Titulo Estágio
ML-guided service placement in the Cloud-to-Edge continuum
Áreas de especialidade
Comunicações, Serviços e Infraestruturas
Sistemas Inteligentes
Local do Estágio
Laboratory of Communications and Telematics (LCT – DEI – UC)
Enquadramento
The Cloud-to-Edge continuum describes a distributed environment that provides network and computing capabilities enabling the deployment of applications and services taking advantage of performance, security, and cost-efficient characteristics that are more suited for each one. However, since the Cloud-to-Edge continuum is a dense and vastly heterogeneous environment, selecting the optimal location for service placement is a big challenge. AI and ML are becoming essential to help process and analyze alternatives to guide different management tasks, including those related to service placement, allowing the Edge nodes to cooperate among themselves to select the location in which service instances should be deployed in order to improve different metrics such as latency or energy consumption.
This work focuses on applying ML techniques to allow cooperation among Edge nodes, enabling a distributed solution for service placement in the Cloud-to-Edge continuum. The work will be developed using simulation tools such as YAFS (https://github.com/acsicuib/YAFS) and MobFogSim (https://github.com/diogomg/MobFogSim).
The work comprises the interaction of an international team, being supervised by researchers from the Laboratory of Communications and Telematics (LCT) in the Centre for Informatics and Systems of the University of Coimbra (CISUC), Portugal, and from the Institute of Computing (IC) from the University of Campinas (UNICAMP), Brazil.
Objetivo
The work consists of designing and developing an ML-based solution for service placement for the Cloud-to-Edge continuum.
Plano de Trabalhos - Semestre 1
Phase 1: Study of the state-of-the-art on Cloud-to-Edge environments
Phase 2: Review of ML techniques for distributed environments such as the Cloud-to-Edge
Phase 3: Familiarization with the simulation environment chosen
Phase 4: Service requirement and network resource definition
Phase 5: Prepare midterm defense document
Plano de Trabalhos - Semestre 2
Phase 7: Implement a baseline service placement mechanism based on ML using the simulator chosen
Phase 8: Implement a second service placement mechanism based on ML using the simulator chosen
Phase 9: Validate the mechanisms and analyze their results
Phase 10: Prepare the dissertation document
Condições
The work will be performed in the LCT Laboratory (Departamento de Engenharia Informática, Universidade de Coimbra, Portugal), in close collaboration with researchers from the Institute of Computing (University of Campinas, Brazil).
Observações
There is the possibility of awarding the internship with a scholarship, according to the candidate’s profile.
This work will be co-supervised by Prof. Nuno Lourenço (DEI-UC, Portugal) and Prof. Luiz Bittencourt (IC-UNICAMP, Brazil).
Orientador
Karima Velasquez
kcastro@dei.uc.pt 📩