Intent-based network operation is an essential paradigm to create autonomous networks as it eliminates the complexity of manual configurations and replaces it with an abstracted, automated method. To enable intent-based networks that can handle and resolve conflicts under shared and limited network resources, prediction of the impact of proposed configuration and topology changes is crucial. In this respect, Graph Neural Networks (GNN)s are gaining attention in the networking domain thanks to their ability to process data based on close to real modeling of networks as a graph. In this paper, we develop a GNN framework of intent-based networks that takes the network information as inputs and predicts the specific key performance indicators (KPIs) of intents. We propose to make use of GNNs for accurate prediction of the impacts of proposed actions on all of the active KPIs of an intent-based network. Experiments with different configurations are conducted and the empirical evaluations demonstrate that a GNN based prediction agent outperforms a baseline neural-network based agent in both prediction performance and ability to generalize to previously unseen configurations.

The ever-growing complexity in networks and the various future use cases with diverse, and often stringent performance requirements call for a higher level of automation. A tool to achieve this higher level of automation is intent-based management, where the human operator only communicates via high-level intents with the network. Intents are declarative in nature, they specify the desired state but don’t specify how to achieve it. The system will assure that the intents are fulfilled, by monitoring the behavior of the network, and adjusting its configurations if needed. In future networks that will serve multiple tenants and use cases at the same time, many intents will co-exist. As the resources are limited, conflicts may arise between intents. Therefore, a solution is needed for conflict detection and resolution. In this paper, we present a system that is capable of handling multiple intents and detecting and resolving conflicts at run-time. To show the feasibility, we implement our approach in an end-to-end prototype.

Location-based social networks (LBSNs) such as Foursquare offer a platform for users to share and be aware of each other’s physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit (or check-in) at the locations visited by their friends. Quantifying such influences in these LBSNs is useful in various settings such as location promotion, personalized recommendations, mobility pattern prediction, and so forth. In this article, we develop a model to quantify the influence specific to a location between a pair of users. Specifically, we develop a framework called LoCaTe , that combines (a) a user mobility model based on kernel density estimates; (b) a model of the semantics of the location using topic models; and (c) a user correlation model that uses an exponential distribution. We further develop LoCaTe+ , an advanced model within the same framework where user correlation is quantified using a Mutually Exciting Hawkes Process. We show the applicability of LoCaTe and LoCaTe+ for location promotion and location recommendation tasks using LBSNs. Our models are validated using a long-term crawl of Foursquare data collected between January 2015 and February 2016, as well as other publicly available LBSN datasets. Our experiments demonstrate the efficacy of the LoCaTe framework in capturing location-specific influence between users. We also show that our models improve over state-of-the-art models for the task of location promotion as well as location recommendation.

Location-based social networks (LBSNs) such as Foursquare offer a platform for users to share and be aware of each other’s physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit (or check-in) at the locations visited by their friends. Quantifying such influences in these LBSNs is useful in various settings such as location promotion, personalized recommendations, mobility pattern prediction etc. In this paper, we focus on the problem of location promotion and develop a model to quantify the influence specific to a location between a pair of users. Specifically, we develop a joint model called LoCaTe, consisting of (i) user mobility model estimated using kernel density estimates; (ii) a model of the semantics of the location using topic models; and (iii) a model of time-gap between check-ins using exponential distribution. We validate our model on a long-term crawl of Foursquare data collected between Jan 2015 Feb 2016, as well as on publicly available LBSN datasets. Our experiments demonstrate that LoCaTe significantly outperforms state-of-the-art models for the same task.

Shortest path querying is a fundamental graph problem which is computationally quite challenging when operating over massive scale graphs. Recent results have addressed the problem of computing either exact or good approximate shortest path distances efficiently. Some of these techniques also return the path corresponding to the estimated shortest path distance fast.