arXiv subjects

Patrizio Dazzi

Publications and source records attributed to Patrizio Dazzi.

At least 19 recordsLinked to original sources

arXiv2026

Epico: Long-Lived WebAssembly Components for High-Performance Serverless Stream Processing

While serverless computing is popular, its dominant Function-as-a-Service (FaaS) model is ill-suited for stream processing because its stateless, centrally orchestrated functions cannot efficiently handle continuous, low-latency event flows. We introduce Epico, a serverless runtime explicitly designed to resolve these inefficiencies at the runtime level. Epico executes pipeline stages as persistent WebAssembly components, enabling independent, zero-to-infinity autoscaling based on queue-depth SLOs and routing events directly between stages using broker-free ZeroMQ channels. To optimize short execution paths, it utilizes a credit-based sliding window to amortize inter-process communication costs. Evaluations demonstrate that Ahead-of-Time (AOT) compilation reduces cold-start latencies from hundreds of milliseconds to sub-millisecond ranges, while the credit window improves single-worker throughput by up to $4.3\times$. Compared to Apache OpenWhisk, Epico bypasses the orchestrator bottlenecks and container overheads that typically hinder FaaS streaming workloads.

cs.DC↗

arXiv2026

An Analysis of Untrained Deep Reservoir Networks for Audio Surveillance

In this paper, we investigate untrained recurrent models from the Reservoir Computing (RC) paradigm for audio surveillance, focusing on bidirectional Echo State Networks with different depths, from shallow to deep configurations, for emergency sound event detection. We evaluate these models on the MIVIA Audio Events dataset in a multiclass setting across different Signal-to-Noise Ratio (SNR) levels, with the goal of assessing the trade-off between depth, recognition performance, and computational efficiency. We compare the proposed architectures against fully trained recurrent and convolutional-recurrent baselines, namely Bidirectional Long Short-Term Memory networks (BiLSTMs) and Convolutional Recurrent Neural Networks (CRNNs). Results show that deep and shallow reservoir-based models achieve competitive recognition rates, with deeper variants being more robust in highly noisy conditions and shallower ones offering the most favorable efficiency profile, particularly on edge devices such as the NVIDIA Orin. In addition, the proposed approach remains robust across different input representations, including log-Mel spectrograms and MFCCs with varying resolutions. These findings highlight untrained reservoir architectures as a promising solution for resource-constrained audio surveillance scenarios.

cs.SD↗

arXiv2026

A Neurosymbolic Prolog Skill for LLM-Driven Service Placement

Service placement in the cloud-edge continuum requires assigning application components to heterogeneous resources under multiple constraints, including latency, locality, and policy requirements. Existing approaches rely on optimisation models or heuristics that require explicit modelling, while neural methods lack transparency and formal guarantees. This work proposes a neuro-symbolic alternative based on a Prolog skill, a reusable interface for schema-constrained fact generation and querying, for constraint-aware placement. The skill enables a language model to structure placement intent into symbolic facts, rules, and queries, while delegating validation and reasoning to Prolog. This design bridges high-level intent and formal constraint evaluation, enabling inspectable and policy-aware placement decisions in cloud-edge environments.

cs.DC↗

arXiv2026

Trade-offs in Decentralized Agentic AI Discovery Across the Compute Continuum

Agentic systems deployed across the compute continuum need discovery mechanisms that remain effective across cloud, edge, and intermittently connected domains. In some emerging agentic architectures, decentralized discovery is already an active design direction, placing DHT-based lookup on the path toward agent directories. This paper studies the trade-offs among major structured-overlay families for agent discovery, comparing Chord, Pastry, and Kademlia as candidate indexing substrates within a shared control-plane framework. Using a benchmark subset centered on a 4096-node stationary comparison and a representative 4096-node churn benchmark, the paper characterizes how discovery reliability, startup behavior, and control-plane overhead vary across these overlays. The goal is to clarify the operating points they expose for agent discovery across edge-to-cloud environments.

