Edge–cloud computing infrastructures are increasingly widespread as they combine the flexibility of cloud-native development tools with the performance and security of distributed computing environments. Function-as-a-Service has emerged as a powerful abstraction that overcomes the limitations of a micro-service architecture. However, it generally does not support stateful functions, making it unsuitable for many practical applications in, e.g., Internet of Things (IoT) and real-time analytics. In this paper, we explore a novel paradigm, based on stateful asynchronous agents, that goes beyond traditional serverless computing. We focus on several key technical aspects: programming model, deployment procedures, design of a flexible compute node, and state management. We illustrate our paradigm using the EDGELESS platform as a concrete implementation of this stateful agents’ pattern. We report proof-of-concept experiment results obtained in a testbed with heterogeneous resource-constrained edge nodes that showcase some distinguishing features of our platform: scalable management of lightweight function instances, the advantage of keeping the state local at function instances, and delegated orchestration to enable a third-party agent to make migration decisions in a group of local nodes.

In critical scenarios like disaster response or real-time monitoring, efficient and adaptive task scheduling is essential. This paper presents a decentralized framework using Nash Q-learning, a game-theoretic reinforcement learning technique, for urgent edge computing. Tasks are allocated among agents based on strategies derived from multi-agent interactions modeled as a Markov Game. The method promotes decentralized cooperation and improves responsiveness by adapting to dynamic conditions. We provide a formal model and outline its applicability to various edge environments.

The rise of Artificial Intelligence and Large Language Models is driving increased GPU usage in data centers for complex training and inference tasks, impacting operational costs, energy demands, and the environmental footprint of large-scale computing infrastructures. This work addresses the online scheduling problem in GPU datacenters, which involves scheduling tasks without knowledge of their future arrivals. We focus on two objectives: minimizing GPU fragmentation and reducing power consumption. GPU fragmentation occurs when partial GPU allocations hinder the efficient use of remaining resources, especially as the datacenter nears full capacity. A recent scheduling policy, Fragmentation Gradient Descent (FGD), leverages a fragmentation metric to address this issue. Reducing power consumption is also crucial due to the significant power demands of GPUs. To this end, we propose PWR, a novel scheduling policy to minimize power usage by selecting power-efficient GPU and CPU combinations. This involves a simplified model for measuring power consumption integrated into a Kubernetes score plugin. Through an extensive experimental evaluation in a simulated cluster, we show how PWR, when combined with FGD, achieves a balanced trade-off between reducing power consumption and minimizing GPU fragmentation.

We present Rusty-Cracker, a high-performance Rust library that implements a parallel version of the Cracker algorithm for efficiently identifying connected components in large-scale graphs. Designed to address the growing demands of graph analytics in fields such as social network analysis, bioinformatics, and infrastructure modeling, Rusty-Cracker leverages Rust's concurrency and memory safety features to ensure both speed and reliability. The adapted Cracker algorithm capitalizes on modern multi-core architectures through parallel processing, effectively minimizing synchronization overhead and optimizing workload distribution. This design significantly reduces computational time while maintaining accuracy. We present the implementation details and evaluate its performance on a real-world dataset of undirected graphs.

Serverless computing is regarded as a good match for distributed edge infrastructures. However, bringing the function-as-a-service model to a highly dynamic, distributed, and heterogeneous pool of resources has its fair amount of challenges. Allocation of functions to the proper resource is an essential operation that avoids over-provisioning and the relative waste of computational resources. In this work, we propose Dike, a bi-level function scheduling and resource allocation framework designed to meet end-to-end latency SLOs (service-level objectives). Dike leverages deep reinforcement learning to balance resource provisioning and monetary cost by incorporating both composite cost and SLO violations into the reward. Extensive simulations with real-world production workloads demonstrate the superiority of Dike. Experimental results show that the proposed algorithms approximate the results of state-of-the-art ILP solver within a factor of 1.08 while dramatically reducing the scheduling time.

This paper presents SPARE, a novel serverless platform that supports self-adaptive resource allocation and reconfiguration, thereby increasing the availability of computing resources for time-critical tasks in urgent events. In emergency scenarios, SPARE reallocates resources by forwarding serverless function invocations to the nearest edge nodes having sufficient capacity. Additionally, the platform employs the use of unikernels and lightweight virtualization through Firecracker, which helps to reduce cold start times and improve function responsiveness. The experimental results demonstrate that SPARE is capable of releasing up to one-third of edge nodes within a serverless edge platform, while only experiencing a mild increase in latency, thus maintaining service continuity.

