The transition to AI-native 6G networks embeds artificial intelligence (AI) across all network functions, enabling automation, efficiency, and advanced services. The AI-native approach is more than just a technical enhancement; it is a fundamental shift that drives the evolution toward autonomous networks, unlocking new advancements in network capabilities and user experience. A key enabler is AI as a Service (AIaaS), which allows Communication Service Providers (CSPs) to expose AI capabilities via standardized APIs. Built on Machine Learning Operations (MLOps), AIaaS streamlines AI model lifecycle management, reducing complexity for developers and optimizing AI-driven workflows.This paper investigates the integration of AIaaS into network exposure frameworks such as the 3rd Generation Partnership Project (3GPP) Network Exposure Function (NEF), Service Enabler Architecture Layer (SEAL), and the open-source CAMARA interface emphasizing the interacting and collaborating robots (cobots) use case in the context of industrial automation. We discuss how AIaaS enables CSPs to offer AI-powered services with guaranteed Quality of Service (QoS), driving innovation across industries and shaping the future of AI-native 6G networks.

Internet of Things (IoT) applications consist of diverse resource-constrained/rich devices with a considerable portion being mobile. Such devices demand lightweight, loosely coupled interactions in terms of time, space, and synchronization. IoT protocols at the middleware layer support several interaction types (e.g., asynchronous messaging, streaming, etc.) ensuring successful interactions between devices that use the same protocol. Additionally, they introduce different Quality of Service (QoS) delivery modes for data exchange with respect to available device and network resources. On the other hand, interconnecting heterogeneous IoT devices requires mapping both their functional and QoS properties. This calls for advanced interoperability solutions integrated with QoS modeling and analysis techniques. This paper introduces an automated synthesis of QoS-aware mediating artifacts. Such mediators enable the interconnection between IoT devices employing heterogeneous middleware protocols. Additionally, representative QoS models are synthesized. Leveraging these models, system designers can evaluate the effectiveness of the interconnection in terms of end-to-end QoS. We evaluate the usefulness of our approach through experimentation with a case study employing heterogeneous middleware protocols. In particular, we statistically analyze through simulations the effect of varying system parameters on the end-to-end QoS.

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.

With the emergence of Industry 4.0, comes an increasing need for multi-robot coordination and communication to efficiently complete joint tasks. A critical technology is the fifth generation (5G) mobile network, which enables cloud-controlled robots to execute tasks with differentiated quality-of-service (QoS) features. While there has been significant research on multi-robot planning, the integration with the capabilities of realistic network systems has been limited. In this paper, we introduce RoboPlan5G, a framework for 5G-aware robot planning. We propose a joint state-search model that includes task planning coordination in conjunction with 5G physical resource block (PRB) allocation. This process ensures efficient usage of the limited indoor 5G spectrum and effective service performance, while still aiming for the completion of tasks in the shortest possible time frame. Scenarios are generated in an Industry 4.0 environment, where the planner is shown to decrease the required 5G spectrum allocation by 50%, and on average improve the plan quality by 45% while maintaining a small computation time.

This paper presents PlanIoT, a middleware approach for enabling adaptive data flow management in IoT-enhanced spaces (e.g., buildings) using automated planning methodologies. Today’s sensorized spaces deploy applications falling to diverse categories such as analytics, real-time, transactional, video streaming and emergency response. Depending on the category, applications have different QoS requirements related to timely delivery, networking resources, accuracy, etc. Typically, state-of-the-art data exchange systems introduce policies for bandwidth allocation or prioritization for specific data types and applications (e.g., camera data). PlanIoT introduces a generic QoS model to evaluate the performance of data flowing in Edge infrastructures and generates their performance metrics dataset. Such a dataset is used as input to automated planning representations to intelligently satisfy QoS requirements of deployed applications. The experimental results show that PlanIoT improves the end-to-end response time of time-sensitive flows by more than 50%, especially with an overloaded Edge infrastructure. We also show the adaptivity of our approach by considering emergency cases that require Edge infrastructure reconfiguration.

This paper presents the implementation and guideline of PlanIoT, an adaptive flow management framework for IoT-enhanced spaces. Such spaces are composed of applications deployed at the Edge with varying QoS requirements in terms of response time, timely delivery, throughput, etc. Configuring the Edge infrastructure requires tuning multiple parameters for optimal QoS satisfaction of applications. This is a complex task especially when the system has to be re-adapted (e.g., emergency situations). The PlanIoT framework manages application data flows in an adaptive manner. This is achieved via the following core software components: (i) a queueing network composer; (ii) an automated planning modeler; and (iii) an AI planner. This artifact presents implementation details of these components as well as guidelines for using the PlanIoT framework.

