Andrea Morichetta

Abhishek Hazra, Andrea Morichetta, Ilir Murturi et al.

Zero-touch network is anticipated to inaugurate the generation of intelligent and highly flexible resource provisioning strategies where multiple service providers collaboratively offer computation and storage resources. This transformation presents substantial challenges to network administration and service providers regarding sustainability and scalability. This article combines Distributed Artificial Intelligence (DAI) with Zero-touch Provisioning (ZTP) for edge networks. This combination helps to manage network devices seamlessly and intelligently by minimizing human intervention. In addition, several advantages are also highlighted that come with incorporating Distributed AI into ZTP in the context of edge networks. Further, we draw potential research directions to foster novel studies in this field and overcome the current limitations.

Boris Sedlak, Victor Casamayor Pujol, Andrea Morichetta et al.

The current scenario of IoT is witnessing a constant increase on the volume of data, which is generated in constant stream, calling for novel architectural and logical solutions for processing it. Moving the data handling towards the edge of the computing spectrum guarantees better distribution of load and, in principle, lower latency and better privacy. However, managing such a structure is complex, especially when requirements, also referred to Service Level Objectives (SLOs), specified by applications' owners and infrastructure managers need to be ensured. Despite the rich number of proposals of Machine Learning (ML) based management solutions, researchers and practitioners yet struggle to guarantee long-term prediction and control, and accurate troubleshooting. Therefore, we present a novel ML paradigm based on Active Inference (AIF) -- a concept from neuroscience that describes how the brain constantly predicts and evaluates sensory information to decrease long-term surprise. We implement it and evaluate it in a heterogeneous real stream processing use case, where an AIF-based agent continuously optimizes the fulfillment of three SLOs for three autonomous driving services running on multiple devices. The agent used causal knowledge to gradually develop an understanding of how its actions are related to requirements fulfillment, and which configurations to favor. Through this approach, our agent requires up to thirty iterations to converge to the optimal solution, showing the capability of offering accurate results in a short amount of time. Furthermore, thanks to AIF and its causal structures, our method guarantees full transparency on the decision making, making the interpretation of the results and the troubleshooting effortless.

Ilir Murturi, Praveen Kumar Donta, Victor Casamayor Pujol et al.

Converging Zero Trust (ZT) with learning techniques can solve various operational and security challenges in Distributed Computing Continuum Systems (DCCS). Implementing centralized ZT architecture is seen as unsuitable for the computing continuum (e.g., computing entities with limited connectivity and visibility, etc.). At the same time, implementing decentralized ZT in the computing continuum requires understanding infrastructure limitations and novel approaches to enhance resource access management decisions. To overcome such challenges, we present a novel learning-driven ZT conceptual architecture designed for DCCS. We aim to enhance ZT architecture service quality by incorporating lightweight learning strategies such as Representation Learning (ReL) and distributing ZT components across the computing continuum. The ReL helps to improve the decision-making process by predicting threats or untrusted requests. Through an illustrative example, we show how the learning process detects and blocks the requests, enhances resource access control, and reduces network and computation overheads. Lastly, we discuss the conceptual architecture, processes, and provide a research agenda.

Anna Lackinger, Andrea Morichetta, Pantelis A. Frangoudis et al.

Federated Learning (FL) is a promising machine learning solution in large-scale IoT systems, guaranteeing load distribution and privacy. However, FL does not natively consider infrastructure efficiency, a critical concern for systems operating in resource-constrained environments. Several Reinforcement Learning (RL) based solutions offer improved client selection for FL; however, they do not consider infrastructure challenges, such as resource limitations and device churn. Furthermore, the training of RL methods is often not designed for practical application, as these approaches frequently do not consider generalizability and are not optimized for energy efficiency. To fill this gap, we propose BIPPO (Budget-aware Independent Proximal Policy Optimization), which is an energy-efficient multi-agent RL solution that improves performance. We evaluate BIPPO on two image classification tasks run in a highly budget-constrained setting, with FL clients training on non-IID data, a challenging context for vanilla FL. The improved sampler of BIPPO enables it to increase the mean accuracy compared to non-RL mechanisms, traditional PPO, and IPPO. In addition, BIPPO only consumes a negligible proportion of the budget, which stays consistent even if the number of clients increases. Overall, BIPPO delivers a performant, stable, scalable, and sustainable solution for client selection in IoT-FL.

