Traditional Cloud-based multimedia transcoding approaches struggle with issues like latency and bandwidth limitations. In this work, we experiment with strategies that exploit Edge computing capabilities to get around such constraints. We focus on an on-demand multimedia transcoding service for real-time video streaming on the uplink, leveraging a state-of-the-art Edge Computing service orchestrator to handle its provisioning. We regard this as a use case for the orchestration platform, with the objective of enhancing the experience of the user as well as keeping important metrics in check, including resource utilization and load distribution over the Edge compute nodes. We perform evaluations on a physical testbed to assess the performance of the Edge-based system using real-world video transcoding workloads.

Goal-Oriented (GO) communication is an emerging paradigm that aims at enhancing the efficiency of Internet of Things (IoT) systems by sending only the minimum data needed to achieve the application goal. In GO communications, the network can apply techniques to reduce the amount of data sent through the network to a cloud node, such as analyzing and selecting data at the source with intelligent IoT devices. GO communication and the related networking techniques have primarily been studied from a device-centric perspective, often overlooking their impact on the network. However, the network consumes significant amounts of energy, with the Radio Access Network (RAN) alone accounting for approximately 70 % of the total energy consumption in mobile communication systems. Therefore, this paper introduces an end-to-end model for energy consumption, latency, and accuracy to assess GO networking strategies. We then use the model to evaluate the performance of GO strategies in edge cloud scenarios with different hardware platforms.

Kubernetes (K8S) is a widely used orchestration solution that helps manage complex IT applications by providing mechanisms for autoscaling, health checking, cluster formation, and replication, which are essential to deploy and manage the multitude of connected microservices. However, they may suffer in case of unexpected faults which can severely change the underlying computing infrastructure and lead to service outages, highlighting the need for resilient solutions capable of mitigating the adverse effects of faults. To address this, the TELKA sched-uler integrates Chaos Engineering (CE), Reinforcement Learning (RL), and Digital Twin (DT) to reallocate K8S pods evicted due to unexpected faults. While TELKA showed promising results in reallocating evicted pods, its preliminary implementations suffered from scalability issues, as the RL agent could only effectively operate on scenarios with the same number of nodes seen during training. To overcome this limitation, this paper improves TELKA by incorporating a neural network architecture called Deep Sets (DS), which can generalize the operation of TELKA on different numbers of nodes. Experimental results not only demonstrate the validity of the improved TELKA but also show how it can be used to identify good operating conditions.

The Edge-Cloud Continuum represents a paradigm shift in distributed computing, seamlessly integrating resources from cloud data centers to edge devices. However, orchestrating services across this heterogeneous landscape poses significant challenges, as it requires finding a delicate balance between different (and competing) objectives, including service acceptance probability, offered Quality-of-Service, and network energy consumption. To address this challenge, we propose leveraging Multi-Objective Reinforcement Learning (MORL) to approximate the full Pareto Front of service orchestration policies. In contrast to conventional solutions based on single-objective RL, a MORL approach allows a network operator to inspect all possible “optimal” trade-offs, and then decide a posteriori on the orchestration policy that best satisfies the system’s operational requirements. Specifically, we first conduct an extensive measurement study to accurately model the energy consumption of heterogeneous edge devices and servers under various workloads, alongside the resource consumption of popular cloud services. Then, we develop a set-based MORL policy for service orchestration that can adapt to arbitrary network topologies without the need for retraining. Illustrative numerical results against selected heuristics show that our MORL policy outperforms baselines by 30% on average over a broad set of objective preferences, and generalizes to network topologies up to 5x larger than training.

Chaos engineering is the discipline of injecting computing and network faults, such as increased network latency and unavailability of computing nodes, into an IT system to help developers in identifying problems that could arise in a production environment and tackle them. Several tools have emerged to ease the application of chaos engineering to complex IT systems, leveraging microservice and container-based applications deployed on Kubernetes. However, applying of such tools requires several phases to be put into practice, from defining a steady state to establishing an effective response plan if something goes wrong. To ease the application of chaos engineering in improving the resilience of Kubernetes applications, this work presents a smart scheduler for Kubernetes called TELKA: a Twin-Enhanced Learning for Kubernetes Applications, which combines chaos engineering, Digital Twin (DT), and Reinforcement Learning (RL) methodologies to mitigate the effects of computing and network faults. Instead of interacting directly with the physical Kubernetes application, TELKA learns by interacting with a digital twin, thus reducing the learning time and the operation costs related to the application of chaos engineering. Experiment results compare TELKA with other approaches to show its effectiveness in mitigating the adverse effects of injected faults.

