Projects

Sustainable digitalisation

This research aims to support sustainable digital transformation and Green technology development.

Grant, NSERC CRD grant (2014-2017)

Principal Investigator: Abdelouahed Gherbi

Abstract:

Cloud computing is a computing paradigm that enables an on-demand and scalable provision of computing resources. Cloud computing is increasingly gaining industrial acceptance due to its flexibility, efficiency and ability to reduce OPEX (operational expenses) and CAPEX (capital expenses) for Cloud users. Although Cloud computing provides an efficient solution for hosting non-critical IT services, it is still limited in terms of dependability, including high availability (HA), in hosting telecom and critical real-time applications. From the perspective of major Cloud providers, such as Amazon, the focus is to ensure the availability of the virtual resources that they offer as a service. From the perspective of Cloud tenants, the main concern is the HA of their applications deployed in the Cloud. The first objective of this research project is to define a HA solution that bridges the gap between the aspirations of Cloud users and the offerings of Cloud providers. In order to achieve this objective in the project, we define a novel agent-based software architecture, which allows for scaling up of the HA solution to meet the stringent requirements in Cloud computing. Moreover, within the context of Cloud computing, high availability imposes certain requirements that conflict with Green computing, which targets efficient resource utilization and reduction of the environmental footprint of the Cloud. In particular, HA favors redundancy and the use of standby entities to eliminate single failure points, whereas Green computing encourages the consolidation of resources to maximize utilization. This introduces a multi-dimensional optimization problem where we look for a trade-off between HA and the Green requirements. The second objective of this research project is to incorporate Green-awareness into the HA solutions that are defined in order to achieve the first objective. We will achieve this objective using a meta-heuristic approach that allows for optimal placement and live migration of VMs from the perspective of both HA and Green requirements. We expect the results of this project to have a significant impact on the development and management of HA software applications deployed in the Cloud and to meet Green requirements.

Mitacs Clusters (Ericsson Canada, 2019-2021)

Principal Investigator: Chamseddine Talhi

Abstract:

This project is part of the Ericsson’s GAIA (Global Artificial Intelligence Accelerator), focusing on managing the lifecycle of machine learning solutions for monitoring a virtualized infrastructure.

Smart transportation

This research aims to develop new technologies to support different transportation systems and applications such as intelligent traffic management and V2X communication.

International project (2021-2023)

Principal Investigator: Nadjia Kara

Abstract:

This project focuses on the development and evaluation of an EDGE computing platform for Unmanned Aerial Vehicles (UAVs), including a set of cutting-edge technologies aimed at creating new IoT business models and delivering a plethora of IoT services (e.g.: wildlife monitoring). UAVs can be seamlessly integrated into these types of advanced architectures and provide IoT-driven applications anytime and anywhere. In fact, they can be deployed in a variety of environments (e.g.: urban or wildlife surveillance/sensing environments) to collect and disseminate huge volumes of data (e.g.: UAVs with computer vision and infrared thermography). In such dynamic environments, data should be collected, processed and disseminated in an efficient and timely manner. Within this context, one of the main challenges will be to ensure reliable and secure data collection and dissemination while meeting the UAV-based application performance requirements (e.g.: latency highly sensitive, availability highly sensitive applications). Therefore, the key focus of this project is to develop, demonstrate and evaluate a novel 5G EDGE computing architecture for efficient and dynamic deployment and management of UAV-based applications.

Communication networks

This research focusses on the development of novel technologies (including methodologies, protocols, algorithms) to support efficient and effective management and deployment of various communication ecosystems (e.g., Edge computing, Cloud computing, IoT, 5G/B5G).

Mitacs Clusters (Ericsson Canada, 2019-2021)

Principal Investigator: Chamseddine Talhi

Abstract:

The goal of this project is to develop solutions for monitoring and orchestrating virtual network functions (VNF). It is part of a light virtualization development initiative identified today by Cloud native.

Prompt (Ericsson Canada, Rogers communication Inc., 2018-2020)

Principal investigator: Nadjia Kara

Abstract:

The main objective of this project is to develop a platform for dynamic resource management for virtualized network functions (VNF). This platform integrates a set of mechanisms, such as dynamic adaptation of resources and dynamic service chain placement, along with workload modeling and prediction. It allows for automated resource management under variable workload conditions and timescales while meeting the performance requirements of different VNF (e.g.: reliability and latency requirements).

NSERC CRD (Ericsson Canada, Rogers communication Inc., 2016-2020)

Principal investigator: Nadjia Kara

Abstract:

The main objective of this project is to define and validate new mechanisms (e.g.: models, algorithms and methodologies) to support dynamic resource management for virtualized network functions through efficient and fine-grained resource provisioning while satisfying Service Level Agreements (SLAs). These approaches will react to various dynamic virtualized network functions and enable efficient and flexible resource management. The proposed research activities will increase flexibility for network and service management, and will help to reduce physical infrastructure cost, energy consumption and time to market for the deployment of new applications.

