We present FastFlow-Python, a framework that brings parallelism to Python for stream-processing applications. FastFlow-Python enables developers to build high-throughput, low-latency data-flow networks by instantiating high-level, ready-to-use parallel building blocks. Built on the C++ FastFlow library, it leverages Python bindings via the C/Python API to efficiently manage parallel execution using both subinterpreters and multiprocessing, all abstracted by the framework. We demonstrate the performance benefits of FastFlow-Python through a comparative analysis with a pure Python stream-processing implementation, highlighting its effectiveness in overcoming the limitations imposed by the Global Interpreter Lock (GIL). Experimental results show almost linear scalability when increasing the number of workers.

As the Internet of Things (IoT) gains popularity, the multitude of connected devices accessing computing facilities along the Cloud-IoT continuum has risen rapidly. This growth has prompted an increase in IoT-based applications and the data they process. These applications often carry specific Quality of Service (QoS) requirements, necessitating the research of strategic management for services and data, coupled with routes across the IoT and application components. Concurrently, our research explores the integration of Intent-Based Networking (IBN) principles to manage data flows and resolve conflicts among stakeholders sharing the same infrastructure. This dual-focus approach aims to support the effective placement and management of multi-service applications on Cloud-IoT resources, considering QoS, data characteristics, and resource conflict detection and resolution. This work outlines our primary objectives and shares findings from our ongoing research.

This article proposes an approach to the data-aware multi-service application placement problem in Cloud-Edge settings. We propose both declarative programming and a Mixed-Integer Linear Programming (MILP) approach to determine eligible placements that minimise operational costs and reduce the number of used nodes to contain the amount of data transfers. After assessing the performance of both approaches, we reconcile them into a methodology that combines the best of the two worlds by exploiting a declarative pre-processing step to boost the MILP solver while determining optimal solutions. We open-sourced the methodology into a prototype that is l0x faster than pure MILP, determines optimal results, and easily accommodates non-numerical constraints on application placements.

Intent-based Networking (IBN) aims at simplifying network configuration and management by using high-level objectives that express the desired state of the network rather than the details of how to implement it. In this article, we propose a declarative methodology and an associated open-source Prolog prototype (i) to model IBN intents related to the provisioning of Virtual Network Function (VNF) chains, and (ii) to process those intents to assemble and place a VNF chain that fulfils them. Our prototype is assessed over a lifelike motivating scenario.