Seaports play a crucial role in the global economy, and researchers have sought to understand their significance through various studies.In this paper, we aim to explore the common characteristics shared by important ports by analyzing the network of connections formed by vessel movement among them.To accomplish this task, we adopt a bottom-up network construction approach that combines three years' worth of AIS (Automatic Identification System) data from around the world, constructing a Ports Network that represents the connections between different ports.Through this representation, we utilize machine learning to assess the relative significance of various port features.Our model examined such features and revealed that geographical characteristics and the port's depth are indicators of a port's importance to the Ports Network.Accordingly, this study employs a data-driven approach and utilizes machine learning to provide a comprehensive understanding of the factors contributing to the extent of ports.Our work aims to inform decision-making processes related to port development, resource allocation, and infrastructure planning within the industry.

This paper presents a novel algorithm for the Vertex Cover problem, inspired by the Think-Like-A-Vertex (TLAV) paradigm. The Vertex Cover problem, a fundamental challenge in graph theory, finds significant relevance in the context of the compute continuum, where the optimal placement of application images across a diverse range of computational resources is a critical concern. Our proposed TLAV-based algorithm addresses this challenge by leveraging local information at each vertex to make intelligent decisions, thereby reducing the global complexity of the problem. While this approach could potentially lead to resource overprovisioning, we argue that in the context of the compute continuum, this trade-off can provide more flexibility and redundancy, enhancing the reliability of the system. Through extensive analysis and experimental results, we demonstrate the efficiency and scalability of our algorithm on large-scale graphs, making a significant contribution to the field of resource management in the compute continuum.

EDGELESS is a serverless platform targeting edge computing that supports widely distributed deployments using heterogeneous devices. We present its components, architecture, and programming model. Our working prototype of EDGELESS enables executing lightweight functions and is already available as open-source.

The transition from Cloud Computing to a Cloud-Edge continuum brings many new exciting possibilities for interactive and data-intensive Next Generation applications, but as many challenges. Approaches and solutions that successfully worked in the Cloud space now need to be rethought for the Edge's distributed, heterogeneous and dynamic ecosystem. The placement of application images needs to be proactively devised to reduce as much as possible the image transfer time and comply with the dynamic nature and strict requirements of the applications. To this end, this paper proposes an approach based on the combination of Graph Neural Networks and actor-critic Reinforcement Learning. The approach is analyzed empirically and compared with a state-of-the-art solution. The results show that the proposed approach exhibits a larger execution times but generally better results in terms of application image placement.

This paper proposes a continuum-aware programming model enabling the execution of application workflows across the compute continuum: cloud, fog and edge resources. It simplifies the management of heterogeneous nodes while alleviating the burden of programmers and unleashing innovation. This model optimizes the continuum through advanced development experiences by transforming workflows into autonomous service collaborations. It reduces complexity in positioning/interconnecting services across the continuum. A meta-model introduces high-level workflow descriptions as service networks with defined contracts and quality of service, thus enabling the deployment/management of workflows as first-class entities. It also provides automation based on policies, monitoring and heuristics. Tailored mechanisms orchestrate/manage services across the continuum, optimizing performance, cost, data protection and sustainability while managing risks. This model facilitates incremental development with visibility of design impacts and seamless evolution of applications and infrastructures. In this work, we explore this new computing paradigm showing how it can trigger the development of a new generation of tools to support the compute continuum progress.

Federated learning is a popular framework that enables harvesting edge resources' computational power to train a machine learning model distributively. However, it is not always feasible or profitable to have a centralized server that controls and synchronizes the training process. In this paper, we consider the problem of training a machine learning model over a network of nodes in a fully decentralized fashion. In particular, we look for empirical evidence on how sensitive is the training process for various network characteristics and communication parameters. We present the outcome of several simulations conducted with different network topologies, datasets, and machine learning models.

To assure a timely fruition of media and interactive applications to end users is a complex challenge, especially when potentially spread worldwide, at home or in mobility. It in fact requires a careful placement of the software services on the right computational resources, such that those services are placed as close as possible to end users to mitigate the effect of network on the user experience. In this demo paper, we present a tool that aims to facilitate the placement of latency sensitive applications on computational resources, by considering the geographical positioning of the user demand, the user experience, and the budget limitation of application owners.

Summary Avatars' mobility is an essential element to design, validate, and compare different distributed virtual environment architectures. It has a direct impact on the management of such systems because it defines the workload associated with the areas in the virtual world. Currently, a relevant part of this evaluation is conducted by means of synthetic traces generated through mobility models. Despite that, in the last decade, several models have been proposed in literature to describe avatars mobility. However, a standard methodology that drives researchers in their evaluation does not yet exist. In order to alleviate this issue, we presentTRACE, an open source tool supporting the generation and analysis of traces by means of embedded mobility models.TRACE's ultimate aim is to facilitate the evaluation and comparison of virtual environments and allow researchers to focus on developing their solution rather than spend time to code and test custom mobility traces.TRACEprovides a unified format to describe the traces. It enables scalable and efficient trace generation and analysis for thousands of avatars with seven built‐in models. Also, it defines APIs enabling the integration of additional models, different configurations of the environment, and several built‐in metrics to analyze the generated traces.

