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About

About

Education: PhD in Computer Science, Faculdade de Ciências da Universidade do Porto, 2011.

Research interests: concurrency, software verification and testing, unmanned vehicle networks

Selected publications:

Short bio

  • Currently researcher at CRACS/INESC-TEC, Prof. Auxiliar Convidado 25% FCUP
  • 2012-2016: Prof. Auxiliar Convidado c/dedicação exclusiva, FCUL 
  • 2006-2011: PhD student in Computer Science, FCUP
  • 1998-2005: Software engineer/programmer for consulting companies (Portugal and Brazil)
  • 1998: MSc in Advanced Computing, Imperial College London
  • 1997: BSc in Computer Science (pre-Bologne), FCUP

Interest
Topics
Details

Details

  • Name

    Eduardo Brandão Marques
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st April 2016
002
Publications

2023

Jay: A software framework for prototyping and evaluating offloading applications in hybrid edge clouds

Authors
Silva, J; Marques, ERB; Lopes, LMB; Silva, FMA;

Publication
SOFTWARE-PRACTICE & EXPERIENCE

Abstract
We present Jay, a software framework for offloading applications in hybrid edge clouds. Jay provides an API, services, and tools that enable mobile application developers to implement, instrument, and evaluate offloading applications using configurable cloud topologies, offloading strategies, and job types. We start by presenting Jay's job model and the concrete architecture of the framework. We then present the programming API with several examples of customization. Then, we turn to the description of the internal implementation of Jay instances and their components. Finally, we describe the Jay Workbench, a tool that allows the setup, execution, and reproduction of experiments with networks of hosts with different resource capabilities organized with specific topologies. The complete source code for the framework and workbench is provided in a GitHub repository.

2022

Identificação Taxonómica em Biologia usando Inteligência Artificial

Authors
Lopes, L; Marques, E; Mamede, T; Filgueiras, A; Marques, M; Coutinho, M;

Publication
Revista de Ciência Elementar

Abstract

2021

Energy-aware adaptive offloading of soft real-time jobs in mobile edge clouds

Authors
Silva, J; Marques, ERB; Lopes, LMB; Silva, F;

Publication
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS

Abstract
We present a model for measuring the impact of offloading soft real-time jobs over multi-tier cloud infrastructures. The jobs originate in mobile devices and offloading strategies may choose to execute them locally, in neighbouring devices, in cloudlets or in infrastructure cloud servers. Within this specification, we put forward several such offloading strategies characterised by their differential use of the cloud tiers with the goal of optimizing execution time and/or energy consumption. We implement an instance of the model using Jay, a software framework for adaptive computation offloading in hybrid edge clouds. The framework is modular and allows the model and the offloading strategies to be seamlessly implemented while providing the tools to make informed runtime offloading decisions based on system feedback, namely through a built-in system profiler that gathers runtime information such as workload, energy consumption and available bandwidth for every participating device or server. The results show that offloading strategies sensitive to runtime conditions can effectively and dynamically adjust their offloading decisions to produce significant gains in terms of their target optimization functions, namely, execution time, energy consumption and fulfilment of job deadlines.

2020

RAMBLE: Opportunistic Crowdsourcing of User-Generated Data using Mobile Edge Clouds

Authors
Garcia, M; Rodrigues, J; Silva, J; Marques, ERB; Lopes, LMB;

Publication
2020 FIFTH INTERNATIONAL CONFERENCE ON FOG AND MOBILE EDGE COMPUTING (FMEC)

Abstract
We present RAMBLE(1), a framework for georeferenced content-sharing in environments that have limited infrastructural communications, as is the case for rescue operations in the aftermath of natural disasters. RAMBLE makes use of mobile edge-clouds, networks formed by mobile devices in close proximity, and lightweight cloudlets that serve a small geographical area. Using an Android app, users ramble whilst generating geo-referenced content (e.g., text messages, sensor readings, photos, or videos), and disseminate that content opportunistically to nearby devices, cloudlets, or even cloud servers, as allowed by intermittent wireless connections. Each RAMBLE-enabled device can both produce information; consume information for which it expresses interest to neighboors, and; serve as an opportunistic cache for other devices. We describe the architecture of the framework and a case-study application scenario we designed to evaluate its behavior and performance. The results obtained reinforce our view that kits of RAMBLE-enabled mobile devices and modest cloudlets can constitute lightweight and flexible untethered intelligence gathering platforms for first responders in the aftermath of natural disasters, paving the way for the deployment of humanitary assistance and technical staff at large.

2020

JAY: Adaptive Computation Offloading for Hybrid Cloud Environments

Authors
Silva, J; Marques, ERB; Lopes, LMB; Silva, F;

Publication
2020 FIFTH INTERNATIONAL CONFERENCE ON FOG AND MOBILE EDGE COMPUTING (FMEC)

Abstract
Edge computing is a hot research topic given the ever-increasing requirements of mobile applications in terms of computation and communication and the emerging Internet-of-Things with billions of devices. While ubiquitous and with considerable computational resources, devices at the edge may not be able to handle processing tasks on their own and thus resort to offloading to cloudlets, when available, or traditional cloud infrastructures. In this paper, we present JAY, a modular and extensible platform for mobile devices, cloudlets, and clouds that can manage computational tasks spawned by devices and make informed decisions about offloading to neighboring devices, cloudlets, or traditional clouds. JAY is parametric on the scheduling strategy and metrics used to make offloading decisions, providing a useful tool to study the impact of distinct offloading strategies. We illustrate the use of JAY with an evaluation of several offloading strategies in distinct cloud configurations using a real-world machine learning application, firing tasks can be dynamically executed on or offloaded to Android devices, cloudlet servers, or Google Cloud servers. The results obtained show that edge-clouds form competent computing platforms on their own and that they can effectively be meshed with cloudlets and traditional clouds when more demanding processing tasks are considered. In particular, edge computing is competitive with infrastructure clouds in scenarios where data is generated at the edge, high bandwidth is required, and a pool of computationally competent devices or an edge-server is available. The results also highlight JAY's ability of exposing the performance compromises in applications when they are deployed over distinct hybrid cloud configurations using distinct offloading strategies.

Supervised
thesis

2021

Adaptive Computation Offloading in Mobile Edge Clouds

Author
Joaquim Magalhães Esteves da Silva

Institution
UP-FCUP

2021

Beyond Distributed Transactions through Exactly-once Exchanges

Author
Ziad Ali Kassam

Institution
UM

2020

Property-based testing of ERC-20 smart contracts

Author
Célio Gil Gouveia Rodrigues

Institution
UP-FCUP

2020

Application Security in Continuous Delivery

Author
Fábio Daniel Santos Freitas

Institution
UP-FCUP

2020

Scheduling Computations Over High Churn Networks of Mobile Devices

Author
Joaquim Magalhães Esteves da Silva

Institution
UP-FCUP