Data-center network configurations are crucial in ensuring end-to-end differentiated service performance within 5G. Data-center networks encom-pass two domains: (i) the fat-tree networking fabric with leaf, spine and super-spine layers (ii) data-center server nodes with container and workload placement policies. These have traditionally been managed within silos with context and configurations driven within each domain. In this work, we examine the effect of configuration changes in one domain and its effect on the other. We develop Madelyn, a multi-domain multi-agent rein-forcement learning framework for data-center networks that can propose network-aware, virtual network function placement. This framework takes into account the data-center fabric wights, drop rates, capacities, load balancing and traffic shaping. It also considers the network function pod placements based on affinity / anti-affinity rules, node capacities and taints/tolerations. Using this multi-agent framework, we provide network aware scheduling policies for differentiated network function virtualization services running on Kubernetes pods within data-center networks. The results are demonstrated over a real traffic dataset collected over Ericsson's testbed networks.

With the emergence of Industry 4.0, comes an increasing need for multi-robot coordination and communication to efficiently complete joint tasks. A critical technology is the fifth generation (5G) mobile network, which enables multiple robots to execute control tasks with differentiated quality-of-service (QoS) features. However, there has been limited analysis of the impact of real 5G capabilities on multi-agent robot planning problems. In this paper, we provide a review of robot planning algorithms suitable for industrial use-cases, which consider communication aspects in the planning formulation. The paper is further positioned to identify gaps in existing state of the art within communication-aware planning. This is followed by an analysis of key challenges to be targeted at the intersection of 5G, Industry 4.0 and multi-agent robot planning. This analysis is strategically important and would prove useful to academic researchers and industry experts focusing on deployment of robots in industrial settings.

Industry 4.0 applications require robust and reliable connectivity to ensure optimal performance. The emergence of 5G private networks attempts to provide differentiated services for various industrial traffic requirements. To guarantee service quality, it is crucial to correctly map metrics of the Industry 4.0 domain to the 5G Network domain. Mapping the metrics will ensure optimal system performance without the need for network over-provisioning or requirement violations. In this work, we make use of a Partially Observable Markov Decision Process (POMDP) formulation to map Industry 4.0 metrics to 5G requirements. The optimal mapping is shown to generate policies to reconfigure the 5G network and satisfy Industry 4.0 process requirements. The system is demonstrated over use cases from the industrial project 5G SMART.

5G Radio Access Network (RAN) slicing concerns strategies to share radio resources while guaranteeing differentiated service requirements. Current state of the art approaches make use of strict isolation or dedicated RAN physical resource block (PRB) partitioning among slices to ensure differentiated services. However, spectrum multiplexing may be rendered suboptimal due to isolation of resources; it further cannot handle variations in traffic patterns or intents in a dynamic way. In this paper, we propose a flexible multi-service partitioning strategy that can balance functional isolation and optimal sharing of resources. This system, called Muesli: Multi-objective Radio Resource Slice Management, makes use of model-based reinforcement learning techniques to dynamically modify PRB partitions. The reinforcement learning reward structure ensures that the system is trained to meet multiple objectives such as network slice Service Level Agreement (SLA) compliance, spectrum usage efficiency and fairness among customer classes. On a real use case from Ericsson, the throughput levels for individual services are shown to be optimized with accurate PRB partitioning.

With the proliferation of Reinforcement Learning (RL) algorithms across multiple applications, the design of appropriate reward mechanisms that elicit desired behaviours becomes crucial. Reward setting is made more difficult in the multi-agent case where cooperative, competitive or mixed interactions between agents may lead to differing outcomes. In this paper, we formulate the reward engineering of multi-agent reinforcement learning approaches via game theoretic models. This approach is used to analyze the overall team reward when choosing one game theoretic structure over another. An empirical analysis is provided over game theoretic simulators that demonstrate co-operative game rewards improve the rewards by upto 30%. This formulation may be applied to a variety of use cases within logistics, transport and telecommunications domains that employ multi-agent techniques.

A robot as a coworker or a cohabitant is becoming mainstream day-by-day with the development of low-cost sophisticated hardware. However, an accompanying software stack that can aid the usability of the robotic hardware remains the bottleneck of the process, especially if the robot is not dedicated to a single job. Programming a multi-purpose robot requires an on the fly mission scheduling capability that involves task identification and plan generation. The problem dimension increases if the robot accepts tasks from a human in natural language. Though recent advances in NLP and planner development can solve a variety of complex problems, their amalgamation for a dynamic robotic task handler is used in a limited scope. Specifically, the problem of formulating a planning problem from natural language instructions is not studied in details. In this work, we provide a non-trivial method to combine an NLP engine and a planner such that a robot can successfully identify tasks and all the relevant parameters and generate an accurate plan for the task. Additionally, some mechanism is required to resolve the ambiguity or missing pieces of information in natural language instruction. Thus, we also develop a dialogue strategy that aims to gather additional information with minimal question-answer iterations and only when it is necessary. This work makes a significant stride towards enabling a human-like task understanding capability in a robot.