Finding connected components is a fundamental task in applications dealing with graph analytics, such as social network analysis, web graph mining and image processing. The exponentially growing size of today's graphs has required the definition of new computational models and algorithms for their efficient processing on highly distributed architectures. In this paper we present CRACKER, an efficient iterative MapReduce-like algorithm to detect connected components in large graphs. The strategy of CRACKER is to transform the input graph in a set of trees, one for each connected component in the graph. Nodes are iteratively removed from the graph and added to the trees, reducing the amount of computation at each iteration. We prove the correctness of the algorithm, evaluate its computational cost and provide an extensive experimental evaluation considering a wide variety of synthetic and real-world graphs. The experimental results show that CRACKER consistently outperforms state-of-the-art approaches both in terms of total computation time and volume of messages exchanged.

The problem of finding connected components in a graph is common to several applications dealing with graph analytics, such as social network analysis, web graph mining and image processing. The exponentially growing size of graphs requires the definition of appropriated computational models and algorithms for their processing on high throughput distributed architectures. In this paper we present cracker, an efficient iterative algorithm to detect connected components in large graphs. The strategy of cracker is to iteratively grow a spanning tree for each connected component of the graph. Nodes added to such trees are discarded from the computation in the subsequent iterations. We provide an extensive experimental evaluation considering a wide variety of synthetic and real-world graphs. The experimental evaluation shows that cracker consistently outperforms state-of-the-art approaches both in terms of total computation time and volume of messages exchanged.

Summary A fundamental problem for the development of peer‐to‐peer (P2P) distributed virtual environments, like massively multiplayer on‐line games, is the definition of an overlay supporting interest management, that is, determining all the entities of the virtual world that are relevant for a given player. To this end, this paper proposes a gossip‐based approach that considers the coverage of the area of interest of peers as the guiding principle for the definition of the P2P overlay and its maintenance. The resulting overlay provides a support for a best‐effort resolution of interest management, mostly supported through communications on the P2P overlay, with minimal intervention of a centralized entity. The paper presents a set of extensive simulations based on realistic mobility traces. The experimental results show the effectiveness of gossiping for the construction and maintenance of a best‐effort overlay for interest management. Copyright © 2014 John Wiley & Sons, Ltd.

Summary This paper presents a novel Area Of Interest (AOI)‐cast algorithm for distributed virtual environments targeted to Delaunay‐based P2P overlays. The algorithm exploits the mathematical properties of Delaunay triangulations to build a spanning tree supporting the notification of the events generated by a peer to the other ones located in its AOI. The spanning tree is computed by thereversing compass routing, a routing algorithm proposed for geometric networks. Our approach presents a set of novel features. First, it requires only the knowledge of the peer's neighbors, so that the amount of traffic load on the P2P overlay is minimized. Second, we prove that, for circular shaped AOI, the algorithm builds a spanning tree covering all and only the peers of the AOI. Finally, our approach takes into account the possible inconsistencies among the local views of the peers, because the network latency, by introducing a tolerance threshold in the reverse compass routing. We present a set of simulations considering both synthetic data and real data traces taken from a real multiplayer game, which show the effectiveness of our proposal. Copyright © 2012 John Wiley & Sons, Ltd.

The broad diffusion of Cloud Computing has fostered the proliferation of a large number of cloud computing providers. The need of Cloud Brokers arises for helping consumers in discovering, considering and comparing services with different capabilities and offered by different providers. Also, consuming services exposed by different providers, when possible, may alleviate the vendor lock-in. While it can be straightforward to choose the best provider when deploying small and homogeneous applications, things get harder if the size and complexity of applications grow up. In this paper we propose a genetic approach for Cloud Brokering, focusing on finding Infrastructure-as-a-Service (IaaS) resources for satisfying Quality of Service (QoS) requirements of applications. We performed a set of experiments with an implementation of such broker. Results show that our broker can find near-optimal solutions even when dealing with hundreds of providers, trying at the same time to mitigate the vendor lock-in.

Cloud Federation is a promising approach to enhance cross-cloud application execution. Nevertheless, such approach emphasizes open challenges in Cloud Computing, such as revoking long-lasting authorization on resources as soon as conditions granting the access right are no longer valid. To tackle this kind of issues, we built a prototype of Cloud Federation that leverages the concept of Usage Control (UCON), by continuously monitoring and reassessing the users right on resources. We exploited an extension of the XACML standard and measured the overhead caused by different security policies and distributions of requests. Results suggest that the UCON model can be effectively applied in Cloud Federations and its performance is sustainable when applied to the relevant actions of the lifecycle of applications.