combination of service virtualization and edge computing allows for low latency services, while keeping data storage and processing local. However, given the limited resources available at the edge, a conflict in resource usage arises when both virtualized user applications and network functions need to be supported. Further, the concurrent resource request by user applications and network functions is often entangled, since the data generated by the former has to be transferred by the latter, and vice versa. In this paper, we first show through experimental tests the correlation between a video-based application and a vRAN. Then, owing to the complex involved dynamics, we develop a scalable reinforcement learning framework for resource orchestration at the edge, which leverages a Pareto analysis for provable fair and efficient decisions. We validate our framework, named VERA, through a real-time proof-of-concept implementation, which we also use to obtain datasets reporting real-world operational conditions and performance. Using such experimental datasets, we demonstrate that VERA meets the KPI targets for over 96% of the observation period and performs similarly when executed in our real-time implementation, with KPI differences below 12.4%. Further, its scaling cost is 54% lower than a centralized framework based on deep-Q networks

VERA: Resource Orchestration for Virtualized Services at the Edge

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 testbe

Automated service provisioning and hierarchical SLA management in 5G systems

Empowered by network softwarization, 5G systems have become the key enabler to foster the digital transformation of the vertical industries by expanding the scope of traditional mobile networks and enriching the network service offerings. To make this a reality, we propose an automation solution for vertical services provisioning and hierarchical Service Level Agreement (SLA) management. Service scaling is one of the most essential operations to adapt the service deployments and resource allocations to ensure SLA fulfilment. Three different scaling levels are addressed in this work: application-, service- and resource-level. We have implemented our solution in a proof-of-concept of a virtualized mobile network platform, spanning over three geographically-distributed sites. To evaluate our solution, we leverage field tests, focusing on automotive vertical services comprising a mission-critical application (collision-avoidance) and an entertainment one (video streaming). The results demonstrate the excellent performance of our solution, and its ability to automatically deploy vertical services and ensure their SLAs through different levels of service scaling.This work has been partially supported by European Commission H2020 5GPPP through the 5G-TRANSFORMER and 5GROWTH projects (Grants No. 761536 and 856709)

ML-driven provisioning and management of vertical services in automated cellular networks

One of the main tasks of new-generation cellular networks is the support of the wide range of virtual services that may be requested by vertical industries, while fulfilling their diverse performance requirements. Such task is made even more challenging by the time-varying service and traffic demands, and the need for a fully-automated network orchestration and management to reduce the service operational costs incurred by the network provider. In this paper, we address these issues by proposing a softwarized 5G network architecture that realizes the concept of ML-as-a-Service (MLaaS) in a flexible and efficient manner. The designed MLaaS platform can provide the different entities of a MANO architecture with already-trained ML models, ready to be used for decision making. In particular, we show how our MLaaS platform enables the development of two ML-driven algorithms for, respectively, network slice subnet sharing and run-time service scaling. The proposed approach and solutions are implemented and validated through an experimental testbed in the case of three different services in the automotive domain, while their performance is assessed through simulation in a large-scale, real-world scenario. In-testbed validation shows that the use of the MLaaS platform within the designed architecture and the ML-driven decision-making processes entail a very limited time overhead, while simulation results highlight remarkable savings in operational costs, e.g., up to 40% reduction in CPU consumption and up to 30% reduction in the OPEX.This work was supported by the EU Commission through the 5GROWTH project (Grant Agreement No. 856709), Spanish MINECO 5G-REFINE project (TEC2017-88373-R), and Generalitat de Catalunya 2017 SGR 1195.Publicad

SEM-O-RAN: Semantic and Flexible O-RAN Slicing for NextG Edge-Assisted Mobile Systems

5G and beyond cellular networks (NextG) will support the continuous execution of resource-expensive edge- assisted deep learning (DL) tasks. To this end, Radio Access Network (RAN) resources will need to be carefully “sliced” to satisfy heterogeneous application requirements while minimizing RAN usage. Existing slicing frameworks treat each DL task as equal and inflexibly define the resources to assign to each task, which leads to sub-optimal performance. In this paper, we propose SEM-O-RAN, the first semantic and flexible slicing framework for NextG Open RANs. Our key intuition is that different DL classifiers can tolerate different levels of image compression, due to the semantic nature of the target classes. Therefore, compression can be semantically applied so that the networking load can be minimized. Moreover, flexibility allows SEM-O-RAN to consider multiple edge allocations leading to the same task-related performance, which significantly improves system-wide performance as more tasks can be allocated. First, we mathematically formulate the Semantic Flexible Edge Slicing Problem (SF-ESP), demonstrate that it is NP-hard, and provide an approximation algorithm to solve it efficiently. Then, we evaluate the performance of SEM-O-RAN through extensive numerical analysis with state-of-the-art multi-object detection (YOLOX) and image segmentation (BiSeNet V2), as well as real- world experiments on the Colosseum testbed. Our results show that SEM-O-RAN improves the number of allocated tasks by up to 169% with respect to the state of the art

5Growth Data-Driven AI-Based Scaling

This paper presents a data-driven approach leveraging AI/ML models to automate the service scaling operation and, in this way, meet the service requirements while minimizing the consumption of network, computing, and storage resources. This approach is integrated into the 5Growth service management software platform. In particular, a prototype was developed to demonstrate how the novel 5Growth AI/ML platform can be used in a closed-loop automation system to support the automated service scaling operation. Furthermore, a number of additional ML-based approaches are developed in the context of eMBB and C-V2N scenarios, which can be embedded into the system for handling more complex use cases

5Growth Data-driven AI-based Scaling

This paper presents a data-driven approach leveraging AI/ML models to automate the service scaling operation and, in this way, meet the service requirements while minimizing the consumption of network, computing, and storage resources. This approach is integrated into the 5Growth service management software platform. In particular, a prototype was developed to demonstrate how the novel 5Growth AI/ML platform can be used in a closed-loop automation system to support the automated service scaling operation. Furthermore, a number of additional ML-based approaches are developed in the context of eMBB and C-V2N scenarios, which can be embedded into the system for handling more complex use cases