Automation and the use of robotic components within business processes is in vogue across retail and manufacturing industries. However, a structured way of analyzing performance improvements provided by automation in complex workflows is still at a nascent stage. In this paper, we consider the common Industry 4.0 automation workflow resource patterns and model them within a hybrid queuing network. The queuing stations are replaced by scale up, scale out and hybrid scale automation patterns, to examine improvements in end-to-end process performance. We exhaustively simulate the throughput, response time, utilization and operating costs at higher concurrencies using Mean Value Analysis (MVA) algorithms. The queues are analyzed for cases with multiple classes, batch/transactional processing and load dependent service demands. These solutions are demonstrated over an exemplar use case of automation in Industry 4.0 warehouse automation workflows. A structured process of automation workflow performance analysis will prove valuable across industrial deployments.

Industry 4.0 deployments involve machines, robots, Internet of Things, business processes and human participants coordinating in time constrained and safety critical environments. As these deployments make use of complex workflow patterns, accurate formal modeling, verification and timing analysis of such industrial systems are needed. In this paper, we model the workflow interactions of Industry 4.0 warehouse operations using the concurrent programming language Orc. Complex deployments involving multiple robotic agents and business processes further require analysis of correctness, liveness and safety properties. In order to verify the workflows, the Orc specifications are translated into Workflow net representations, with verification done using the TAPAAL model checker. Additional composition of timing constraints are analyzed to enable hard robotic deadlines to interact with best effort Service Level Agreement (SLA) requirements. We demonstrate these aspects over a realistic use case in automated warehouse management with pick and delivery robots.

Robotic automation has made significant inroads into industrial manufacturing and supply chains. With Industry 4.0 requirements proposing further autonomy to robotic participants, it is necessary to reason about robotic tasks within a knowledge dependent software framework. In this work, we model robotic automation tasks using hierarchical decomposition models, that are used to extract action plans to satisfy end goals. By abstracting components as intelligent agents that have perception, action, goal and knowledge base elements, we provide a reusable model to abstract robotic automation behavior. Through the use of the formal typed specification language Orc, implementations that confirm to the goal decomposition process are formulated. We demonstrate our techniques over Smart Warehouse deployments, with domain specific ontologies to ensure accurate type based descriptions of knowledge elements. This provides a generic framework for deploying intelligent automation systems across a host of industrial settings.

Mobile IoT applications are typically deployed on resource-constrained devices with intermittent network connectivity. To support the deployment of such applications, the Publish/Subscribe (pub/sub) interaction paradigm is often employed, as it decouples mobile peers in time and space. Pub/sub middleware protocols and APIs consider the Things' hardware limitations and support the development of effective applications by providing QoS features. These features aim to enable developers to tune an application by switching different levels of response times and success rates. However, the profusion of pub/sub protocols coupled with intermittent connectivity result in non-trivial application tuning. In this paper, we model the performance of middleware protocols found in IoT, which are classified within the pub/sub interaction paradigm. We model reliable and unreliable protocols, by considering QoS semantics for data validity, buffer capacities as well as the intermittent availability of peers. Finally, we perform statistical analysis by varying these QoS semantics, demonstrating their significant effect on the rate of successful interactions. We showcase the application of our analysis in concrete scenarios relating to Traffic Information Management systems, that integrate both reliable and unreliable participants. The consequent PerfMP performance modeling pattern may be tailored for a variety of deployments, in order to control fine-grained QoS policies.

Systems deployed in mobile environments are typically characterized by intermittent connectivity and asynchronous sending/reception of data. To create effective mobile systems for such environments, it is essential to guarantee acceptable levels of timeliness between sending and receiving mobile users. In order to provide QoS guarantees in different application scenarios and contexts, it is necessary to model the system performance by incorporating the intermittent connectivity. Queueing Network Models (QNMs) offer a simple modeling environment, which can be used to represent various application scenarios, and provide accurate analytical solutions for performance metrics, such as system response time. In this paper, we provide an analytical solution regarding the end-to-end response time between users sending and receiving data by modeling the intermittent connectivity of mobile users with QNMs. We utilize the publish/subscribe (pub/sub) middleware as the underlying communication infrastructure for mobile users. To represent the user's connections/disconnections, we model and solve analytically an ON/OFF queueing system by applying a mean value approach. Finally, we validate our model using simulations with real-world workload traces. The deviations between the performance results foreseen by the analytical model and the ones provided by the simulator are shown to be less than 5% for a variety of scenarios.

Resource demand estimation is essential for the application of analyical models, such as queueing networks, to real-world systems. In this paper, we investigate maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent and load-dependent service times. Stemming from a characterization of necessary conditions for ML estimation, we propose new estimators that infer demands from queue-length measurements, which are inexpensive metrics to collect in real systems. One advantage of focusing on queue-length data compared to response times or utilizations is that confidence intervals can be rigorously derived from the equilibrium distribution of the queueing network model. Our estimators and their confidence intervals are validated against simulation and real system measurements for a multi-tier application.

