Matteo Flocco

Bio: Matteo Flocco is an academic researcher from Saint Louis University. The author has contributed to research in topics: Edge computing & Enhanced Data Rates for GSM Evolution. The author has an hindex of 2, co-authored 6 publications receiving 18 citations.

Owl: Congestion Control with Partially Invisible Networks via Reinforcement Learning

Abstract: Years of research on transport protocols have not solved the tussle between in-network and end-to-end congestion control. This debate is due to the variance of conditions and assumptions in different network scenarios, e.g., cellular versus data center networks. Recently, the community has proposed a few transport protocols driven by machine learning, nonetheless limited to end-to-end approaches.In this paper, we present Owl, a transport protocol based on reinforcement learning, whose goal is to select the proper congestion window learning from end-to-end features and network signals, when available. We show that our solution converges to a fair resource allocation after the learning overhead. Our kernel implementation, deployed over emulated and large scale virtual network testbeds, outperforms all benchmark solutions based on end-to-end or in-network congestion control.

APRON: an Architecture for Adaptive Task Planning of Internet of Things in Challenged Edge Networks

Abstract: Recently, the growth of Internet of Things (IoT) devices combined with edge computing opened many opportunities for several novel applications. Typical examples are Unmanned Aerial Vehicles (UAV) that are deployed for photogrammetry, surveillance, disaster rapid response and environmental monitoring. A common challenge across all these networked applications is the ability to provide a persistent service - a service able to continuously maintain a high level of performance - responding to events that may change the state of the network, e.g., nodes or link failures. To cope with this challenge, in this paper we propose APRON, an edge cloud-assisted architecture for distributed and adaptive task planning management in a network of IoT devices, e.g., drones. APRON uses a novel planning strategy that, leveraging a Jackson's network model, supports monitoring and control operations while the states of the (edge or cloud) network evolve. By using APRON, edge computing application programmers can design and implement a wide range of IoT task management policies leveraging different protection methodologies across several failure models.

Supporting Sustainable Virtual Network Mutations With Mystique

Abstract: The abiding attempt of automation has also permeated the networks, with the ability to measure, analyze, and control themselves in an automated manner, by reacting to changes in the environment (e.g., demand). When provided with these features, networks are often labeled as “self-driving” or “autonomous”. In this regard, the provision and orchestration of physical or virtual resources are crucial for both Quality of Service (QoS) guarantees and cost management in the edge/cloud computing environment. To effectively manage the lifecycle of these resources, an auto-scaling mechanism is essential. However, traditional threshold-based and recent Machine Learning (ML)-based policies are often unable to address the soaring complexity of networks due to their centralized approach. By relying on multi-agent reinforcement learning, we propose Mystique, a solution that learns from the load on links to establish the minimal set of active network resources. As traffic demands ebb and flow, our adaptive and self-driving solution can scale up and down and also react to failures in a fully automated, flexible, and efficient manner. Our results demonstrate that the presented solution can reduce network energy consumption while providing an adequate service level, outperforming other benchmark auto-scaling approaches.

Partially Oblivious Congestion Control for the Internet via Reinforcement Learning

Abstract: Despite years of research on transport protocols, the tussle between in-network and end-to-end congestion control has not been solved. This debate is due to the variance of conditions and assumptions in different network scenarios, e.g., cellular versus data center networks. Recently, the community has proposed a few transport protocols driven by machine learning, nonetheless limited to end-to-end approaches. In this paper, we present Owl, a transport protocol based on reinforcement learning, whose goal is to select the proper congestion window learning from end-to-end features and network signals, when available. We show that our solution converges to a fair resource allocation after the learning overhead. Our kernel implementation, deployed over emulated and large scale virtual network testbeds, outperforms all benchmark solutions based on end-to-end or in-network congestion control.

Sustainable Task Offloading in UAV Networks via Multi-Agent Reinforcement Learning

Abstract: The recent growth of IoT devices, along with edge computing, has revealed many opportunities for novel applications. Among them, Unmanned Aerial Vehicles (UAVs), which are deployed for surveillance and environmental monitoring, are attracting increasing attention. In this context, typical solutions must deal with events that may change the state of the network, providing a service that continuously maintains a high level of performance. In this paper, we address this problem by proposing a distributed architecture that leverages a Multi-Agent Reinforcement Learning (MARL) technique to dynamically offload tasks from UAVs to the edge cloud. Nodes of the system co-operate to jointly minimize the overall latency perceived by the user and the energy usage on UAVs by continuously learning from the environment the best action, which entails the decision of offloading and, in this case, the best transmission technology, i.e., Wi-Fi or cellular. Results validate our distributed architecture and show the effectiveness of the approach in reaching the above targets.

Energy-aware mobile edge computing for low-latency visual data processing

Abstract: New opportunities exist for applications such as disaster incident response that can benefit from the convergence of Internet of Things (IoT) and cloud computing technologies. Particularly, new paradigms such as Mobile Edge Computing (MEC) are becoming feasible to handle the data deluge occurring in the network edge to gain insights that assist in real-time decision making. In this paper, we study the potential of MEC to address application issues related to energy management on constrained IoT devices with limited power sources, while also providing low-latency processing of visual data being generated at high resolutions. Using a face recognition application that is important in disaster incident response scenarios, we analyze the tradeoffs in computing policies that offload visual data processing (i.e., to an edge cloud or a core cloud) at low-to-high workloads, and their impact on energy consumption under different visual data consumption requirements (i.e., users with thick clients or thin clients). From our empirical results obtained from experiments with our face recognition application on a realistic edge and core cloud testbed, we show how MEC can provide flexibility to users who desire energy conservation over low-latency or vice versa in the visual data processing.