NSERC Alliance project

Principal investigator: Nadjia Kara

Abstract:

This project will enable the development of new technologies for dynamic and joint optimization of performances and energy consumption in serverless edge computing platforms under time-varying workflows, workloads and resource consumption while meeting application deployment and management constraints (e.g.: configurations of resources, workflow structures). Moreover, these technologies will allow for resources and their configuration runtimes to be adapted to optimize resource and energy costs while meeting the performance requirements of running applications. The developed technologies will stimulate rapid Function as a Service (FaaS) edge infrastructure evolution and adoption, and will speed up the time to market for new serverless applications. They will help FaaS edge computing providers to reduce OPEX, because they will be able to minimize FaaS edge computing infrastructure deployment and operational costs (minimize resource and energy consumption, automatic resource deployment and adaptation). Moreover, they will provide green and reliable FaaS edge computing infrastructure and deliver new serverless applications to customers in a sustainable manner.

NSERC Discovery grant (2016-2023)

Principal investigator: Nadjia Kara

Abstract:

This research program addresses very challenging issues related to resource provisioning and management in distributed virtualized communication environments. New mechanisms and tools will be developed to enable the provisioning and management of flexible and efficient networks and services within these environments. New mathematical models and algorithms will be developed with a view to satisfying multiple objectives of virtualized communication environments. New mechanisms will also be developed to integrate configuration, provisioning and optimization of resources through the federation of virtualized communication environments. This research program will provide novel approaches that ease the migration toward open and interoperable communication environments while satisfying both providers and customers.

NSERC Discovery grant (May 2019 – April 2024)

Principal investigator: Aris Leivadeas 

Abstract:

In recent decades, the Internet has been a key factor in building the global information society and leading economic growth. The mix of technologies in the Internet, including Cloud Computing, Network Function Virtualization (NFV), Data Analytics and the Internet of Things (IoT), provides the necessary tools to build a service-centric Future Internet Architecture. Future Internet is expected to rely on virtualization technologies, moving away from dedicated and expensive hardware middleboxes, while IoT will be one of the main sources of data generation in this communication paradigm. In light of this, the long-term goal of this proposal is to establish a scalable softwarized end-to-end solution that will gracefully combine varied technologies to provide networking services with high Quality of Service (QoS) and low communication costs.

AI technologies

This research focusses on the development of artificial intelligence (AI) technologies for different modern systems (e.g., critical mobility and transportation systems) and the analysis of their impact.

Mitacs Cluster (Iptoki, 2020-2022)

Principal Investigator: Chamseddine Talhi

Abstract:

The goal of this project is to use ML (machine learning) techniques and Blockchain technology for the continuous and transparent authentication of users based on behaviour and procedure recognition models, among other factors.

Mitacs Clusters (StreamScan, 2020-2022)

Principal Investigator: Chamseddine Talhi

Abstract:

The goal of this project is to improve competitiveness within the StreamScan market by developing artificial intelligence solutions leading to the efficient detection of anomalies and the automatic generation of attack and malware signatures.

System engineering

This research focusses on the development of novel technologies (including methodologies, protocols, algorithms) to support efficient and effective management and deployment of various communication ecosystems (e.g., Edge computing, Cloud computing, IoT, 5G/B5G).

NSERC Discovery grant (May 2017 – April 2021)

Principal Investigator: Chamseddine Talhi

Abstract:

The goal of this project is to develop new mobile and IoT equipment authentication solutions in new-generation mobile networks.

NSERC CRD grant (ACME Engineering Prod. Ltd, 2019-2022)

Principal Investigator: Abdelouahed Gherbi

Abstract:

The Internet of Things (IoT) vision is now a firmly established reality, considering the variety of applications and provided services, including Smart cities, Smart grids and Healthcare. According to recent studies, the IoT sector is growing exponentially, which is motivating numerous industrial and business entities in Canada and worldwide, including the industrial partners of this project, to adopt IoT initiatives. IoT systems are often the backbone of various safety-critical applications, such as infrastructure monitoring, smart grid or smart healthcare, which rely on the performance, reliability and predictability of these systems. The failure of these types of IoT systems may have severe consequences. Therefore, the dependability of these systems is a critical and challenging issue. Moreover, major IoT solution providers, including the industrial partners of this project, need to support large-scale deployments of their IoT systems to a growing number of clients with a diversity of system-functional and quality-of-service requirements. The main objective of this research project is to investigate, develop and validate new engineering techniques to enable the automatic deployment and operation of smart large-scale dependable IoT systems.

