The SYRROCA AI-empowered network automation platform

Diamanti, Alessio; Vílchez, José Manuel Sánchez; Secci, Stefano

doi:10.1109/icin51074.2021.9385535

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…For the placement, we introduce binary variables y j , j ∈ N , that take value 1 if the FL server is installed on node j and 0 otherwise, and binary variables x i that take value 1 if an AIF is activated on node i and 0 otherwise. The objective function (1) minimizes the number of activated AIFs. Constraints (2) impose that one FL server is installed, and Constraints (3) that the same node does not host both the FL server and an edge AIF.…”

Section: B Problem Formulationmentioning

confidence: 99%

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Function Placement and Acceleration for In-Network Federated Learning Services

Yellas

Addis

Riggio

et al. 2022

2022 18th International Conference on Network and Service Management (CNSM)

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Edge intelligence combined with federated learning is considered as a way to distributed learning and inference tasks in a scalable way, by analyzing data close to where it is generated, unlike traditional cloud computing where data is offloaded to remote servers. In this paper, we address the placement of Artificial Intelligence Functions (AIF) making use of federated learning and hardware acceleration. We model the behavior of federated learning and related inference point to guide the placement decision, taking into consideration the specific constraint and the empirical behavior of a virtualized infrastructure anomaly detection use-case. Besides hardware acceleration, we consider the specific training time trend when distributing training over a network, by using empirical piece-wise linear distributions. We model the placement problem as a MILP and we propose a variant of the problem. Simulation results show the impact that hardware acceleration can have in the decision of the number of AIF to enable, while dividing by a relevant factor the distributed training time. We also show how our approach exacerbates the importance of monitoring an end-to-end learning system delay budget composed of link propagation delay and distributed training time in the location of AIFs.

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Section: B Problem Formulationmentioning

confidence: 99%

“…Indeed, anomaly detection is a fundamental brick of a general purpose automation system. Many frameworks exist, such as the one presented in [1], taking into consideration a potentially large size (e.g. thousands) and heterogeneous set of monitoring time-series.…”

Section: Introductionmentioning

confidence: 99%

Function Placement and Acceleration for In-Network Federated Learning Services

Yellas

Addis

Riggio

et al. 2022

2022 18th International Conference on Network and Service Management (CNSM)

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5G Network Management System With Machine Learning Based Analytics

et al. 2022

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Application of intelligent data analytics using machine learning in management of 5G networks can enable autonomous networking capabilities in 5G networks. This paper describes the design and implementation of CygNet MaSoN, a management system supporting advanced aggregation and analytics features combined with machine learning. The system supports detection of anomalous network behaviour, detection of degradation in network performance and service quality and also supports resource optimization. The main objective is to achieve self-organizing and closed loop automation functionalities expected as part of autonomous functioning of 5G networks. Details of the system architecture and components are presented. Three real-life use cases implemented on this system are then described. The features considered, machine learning models built and synthetic data generation methods adopted are presented. The results obtained using the MaSoN system are also presented to demonstrate the effectiveness of the system in 5G network operations.

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OROS: Online Operation and Orchestration of Collaborative Robots Using 5G

Romero,

Delgado,

Zanzi

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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The 5G mobile networks extend the capability for supporting collaborative robot operations in outdoor scenarios. However, the restricted battery life of robots still poses a major obstacle to their effective implementation and utilization in real scenarios. One of the most challenging situations is the execution of mission-critical tasks that require the use of various onboard sensors to perform simultaneous localization and mapping (SLAM) of unexplored environments. Given the time-sensitive nature of these tasks, completing them in the shortest possible time is of the highest importance. In this paper, we analyze the benefits of 5G-enabled collaborative robots by enhancing the intelligence of the robot operation through joint orchestration of Robot Operating System (ROS) and 5G resources for energysaving goals, addressing the problem from both offline and online manners. We propose OROS, a novel orchestration approach that minimizes mission-critical task completion times as well as overall energy consumption of 5G-connected robots by jointly optimizing robotic navigation and sensing together with infrastructure resources. We validate our 5G-enabled collaborative framework by means of Matlab/Simulink, ROS software and Gazebo simulator. Our results show an improvement between 3.65% and 11.98% in exploration task by exploiting 5G orchestration features for battery savings when using 3 robots.

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The SYRROCA AI-empowered network automation platform

Cited by 3 publications

References 4 publications

Function Placement and Acceleration for In-Network Federated Learning Services

Function Placement and Acceleration for In-Network Federated Learning Services

5G Network Management System With Machine Learning Based Analytics

OROS: Online Operation and Orchestration of Collaborative Robots Using 5G

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