cs.DC↗

arXiv2026

Usable Agent Discovery for Decentralized AI Systems

Large-scale agentic systems run on distributed infrastructures where many software agents share physical hosts and are discovered via peer-to-peer mechanisms. Discovery must handle node-level churn from failures and host departures and agent-level churn from demand-driven activation, deactivation, and state changes. Their interaction reshapes classic trade-offs between structured and unstructured overlays. We study decentralized agent discovery under this two-level churn, assuming nodes host multiple agents, overlays are structured or gossip-based, and agents switch between warm and cold states. Using Kademlia as a structured and Cyclon+Vicinity as a gossip baseline, we compare stable, node-churn-only, agent-cooling-only, and combined regimes to see when routing efficiency, resilience, and service readiness align or favor different designs. Structured overlays are more robust and efficient in stable and node-churn regimes, while gossip-based overlays remain competitive and can be faster when readiness dominates.

cs.MA↗

arXiv2026

High-Performance Serverless Computing: A Systematic Literature Review on Serverless for HPC, AI, and Big Data

The widespread deployment of large-scale, compute-intensive applications such as high-performance computing, artificial intelligence, and big data is leading to convergence between cloud and high-performance computing infrastructures. Cloud providers are increasingly integrating high-performance computing capabilities in their infrastructures, such as hardware accelerators and high-speed interconnects, while researchers in the high-performance computing community are starting to explore cloud-native paradigms to improve scalability, elasticity, and resource utilization. In this context, serverless computing emerges as a promising execution model to efficiently handle highly dynamic, parallel, and distributed workloads. This paper presents a comprehensive systematic literature review of 122 research articles published between 2018 and early 2025, exploring the use of the serverless paradigm to develop, deploy, and orchestrate compute-intensive applications across cloud, high-performance computing, and hybrid environments. From these, a taxonomy comprising eight primary research directions and nine targeted use case domains is proposed, alongside an analysis of recent publication trends and collaboration networks among authors, highlighting the growing interest and interconnections within this emerging research field. Overall, this work aims to offer a valuable foundation for both new researchers and experienced practitioners, guiding the development of next-generation serverless solutions for parallel compute-intensive applications.

cs.DC↗

arXiv2025

A Parallel and Distributed Rust Library for Core Decomposition on Large Graphs

In this paper, we investigate the parallelization of $k$-core decomposition, a method used in graph analysis to identify cohesive substructures and assess node centrality. Although efficient sequential algorithms exist for this task, the scale of modern networks requires faster, multicore-ready approaches. To this end, we adapt a distributed $k$-core algorithm originally proposed by Montresor et al. to shared-memory systems and implement it in Rust, leveraging the language's strengths in concurrency and memory safety. We developed three progressively optimized versions: SequentialK as a baseline, ParallelK introducing multi-threaded message passing, and FastK further reducing synchronization overhead. Extensive experiments on real-world datasets, including road networks, web graphs, and social networks, show that FastK consistently outperforms both SequentialK and ParallelK, as well as a reference Python implementation available in the NetworkX library. Results indicate up to an 11x speedup on 16 threads and execution times up to two orders of magnitude faster than the Python implementation.

cs.DC↗

arXiv2025

Exact Nearest-Neighbor Search on Energy-Efficient FPGA Devices

This paper investigates the usage of FPGA devices for energy-efficient exact kNN search in high-dimension latent spaces. This work intercepts a relevant trend that tries to support the increasing popularity of learned representations based on neural encoder models by making their large-scale adoption greener and more inclusive. The paper proposes two different energy-efficient solutions adopting the same FPGA low-level configuration. The first solution maximizes system throughput by processing the queries of a batch in parallel over a streamed dataset not fitting into the FPGA memory. The second minimizes latency by processing each kNN incoming query in parallel over an in-memory dataset. Reproducible experiments on publicly available image and text datasets show that our solution outperforms state-of-the-art CPU-based competitors regarding throughput, latency, and energy consumption. Specifically, experiments show that the proposed FPGA solutions achieve the best throughput in terms of queries per second and the best-observed latency with scale-up factors of up to 16.6X. Similar considerations can be made regarding energy efficiency, where results show that our solutions can achieve up to 11.9X energy saving w.r.t. strong CPU-based competitors.

cs.IR↗

arXiv2025

A Fast-Converging Decentralized Approach to the Weighted Minimum Vertex Cover Problem

We address the problem of computing a Minimum Weighted Vertex Cover (MWVC) in a decentralized network. MWVC, a classical NP-hard problem, is foundational in applications such as network monitoring and resource placement. We propose a fully decentralized protocol where each node makes decisions using only local knowledge and communicates with its neighbors. The method is adaptive, communication-efficient, and avoids centralized coordination. We evaluate the protocol on real-world and synthetic graphs, comparing it to both centralized and decentralized baselines. Our results demonstrate competitive solution quality with reduced communication overhead, highlighting the feasibility of MWVC computation in decentralized environments.

