5G advanced network slicing use cases have gained momentum with the ability to provide differentiated Quality of Service (QoS) to various services (voice, mobile broadband, gaming, eXtended Reality, enterprise). However, it is essential to implement adaptive and scalable Network Slice Assurance to ensure differentiated service performance with varying traffic and dynamic requirements. The intent-driven network management paradigm aims at building autonomous systems with minimal expert-driven policies, which may be applied to slice assurance. In this paper, we propose Convergence, a cognitive intent driven framework for 5G Radio Access Network (RAN) slice assurance. Via the use of intelligent agents registered during the prediction, assurance, evaluation and actuation phases, emerging issues in slice assurance may be handled autonomously. In particular, Artificial Intelligence (AI) planning agents are exploited to generate partition over-use, partition top-up or partition ramp-down actions dynamically. The slice assurance framework is demonstrated over a real-world operator use case with multiple slices and intent specifications.

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.

Realization of industry-scale, goal-driven, autonomous systems with AI planning technology faces several challenges: flexibly specifying planning goal states in varying situations, synthesizing plans in large state spaces, re-planning in dynamic situations, and facilitating humans to supervise, give feedback and intervene. In this paper, we present Intent-driven Strategic Tactical Planning (ISTP) to address these challenges. We demonstrate its efficacy through its application for radio base station inspection across several locations using drones. The inspection task involves capturing images, thermal images or signal measurements - called knowledge-objects - of various components of the base stations for downstream processing. In the ISTP approach, an operator indicates her goals by flying the drone to different components of interest. These goals are generalized to capture the intent of the operator, which are then instantiated in new situations to generate goals dynamically. Towards planning and re-planning in large state spaces to achieve these goals efficiently, we extend the Strategic-Tactical Planning paradigm. All the components of ISTP are integrated in an intuitive UI and demonstrated through a real life use-case built on the UNITY simulator platform.

This paper describes the process of assigning students to project teams in a global software development course. Project proposals vary in several characteristics in order to provide students from two universities, who have highly heterogeneous knowledge, a wide range of choices. Students submit an initial questionnaire about their skills, together with ranked project preferences. Based on 24 projects and 155 students in last three years of the course, project types and criteria such as students' knowledge of programming languages or development platforms are analyzed and presented.

The need for educating future software engineers in the field of global software engineering is recognized by many educational institutions. In this paper we outline the characteristics of an existing global software development course run over a period of nine years, and present a flexible project framework for conducting student projects in a distributed environment. Based on data collected from fourteen distributed student projects, a set of collaboration patterns is identified and their causes and implications described. Collaboration patterns are a result of the analysis of collaboration links within distributed student teams, and can assist teachers in better understanding of the dynamics found in distributed projects.

In this paper, we present a tool-chain for the REMES language, which can be used for the construction and analysis of embedded system behavioral models. The tool-chain consists of the following tools: (i) a REMES editor for modeling behaviors of embedded components, (ii) a REMES simulator to test timing and resource behavior prior to formal analysis, and (iii) an automated transformation from REMES to Priced Timed Automata, needed for formal analysis.