Morena Barboni, Andrea Morichetta, Andrea Polini

Smart Contracts are software programs that are deployed and executed within a blockchain infrastructure. Due to their immutable nature, directly resulting from the specific characteristics of the deploying infrastructure, smart contracts must be thoroughly tested before their release. Testing is one of the main activities that can help to improve the reliability of a smart contract, so as to possibly prevent considerable loss of valuable assets. It is therefore important to provide the testers with tools that permit them to assess the activity they performed. Mutation testing is a powerful approach for assessing the fault-detection capability of a test suite. In this paper, we propose SuMo, a novel mutation testing tool for Ethereum Smart Contracts. SuMo implements a set of 44 mutation operators that were designed starting from the latest Solidity documentation, and from well-known mutation testing tools. These allow to simulate a wide variety of faults that can be made by smart contract developers. The set of operators was designed to limit the generation of stillborn mutants, which slow down the mutation testing process and limit the usability of the tool. We report a first evaluation of SuMo on open-source projects for which test suites were available. The results we got are encouraging, and they suggest that SuMo can effectively help developers to deliver more reliable smart contracts.

Boris Sedlak, Philipp Raith, Andrea Morichetta et al.

Edge devices have limited resources, which inevitably leads to situations where stream processing services cannot satisfy their needs. While existing autoscaling mechanisms focus entirely on resource scaling, Edge devices require alternative ways to sustain the Service Level Objectives (SLOs) of competing services. To address these issues, we introduce a Multi-dimensional Autoscaling Platform (MUDAP) that supports fine-grained vertical scaling across both service- and resource-level dimensions. MUDAP supports service-specific scaling tailored to available parameters, e.g., scale data quality or model size for a particular service. To optimize the execution across services, we present a scaling agent based on Regression Analysis of Structural Knowledge (RASK). The RASK agent efficiently explores the solution space and learns a continuous regression model of the processing environment for inferring optimal scaling actions. We compared our approach with two autoscalers, the Kubernetes VPA and a reinforcement learning agent, for scaling up to 9 services on a single Edge device. Our results showed that RASK can infer an accurate regression model in merely 20 iterations (i.e., observe 200s of processing). By increasingly adding elasticity dimensions, RASK sustained the highest request load with 28% less SLO violations, compared to baselines.

Andrea Morichetta, Pedro Casas, Marco Mellia

The application of unsupervised learning approaches, and in particular of clustering techniques, represents a powerful exploration means for the analysis of network measurements. Discovering underlying data characteristics, grouping similar measurements together, and identifying eventual patterns of interest are some of the applications which can be tackled through clustering. Being unsupervised, clustering does not always provide precise and clear insight into the produced output, especially when the input data structure and distribution are complex and difficult to grasp. In this paper we introduce EXPLAIN-IT, a methodology which deals with unlabeled data, creates meaningful clusters, and suggests an explanation to the clustering results for the end-user. EXPLAIN-IT relies on a novel explainable Artificial Intelligence (AI) approach, which allows to understand the reasons leading to a particular decision of a supervised learning-based model, additionally extending its application to the unsupervised learning domain. We apply EXPLAIN-IT to the problem of YouTube video quality classification under encrypted traffic scenarios, showing promising results.

Flavio Corradini, Andrea Morichetta, Andrea Polini et al.

The BPMN 2.0 standard is a widely used semi-formal notation to model distributed information systems from different perspectives. The standard makes available a set of diagrams to represent such perspectives. Choreography diagrams represent global constraints concerning the interactions among system components without exposing their internal structure. Collaboration diagrams instead permit to depict the internal behaviour of a component, also referred as process, when integrated with others so to represent a possible implementation of the distributed system. This paper proposes a design methodology and a formal framework for checking conformance of choreographies against collaborations. In particular, the paper presents a direct formal operational semantics for both BPMN choreography and collaboration diagrams. Conformance aspects are proposed through two relations defined on top of the defined semantics. The approach benefits from the availability of a tool we have developed, named C4, that permits to experiment the theoretical framework in practical contexts. The objective here is to make the exploited formal methods transparent to system designers, thus fostering a wider adoption by practitioners.