Kubernetes is a well-known orchestration and management solution for complex and large-scale service architectures in the Cloud Continuum. While it provides very valuable functions from the operation perspective, the high number of control loops it implements significantly enlarges the already wide space of configuration parameters and policies to consider for management purposes. We argue that optimizing complex Kubernetes deployments considering a multi-cloud and edge computing environment would significantly benefit from a Digital Twin approach, enabling an accurate virtual representation of a Kubernetes application to optimize its deployment and management policies. Towards that goal, this work illustrates the design of KubeTwin, a framework to implement Digital Twins of Kubernetes deployments. Furthermore, we present a validation of KubeTwin in a Multi-access Edge Computing (MEC) scenario, which shows its soundness in reenacting realistic Digital Twins of complex and highly distributed Kubernetes deployments. We believe that KubeTwin can provide useful guidance to the research community working in this field.

The use of microservice-based applications is becoming more prominent also in the telecommunication field. The current 5G core network, for instance, is already built around the concept of a “Service Based Architecture”, and it is foreseeable that 6G will push even further this concept to enable more flexible and pervasive deployments. However, the increasing complexity of future networks calls for sophisticated platforms that could help network providers with their deployments design. In this framework, a central research trend is the development of digital twins of the physical infrastructures. These digital representations should closely mimic the behavior of the managed system, allowing the operators to test new configurations, analyze what-if scenarios, or train their reinforcement learning algorithms in safe environments. Considering that Kubernetes is becoming the de-facto standard platform for container orchestration and microservice-based application lifecycle management, the implementation of a Kubernetes digital twin requires an accurate characterization of the microservice response time, possibly leveraging suitable Machine Learning techniques trained with measurement data collected in the field. In this paper we introduce a new methodology, based on Mixture Density Networks, to accurately estimate the statistical distribution of the response time of microservice-based applications. We show the improvement in performance with respect to simulation-based inference procedures proposed in literature.

Kubernetes provides several functions that can help service providers to deal with the management of complex container-based applications. However, most of these functions need a time-consuming and costly customization process to address service-specific requirements. The adoption of Digital Twin (DT) solutions can ease the configuration process by enabling the evaluation of multiple configurations and custom policies by means of simulation-based what-if scenario analysis. To facilitate this process, this paper proposes KubeTwin, a framework to enable the definition and evaluation of DTs of Kubernetes applications. Specifically, this work presents an innovative simulation-based inference approach to define accurate DT models for a Kubernetes environment. We experimentally validate the proposed solution by implementing a DT model of an image recognition application that we tested under different conditions to verify the accuracy of the DT model. The soundness of these results demonstrates the validity of the KubeTwin approach and calls for further investigation.

We consider the problem of designing and training a neural network-based orchestrator for fog computing service deployment. Our goal is to train an orchestrator able to optimize diversified and competing QoS requirements, such as blocking probability and service delay, while potentially supporting thousands of fog nodes. To cope with said challenges, we implement our neural orchestrator as a Deep Set (DS) network operating on sets of fog nodes, and we leverage Deep Reinforcement Learning (DRL) with invalid action masking to find an optimal trade-off between competing objectives. Illustrative numerical results show that our Deep Set-based policy generalizes well to problem sizes (i.e., in terms of numbers of fog nodes) up to two orders of magnitude larger than the ones seen during the training phase, outperforming both greedy heuristics and traditional Multi-Layer Perceptron (MLP)-based DRL. In addition, inference times of our DS-based policy are up to an order of magnitude faster than an MLP, allowing for excellent scalability and near real-time online decision-making.

Mission Critical (MC) communications take a pivotal role to achieve effective Public Protection and Disaster Relief (PPDR) actions. Even though 3GPP standards define MC applications and services in an architectural framework compatible with current 5G mobile networks, real-life experiments and applications of these concepts are still at the very beginning. In this paper, we present an architectural study and related experimental activity on network slicing for MC communications. We implemented these services in a fully virtualized environment, and deployed and tested them in a multi-domain network slicing scenario compliant with the ETSI NFV-MANO specifications. Our work aligns with the 5G approach separating control and data planes. The level of automation in service deployment and the slice isolation features are demonstrated, showing the benefits in terms of application performance, management flexibility, scalability, and quality of service differentiation capabilities.

