arXiv subjects

Andrea Sabbioni

Publications and source records attributed to Andrea Sabbioni.

3 recordsLinked to original sources

arXiv2026

A Cloud Continuum Research Infrastructure for Distributed CPS Experimentation

Cloud Continuum applications require experimental environments capable of combining heterogeneous Edge, Fog, Cloud, and high-performance computing resources while preserving reproducibility, observability, and control over distributed deployments. This paper presents a two-level reference architecture for Cloud Continuum experimentation built on top of the SLICES Cloud Continuum Blueprint. The proposed approach separates the research-infrastructure layer, which exposes and manages distributed resources, from the application layer, where Cyber-Physical workflows are organized according to an Edge-Fog-Cloud pattern in which placement, timing, and data provenance are treated as first-class experimental concerns. The architecture is designed to support multiple continuum applications rather than a single domain-specific prototype. At the Edge, applications interact with physical devices and perform low-latency sensing or safety actions; at the Fog, they execute near-source coordination, mediation, and stream-processing logic; at the Cloud, they consolidate global knowledge through analytics, optimization, and visualization. This partitioning enables researchers to deploy, customize, and compare alternative control and monitoring strategies over the same programmable infrastructure substrate. The approach is validated through two representative use cases: Renewable Energy Community management, where distributed Digital Twin coordination and time-window-based energy control are requested, and AirWatch, a monitoring pipeline focused on anomaly detection, low-latency alerting, and cloud-side aggregation. Both workloads are evaluated through a systematic campaign of 40 runs comparing virtualized and physical edge deployments over a geographically distributed infrastructure.

cs.DC↗

arXiv2026

Scale: Deep Reinforcement Learning for Container Scheduling in Serverless Edge Computing

Serverless computing has emerged as a promising computing paradigm for edge computing. However, adopting the event driven model in highly dynamic, heterogeneous, and distributed edge systems poses significant challenges in request placement and resource management. Efficiently allocating requests to containers is therefore critical to reduce resource over provisioning and unnecessary data movement. This paper proposes Scale, a Service Level Objective aware container scheduling and resource allocation framework designed for serverless edge computing. Scale employs a policy based deep reinforcement learning algorithm to balance system stability and performance under dynamic workloads. The design jointly incorporates SLO constraints, end to end latency, and data locality into the scheduling decision process. Extensive simulations using large scale real world datasets from Huawei Cloud demonstrate that Scale achieves solutions within a factor of 1.11 to 1.15 of a state of the art Integer Linear Programming solver, while reducing decision making time by up to 99%.

cs.DC↗

arXiv2026

MANATEE: A DevOps Platform for xApp Lifecycle Management and Testing in Open RAN

The shift to disaggregated 5G architectures introduces unprecedented flexibility but also significant complexity in Beyond 5G Radio Access Networks (RANs). Open RAN enables programmability through xApps, yet deploying and validating these applications is critical given the nature of the systems they aim to control. Current Open RAN ecosystems lack robust lifecycle management of xApps that enable automated testing, seamless migration, and production-grade observability, resulting in slow, error-prone xApp delivery. To address these issues, DevOps practices can streamline the xApp lifecycle by integrating Continuous Integration/Continuous Deployment (CI/CD) pipelines with advanced traffic management and monitoring, such as leveraging service mesh technologies to enable progressive deployment strategies (e.g., canary releases and A/B testing) to ensure fine-grained observability and resilience. The solution presented in this article, MANATEE (Mesh Architecture for Radio Access Network Automation and TEsting Ecosystems), is the first platform that combines these principles to simplify xApp delivery into production, accelerate innovation, and guarantee performance across heterogeneous O-RAN environments. We prototyped MANATEE on a Kubernetes cluster integrated with the O-RAN Software Community Near-Real Time RAN Intelligent Controller (RIC), as well as with service mesh technologies, to facilitate testing of xApps across simulated, emulated, and real testbed environments. Our experimental results demonstrate that service mesh integration introduces minimal overhead (below 1 ms latency), while enabling reliable canary deployments with fine-grained traffic control and conflict-free A/B testing through circuit-breaking mechanisms.

cs.NI↗