Location: LAAS-CNRS - Team SARA or IRIT - Team SEPIA
Supervisors: Da Costa Georges (georges.da-costa@irit.fr) / Guérout Tom (tguerout@laas.fr)
Duration: 6 months, possibility of thesis afterwards.

Context

The explosion of the volume of data exchanged within today’s IT systems, due to an increasingly wide use and by an increasingly wide audience (large organizations, companies, general public etc.), has led for several years to question the architectures used until now. Indeed, for the past few years, Fog computing [1], which extends the Cloud computing paradigm to the edge of the network, has been developing steadily, offering more and more possibilities and thus extending the field of Internet of Things applications. The management of these new architectures, involving a large number of heterogeneous devices and applications, potentially large volumes of data to be processed, has the challenge of proposing innovative and high-performance solutions to improve the stability, fluidity, security and efficiency of the services and applications deployed.

Topic

The objective of this internship is to study a task scheduling solution [2], under energy and Quality of Service (QoS) constraints, integrating a learning phase on the intrinsic characteristics of the tasks to be scheduled, aiming at facilitating the decision making of a meta-scheduler. This first step will lead to an overview of different AI methods for data-mining and/or clustering [3]. The problem addressed through the development of the meta-scheduler, in charge of scheduling each task, will be to evaluate the quality of the obtained solutions according to QoS constraints and objectives. The meta-scheduler will initially be able to embed so-called “greedy” algorithms. Upstream or in parallel, a modeling effort of the studied Fog architecture will be required in order to clearly define the contours of the problem. The tasks will be of the “fast” type, implying a relatively short execution time but heterogeneous characteristics and needs (number of resources, time horizon, priority, or energy budget for example) which will reinforce the constraint of the performance/time ratio of the meta-scheduler and will imply an evaluation of the scalability of the proposed solution. The experimentation phase will be done by simulation. A significant effort to master the SimGrid simulator (https://simgrid.org/) will be required.

Details of the key points

1. State of the art of Fog scheduling researches
2. State of the art and understanding of AI techniques such as “data mining” and “clustering”
3. Fog architecture modeling
4. Definition of QoS constraints and objectives through measurable metrics
5. Experimental campaigns by simulation (simGrid)

References

[1] YI, Shanhe, HAO, Zijiang, QIN, Zhengrui, et al. Fog computing: Platform and applications. In : 2015 Third IEEE workshop on hot topics in web systems and technologies (HotWeb). IEEE, 2015. p. 73-78.
[2] MURTAZA, Faizan, AKHUNZADA, Adnan, UL ISLAM, Saif, et al. QoS-aware service provisioning in fog computing. Journal of Network and Computer Applications, 2020, vol. 165, p. 102674.
[3] VERMA, Manish, SRIVASTAVA, Mauly, CHACK, Neha, et al. A comparative study of various clustering algorithms in data mining. International Journal of Engineering Research and Applications (IJERA), 2012, vol. 2, no 3, p. 1379-1384.