We identify that to improve the reusability of implementation in service engineering, we need to collect and modulate reusable strategy, knowledge and experience crossing business modeling, knowledge management and economic analysis simultaneously from an interdisciplinary perspective. Strategically to ease the complexity, we adopt the existing experience from design patterns by forming a service design pattern called service value broker(SVB). SVB can efficiently integrate the three domains with relieved complexity, enhanced reusability and efficiency, etc, catering different abstraction levels. In this paper, we focus on modeling the foundational aspects of SVB presentation and value transaction, etc.

In our previous work, we have introduced various service value broker (SVB) patterns which integrate business modeling, knowledge management and economic analysis. In this paper, working towards the target of maximizing the potential usage of available resource to achieve the optimization of the satisfaction on both the service provider side and the service consumer side under the guidance of the public administrative, we propose to build the E-Tourism platform based on SVB. This paper demonstrates the mechanism for SVB based E-Tourism framework. The advantages of employing SVB include that the SVB can help to increase the value added in a realtime and balanced manner which conforms to the economical goal of both long run and short run. An experiment is shown using a personnel recommendation system.

As an emerging interdisciplinary subject which crosscuts business modeling, knowledge management and economic analysis, service engineering is increasingly demanded to take care of various stakeholders' proting goals of the short run vs. long run. We propose to work towards a value driven design based solution through introducing a form of service design patterns: the service value broker(SVB) patterns to shorten the distance from economical analysis to IT implementation. SVB patterns allow us to not only study the value added in terms of functional and business aspects, but also reason about the need for brokerage across various domains.

Quality of Service (QoS) in orchestrated web services compositions have been well studied with probabilistic and multi-dimensional models. Choreographies that involve message passing among services, on the other hand, require further analysis. In this paper, we begin with the set of QoS domains that may be studied in case of choreographies and the algebraic rules for their composition. As choreographies manage QoS composition in a distributed fashion, techniques to enrich functional specifications with QoS are examined using the model proposed in the CHOReOS project. These are further analyzed with choreographies that may reconfigure due to functional or QoS requirements. Studies on the effects of such reconfiguration on multiple QoS domains can lead to better understanding of optimal runtime configurations along with associated tradeoffs. A goal programming approach is also proposed to choose Pareto optimal solutions with respect to diverse QoS domains.

The service value broker(SVB) pattern integrates business modeling, knowledge management and economic analysis with relieved complexity, enhanced reusability and efficiency, etc. The study of SVB is an emerging interdisciplinary subject which will help to promote the reuse of knowledge, strategy and experience in service based designs and solutions. In this paper, we focus on enumerating collected SVBs empirically with initial analysis on their composition manners. The results from this paper will play a dominating role in fueling a coming E-service Economics era.

Service Level Agreements (SLAs) have been proposed in the context of web services to maintain acceptable quality of service (QoS) performance. This is specially crucial for composite service orchestrations that invoke many atomic services to render functionality. A consequence of SLA management entails efficient negotiation protocols among orchestrations and invoked services. In composite services where data and QoS (modeled in a probabilistic setting) interact, it is difficult to select an atomic service for negotiation, in order to improve end-to-end QoS performance. A superior improvement in one negotiated domain (eg. latency) might mean deterioration in another domain (eg. cost); improvement in one of the invoked services may be annulled by another due to the control flow specified in the orchestration. In this paper, we propose a integer programming formulation based on first order stochastic dominance as a strategy for re-negotiation over multiple services. A consequence of this is better end-to-end performance of the orchestration compared to random selection of services for re-negotiation. We also demonstrate this optimal strategy can be applied to negotiation protocols specified in languages such as Orc. Such strategies are necessary for composite services where QoS contributions from individual atomic services vary significantly.

The ever-growing choice in diverse services is making service orchestration variability an essential aspect of a composite web service. Influence of this variation on the Quality of Service (QoS) of a composite service is critical and the focus of our work. In this paper, we present a methodology to first model orchestration variability using a feature diagram (FD). The FD specifies a product line of orchestrations represented as configurations of invoked/rejected atomic services. Second, due to the potentially large set of configurations we employ combinatorial testing techniques to automatically generate configurations covering all valid pair wise interactions between services. Third, we analyze QoS variation for each configuration using probabilistic models of QoS. Using a crisis management system case study we experimentally show that pair wise generation covers all QoS outliers and eliminates analysis of > 75% of all possible configurations. The QoS analysis of the pair wise configurations reveals unsafe/ineffective configurations, helps determine realistic Service Level Agreements (SLAs), and provides valuable feedback to help remodel an orchestration.