cs.DC↗

arXiv2025

Decentralized and Self-adaptive Core Maintenance on Temporal Graphs

Key graph-based problems play a central role in understanding network topology and uncovering patterns of similarity in homogeneous and temporal data. Such patterns can be revealed by analyzing communities formed by nodes, which in turn can be effectively modeled through temporal $k$-cores. This paper introduces a novel decentralized and incremental algorithm for computing the core decomposition of temporal networks. Decentralized solutions leverage the ability of network nodes to communicate and coordinate locally, addressing complex problems in a scalable, adaptive, and timely manner. By leveraging previously computed coreness values, our approach significantly reduces the activation of nodes and the volume of message exchanges when the network changes over time. This enables scalability with only a minimal trade-off in precision. Experimental evaluations on large real-world networks under varying levels of dynamism demonstrate the efficiency of our solution compared to a state-of-the-art approach, particularly in terms of active nodes, communication overhead, and convergence speed.

cs.DC↗

arXiv2025

WebAssembly and Unikernels: A Comparative Study for Serverless at the Edge

Serverless computing at the edge requires lightweight execution environments to minimize cold start latency, especially in Urgent Edge Computing (UEC). This paper compares WebAssembly and unikernel-based MicroVMs for serverless workloads. We present Limes, a WebAssembly runtime built on Wasmtime, and evaluate it against the Firecracker-based environment used in SPARE. Results show that WebAssembly offers lower cold start times for lightweight functions but suffers with complex workloads, while Firecracker provides higher, but stable, cold starts and better execution performance, particularly for I/O-heavy tasks.

cs.DC↗

arXiv2025

ECLYPSE: a Python Framework for Simulation and Emulation of the Cloud-Edge Continuum

The Cloud-Edge continuum enhances application performance by bringing computation closer to data sources. However, it presents considerable challenges in managing resources and determining service placement, as these tasks require navigating diverse, dynamic environments characterised by fluctuating network conditions. Addressing these challenges calls for tools combining simulation and emulation of Cloud-Edge systems to rigorously assess novel application and resource management strategies. In this paper, we introduce ECLYPSE, a Python-based framework that enables the simulation and emulation of the Cloud-Edge continuum via adaptable resource allocation and service placement models. ECLYPSE features an event-driven architecture for dynamically adapting network configurations and resources. It also supports seamless transitions between simulated and emulated setups. In this work, ECLYPSE capabilities are illustrated over three use cases, showing how the framework supports rapid prototyping across diverse experimental settings.

cs.NI↗

arXiv2025

Combining Declarative and Linear Programming for Application Management in the Cloud-Edge Continuum

This work investigates the data-aware multi-service application placement problem in Cloud-Edge settings. We previously introduced EdgeWise, a hybrid approach that combines declarative programming with Mixed-Integer Linear Programming (MILP) to determine optimal placements that minimise operational costs and unnecessary data transfers. The declarative stage pre-processes infrastructure constraints to improve the efficiency of the MILP solver, achieving optimal placements in terms of operational costs, with significantly reduced execution times. In this extended version, we improve the declarative stage with continuous reasoning, presenting EdgeWiseCR, which enables the system to reuse existing placements and reduce unnecessary recomputation and service migrations. In addition, we conducted an expanded experimental evaluation considering multiple applications, diverse network topologies, and large-scale infrastructures with dynamic failures. The results show that EdgeWiseCR achieves up to 65% faster execution compared to EdgeWise, while preserving placement stability under dynamic conditions.

