Grant: Discovery Grants Program - Individual
École de technologie supérieure
$41,000.00
Exploring Novel Modeling and Learning Techniques for Highly Dependable Emerging Applications on the Edge.
FY 2023-24
Summary
There is an increasing variety of emerging applications and services, which cover almost every aspect and activity of our modern society including healthcare, education, transportation, finance, agriculture, and entertainment. These applications and services have high dependability requirements such as strict real-time constraints and/or high reliability constraints. The recent advancements in the fifth generation (5G) networks and beyond and the integration of network softwarization and virtualization brought the required flexibility and cost efficiency to deploy and operate these services. Furthermore, new concepts such as the network slicing and edge computing have also been defined and standardized in order to support meeting the infrastructure resource management efficiency and the applications stringent latency and reliability requirements. These advancements have led to a very complex and dynamic computing environment, at the edge and core networks, where the aforementioned applications are deployed and run. This in turns leads to new challenging issues facing the design, deployment and operation of these applications and their services in this environment. The long-term vision of the proposed research program is to explore new model-based and machine learning techniques to support the design, deployment and management of emerging microservice-based applications having stringent dependability requirements. We anticipate that the proposed research program will contribute significantly to improving the engineering techniques and practices used for the development and operation of applications with high dependability requirements deployed on edge network infrastructure. Moreover, we expect that this research program will contribute to the graduation of three PhD and three Master students, who will build a strong expertise in the fields of model-driven software engineering, machine learning, cloud-native microservice-based applications design and operation, edge computing and 5G network technologies.
Program
Discovery Grants Program - Individual
Recipient
Location
Quebec, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
RGPIN-2023-03369
Name nom
Gherbi, Abdelouahed
Department département
Software and IT Engineering
Organization id
53
Institution établissement
École de technologie supérieure
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2023
Award amount
41000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1507
Committee name en
Computer Science
Committee name fr
Sciences informatiques
Area of application code
801
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Computer software
Area of application fr
Logiciel
Research subject code
9999
Research subject group en
Not available
Research subject group fr
Not available
Research subject en
Non disponible
Research subject fr
Non disponible
Application title
Exploring Novel Modeling and Learning Techniques for Highly Dependable Emerging Applications on the Edge.
Keyword
Cloud-Native Technologies; Dependability; Distributed Software Systems; Edge Computing; Latency; Machine Learning; Microservices architectures; Model-based Software engineering; Orchestration; Reliability
Application summary
There is an increasing variety of emerging applications and services, which cover almost every aspect and activity of our modern society including healthcare, education, transportation, finance, agriculture, and entertainment. These applications and services have high dependability requirements such as strict real-time constraints and/or high reliability constraints. The recent advancements in the fifth generation (5G) networks and beyond and the integration of network softwarization and virtualization brought the required flexibility and cost efficiency to deploy and operate these services. Furthermore, new concepts such as the network slicing and edge computing have also been defined and standardized in order to support meeting the infrastructure resource management efficiency and the applications stringent latency and reliability requirements. These advancements have led to a very complex and dynamic computing environment, at the edge and core networks, where the aforementioned applications are deployed and run. This in turns leads to new challenging issues facing the design, deployment and operation of these applications and their services in this environment. The long-term vision of the proposed research program is to explore new model-based and machine learning techniques to support the design, deployment and management of emerging microservice-based applications having stringent dependability requirements. We anticipate that the proposed research program will contribute significantly to improving the engineering techniques and practices used for the development and operation of applications with high dependability requirements deployed on edge network infrastructure. Moreover, we expect that this research program will contribute to the graduation of three PhD and three Master students, who will build a strong expertise in the fields of model-driven software engineering, machine learning, cloud-native microservice-based applications design and operation, edge computing and 5G network technologies.