With the establishment of the Everything-as-a-Service (XaaS) paradigm for service provisioning, coupled with the increasingly-demanding requirements imposed by modern network services, the need for a XaaS-aware orchestration system able to cope with a heterogeneous infrastructure, such as the one of Fog Computing environments, is evident. In this work, we describe the working principles and implementation aspects that allow the orchestration of services offered according to the Function-as-a-Service (FaaS) model. The live demonstration will showcase the ability of the system to deploy this kind of services on a suitable test bed, with comments on the procedure and the performance.

The possibility of managing network infrastructures through software-based programmable interfaces is becoming a cornerstone in the evolution of communication networks. The Intent-Based Networking (IBN) paradigm is a novel declarative approach towards network management proposed by a few Standards Developing Organizations. This paradigm offers a high-level interface for network management that abstracts the underlying network infrastructure and allows the specification of network directives using natural language. Since the IBN concept is based on a declarative approach to network management and programmability, we argue that the use of declarative programming to achieve IBN could uncover valuable insights for this new network paradigm. This paper proposes a formalization of this declarative paradigm obtained with concepts from category theory. Taking this approach to Intent, an initial implementation of this formalization is presented using Haskell, a well-known functional programming language.

Fog Computing can facilitate the adoption of the Everything-as-a-Service paradigm in infrastructure segments that are located closer to the end user, or to the data source, compared to typical Cloud solutions. This enables combining the advantages of flexible service deployment models with the need to cope with the strict requirements–especially in terms of latency–of emerging applications in softwarized networks. Along comes the need to consider aspects of service orchestration specific to the Fog environment and its intrinsically dynamic nature. In this paper we propose an architecture for flexible Fog Computing service orchestration, with a particular focus on the awareness of service deployment models. We discuss the design choices and describe the components and operations of the proposed orchestration system. We then present a complete working implementation of such architecture, including insights on its ability to handle critical orchestration functions such as service discovery and resource monitoring. We also report on the experimental validation of the system and the performance evaluation on real-world equipment, proving the feasibility and the effectiveness of the approach on a dynamic Fog infrastructure. We complement the work by presenting the results of a combinatorial analysis, validated by simulation, of the service model-aware resource selection process. As a result of our investigation, we show that Fog services can be effectively deployed in a matter of a few seconds, or even in less than one second when suitable Fog nodes are available, taking advantage of the awareness of the available service models.

The service function chaining paradigm links ordered service functions via network virtualization, in support of applications with severe network constraints. To provide wide-area (federated) virtual network services, a distributed architecture should orchestrate cooperating or competing processes to generate and maintain virtual paths hosting service function chains while, guaranteeing performance and fast asynchronous consensus even in the presence of failures. To this end, we propose a prototype of an architecture for robust service function chain instantiation with convergence and performance guarantees. To instantiate a service chain, our system uses a fully distributed asynchronous consensus mechanism that has bounds on convergence time and leads to a (1 - 1/e)-approximation ratio with respect to the Pareto optimal chain instantiation, even in the presence of (non-byzantine) failures. Moreover, we show that a better optimal chain approximation cannot exist. To establish the practicality of our approach, we evaluate the system performance, policy tradeoffs, and overhead via simulations and through a prototype implementation. We then describe our extensible management object model and compare our asynchronous consensus's overhead against Raft, a recent decentralized consensus protocol, showing superior performance. We furthermore discuss a new management object model for distributed service function chain instantiation.

Mission Critical (MC) communications are key to effective Public Protection and Risk Reduction (PPRR) actions. The 3GPP standards include the definition of MC applications and services in an architectural framework compatible with current (LTE) and future (5G) mobile networks. In this paper we report an experimental activity where MC communication services are implemented in a fully virtualized environment, being deployed and tested in a multi-domain network slicing architecture compliant with the ETSI NFV MANO specifications. The level of automation in service deployment and the slice isolation features are demonstrated, in line with the 5G approach of separation between control and data plane, showing the benefits in terms of application performance and management flexibility.

In this manuscript we describe an experimental work that integrates the NFV-MANO framework with segment routing to support 5G network slicing. The aim is to implement Service Function Chains spanning several cloud domains and the related interconnection transport network in a coordinated way. The manuscript shows the feasibility and the performance effectiveness of this approach, reporting numerical results from practical experiments.