cs.DC↗

arXiv2025

Declarative Traffic Engineering for Low-Latency and Reliable Networking

Cloud-Edge applications like industrial control systems and connected vehicles demand stringent end-to-end latency guarantees. Among existing data plane candidate solutions for bounded latency networking, the guaranteed Latency-Based Forwarding (gLBF) approach ensures punctual delivery of traffic flows by managing per-hop delays to meet specific latency targets, while not requiring that per-flow states are maintained at each hop. However, as a forwarding plane mechanism, gLBF does not define the control mechanisms for determining feasible forwarding paths and per-hop latency budgets for packets to fulfil end-to-end latency objectives. In this work, we propose such a control mechanism implemented in Prolog that complies with gLBF specifications, called declarative gLBF (dgLBF). The declarative nature of Prolog allows our prototype to be concise (~120 lines of code) and easy to extend. We show how the core dgLBF implementation is extended to add reliability mechanisms, path protection, and fate-sharing avoidance to enhance fault tolerance and robustness. Finally, we evaluate the system's performance through simulative experiments under different network topologies and with increasing traffic load to simulate saturated network conditions, scaling up to 6000 flows. Our results show a quasi-linear degradation in placement times and system resilience under heavy traffic.

cs.NI↗

arXiv2024

Graph Neural Networks and Reinforcement Learning for Proactive Application Image Placement

The shift from Cloud Computing to a Cloud-Edge continuum presents new opportunities and challenges for data-intensive and interactive applications. Edge computing has garnered a lot of attention from both industry and academia in recent years, emerging as a key enabler for meeting the increasingly strict demands of Next Generation applications. In Edge computing the computations are placed closer to the end-users, to facilitate low-latency and high-bandwidth applications and services. However, the distributed, dynamic, and heterogeneous nature of Edge computing, presents a significant challenge for service placement. A critical aspect of Edge computing involves managing the placement of applications within the network system to minimize each application's runtime, considering the resources available on system devices and the capabilities of the system's network. The placement of application images must be proactively planned to minimize image tranfer time, and meet the strict demands of the applications. In this regard, this paper proposes an approach for proactive image placement that combines Graph Neural Networks and actor-critic Reinforcement Learning, which is evaluated empirically and compared against various solutions. The findings indicate that although the proposed approach may result in longer execution times in certain scenarios, it consistently achieves superior outcomes in terms of application placement.

cs.DC↗

arXiv2024

Urgent Edge Computing

This position paper introduces Urgent Edge Computing (UEC) as a paradigm shift addressing the evolving demands of time-sensitive applications in distributed edge environments, in time-critical scenarios. With a focus on ultra-low latency, availability, resource management, decentralization, self-organization, and robust security, UEC aims to facilitate operations in critical scenarios such as disaster response, environmental monitoring, and smart city management. This paper outlines and discusses the key requirements, challenges, and enablers along with a conceptual architecture. The paper also outlines the potential applications of Urgent Edge Computing

cs.NI↗

arXiv2023

Double Deep Q-Learning-based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications

Nowadays, as the need for capacity continues to grow, entirely novel services are emerging. A solid cloud-network integrated infrastructure is necessary to supply these services in a real-time responsive, and scalable way. Due to their diverse characteristics and limited capacity, communication and computing resources must be collaboratively managed to unleash their full potential. Although several innovative methods have been proposed to orchestrate the resources, most ignored network resources or relaxed the network as a simple graph, focusing only on cloud resources. This paper fills the gap by studying the joint problem of communication and computing resource allocation, dubbed CCRA, including function placement and assignment, traffic prioritization, and path selection considering capacity constraints and quality requirements, to minimize total cost. We formulate the problem as a non-linear programming model and propose two approaches, dubbed B\&B-CCRA and WF-CCRA, based on the Branch \& Bound and Water-Filling algorithms to solve it when the system is fully known. Then, for partially known systems, a Double Deep Q-Learning (DDQL) architecture is designed. Numerical simulations show that B\&B-CCRA optimally solves the problem, whereas WF-CCRA delivers near-optimal solutions in a substantially shorter time. Furthermore, it is demonstrated that DDQL-CCRA obtains near-optimal solutions in the absence of request-specific information.

cs.NI↗

arXiv2021

Efficient Resources Distribution for an Ephemeral Cloud/Edge continuum

This paper presents the idea and the concepts behind the vision of an Ephemeral Cloud/Edge Continuum, a cloud/edge computing landscape that enables the exploitation of a widely distributed, dynamic, and context-aware set of resources. The Ephemeral Continuum answer to the need of combining a plethora of heterogeneous devices, which nowadays are pervasively embedding anthropic environments, with both federations of cloud providers and the resources located at the Edge. The aim of the Ephemeral Continuum is to realise a context-aware and personalised federation of computational, data and network resources, able to manage their heterogeneity in a highly distributed deployment.

cs.DC↗