NSERC discovery grant (2017-2022)

Principal Investigator: Abdelouahed Gherbi

Abstract:

Software systems are now the backbone of diverse activities within our modern society, including transportation, communication, health and entertainment. In addition to their inherent complexity, these systems are increasingly deployed on highly dynamic runtime platforms (e.g. Cloud Computing), and they interface with an uncertain environment (e.g. Cyber-Physical Systems). Within this context, software systems should be designed with an adaptation capacity and should continuously verify stringent dependability requirements. Model-driven software engineering (MDE) is now a well-established approach that focuses on design-time modeling to support coping with software system complexity. With the emergence of the models at runtime concept, MDE techniques can be extended to support the engineering of adaptive software systems. However, there are still a number of challenging issues related to using these runtime models to support and manage the dependability of adaptive software systems. First, we point to the lack of specific framework to support runtime dependability modeling, in contrast to the numerous standard frameworks for design-time modeling. Having the framework to build such models, there is a need for efficient analysis techniques that allow for using dependability runtime models to generate optimal adaptations. Third, the dependability requirement is a multidimensional concept that encompasses numerous dimensions, including availability, reliability, safety and performance, to mention just a few. These are inter-dependent, inter-related and potentially conflicting dimensions. As a consequence, keeping the dependability runtime model consistent on one hand and also continuously synchronized with the running system throughout the system adaptions on the other hand is challenging. In this research program, we propose leveraging the model-driven software engineering approach combined with the models at runtime concept in order to define, develop and validate modeling techniques to support the development and management of dependable adaptive software systems.

Other achievements

  1. Method for VNF Managers Placement in Large-Scale and Distributed NFV Systems, Patent number: 11032135, Patent status: Granted/Issued, Year Issued 2021, Inventors: Mohammad Abu LebdehDiala NaboulsiRoch GlithoConstant Wette Tchouati.
  2. METHOD, APPARATUS AND SYSTEM FOR HIGH PERFORMANCE PERIPHERAL COMPONENT INTERCONNECT DEVICE RESOURCE SHARING IN CLOUD ENVIRONMENTS. United States. PCT/1B201 9/054737. 2019/06/06. Patent Status: Pending, Inventors: Xu Liu, Hibat-Allah Ounifi, Abdelouahed Gherbi, Wubin LI
  3. Datafall: A Policy-Driven Algorithm for Decentralized Placement and Reorganization of Replicated Data. United States. WO2019122971A1. 2017/12/20. Patent Status: Granted/Issued Year Issued: 2019, Inventors: Fereydoun FARRAHI MOGHADDAM, Reza FARRAHI MOGHADDAM, Wubin Li, Abdelouahed Gherbi.
  4. Pourzandi, M., Ahmed, M. F., Cheriet, M., & Talhi, C. (2018). Multi-tenant isolation in a Cloud Environment Using Software Defined Networking. S. Patent No. 9,912,582. Washington, DC: U.S. Patent and Trademark Office.
  5. Method and System for Deploying a New Service Function Chain Based on Similarities with Previously Deployed SFCs. Canada. W02020255025. 2020/12/08. Patent Status: Granted/Issued Year Issued: 2019 Inventors: Hanan Suwi*, Nadjia Kara and Claes Edstrom.
  6. Machine Learning Method for Adaptive Network Functions Placement and Readjustment. Canada. W02020026140. 2020/02/10. Patent Status: Granted/Issued Year Issued: 2019, Inventors: Omar Abdul Wahab*, Nadjia Kara, Claes Edstrom and Yves Lemieux.
  7. Joint Placement and Chaining of VNFs for Virtualized Systems Based on Scalable Genetic Algorithm. Canada. W02020026142. 2020/02/10. Patent Status: Granted/Issued Year Issued: 2019, Inventors: Laaziz Lahlou*, Nadjia Kara, Claes Edstrom and Yves Lemieux.
  8. Unmanned Aerial Vehicle Deployment Method and System. Canada. United States Patent and Trademark Office (USPTO). 2019/11/22. Patent Status: Pending Year Issued: 2019, Inventors: Houssem Eddine Mohamadi*, Nadjia Kara and Mohand Lagha.
  9. MACHLAREN - unsupervised MACHine Learning techniques for mAnagement of REsources in Virtualized and non-virtualized iNfrastructures. Canada. P79104. 2019/10/08. Patent Status: Pending Year Issued: 2019, Inventors: Laaziz Lahlou*, Imane El Mensoum*, Fawaz Khasawneh*, Nadjia Kara and Claes Edstrom.
  10. Workload Modelling for Cloud Systems. Canada. W02019162859. 2019/08/15. Patent Status: Granted/Issued, Year Issued: 2019, Inventors: Cédric St-Onge* and Nadjia Kara
  11. Resource Needs Prediction in Virtualized Systems: Generic Proactive and Self-Adaptive Solution. Canada. W02019150343. 2019/08/12. Patent Status: Granted/Issued Year Issued: 2019, Inventors: Souhila Benmakrelouf* and Nadjia Kara.
  12. Semantic Detection and Resolution of Conflicts and Redundancies in Network Function Virtualization Policies. Canada. W02020183228. 2019/03/13. Patent Status: Granted/Issued Year Issued: 2018, Inventors: Hanan Suwi*, Nadjia Kara and Claes Edstrom
  13. Generic Workload Periodicity Detection Algorithm for Virtualized Systems. United States. P74351, United States Patent and Trademark Office (USPTO). 2018/04/01. Patent Status: Pending Year Issued: 2018, Inventors: Cédric St-Onge* and Nadjia Kara.
The Information and Communications Technologies (ICT) Sector in Québec
More than 60 students per year
Nearly 150 Master and Ph.D. students graduated over the previous 5 years
More than 30 projects every year