[ecoop-info] Post-doctoral position at Inria on "Application model expressiveness and placement computing complexity in a Fog/Edge context"

[Christian Perez]

A 12 month post-doctoral position is available at Lyon (France) in the context of the Discovery project (http://beyondtheclouds.github.io/).

For more information and to apply, please visit https://jobs.inria.fr/public/classic/en/offres/2018-00801

----

Application model expressiveness and placement computing complexity in a Fog/Edge context

The Post-doctoral researcher will be integrated into the Avalon research team localized in Lyon (LIP,
ENS Lyon), and will collaborated with the Corse research team localized in Grenoble. S/He will be co-
supervised by Christian Perez (Avalon) and Frédéric Desprez (Corse).
This position is within the context of the IPL Discovery.

This post-doctoral position aims at making progress at the interface of two research directions being
done within the Discovery initiative, i.e. component model for describing, deploying, and
reconfiguring applications on Fog/Edge Computing infrastructures and algorithms to compute an
actual placement or reconfiguration. A first objective is to study the relationships between the
features of the component models (and thus their expressiveness) and the complexity of computing
a placement (in particular based on existing solutions). A second objective is to participate to the
building and evaluation of use cases based on the acquired expertise.

The widespread of on-demand resources, first with Clouds, and now with Fog /Edge computing
platforms, has reinforced the need of automatically provisioning and deploying applications on
distributed infrastructures. On one hand, several works have focused on defining models to let a user
describe the application to be deployed. Most of these works are based on component models such as
TOSCA [1], CAMEL [2], AEOLUS [3], HLCM [4], etc. A particular point of variation of these models is the
level of expressiveness such as the management of component cardinality, the complexity of
connectors, their support of generic programming, etc. This variability generates a large variation in
the complexity of induced placement problem. On the other hand, a lot of work have dealt with the
computing of a solution to the placement (and/or reconfiguration) of an application on a set of
resources. Existing solutions consists of constraint based solvers, meta-heuristics, and heuristics.
These solutions also present a lot of variability in their expressiveness of the application model to be
deployed, the resource model, the type of constraints, and of course their scalability in term of
application or resource elements.

The work will be structured around two main actions:
- Study the relationships between the features of component models (and thus their
expressiveness) and the complexity of computing a placement.
-
Participate to the building and evaluation of use cases based on the acquired expertise

Skills
- Knowledge of scheduling/mapping algorithms.
- Strong programming skills (Python and C++ knowledge will be definitely an advantage)
- Experimentation skills (simulations and experiments such as with the Grid’5000 instrument)
- Knowledge of Cloud environments and networking.
- Knowledge of programming abstractions (component model / architecture description language) will be a plus
- Autonomy / Curiosity
- English language mandatory

References
[1] Topology and Orchestration Specification for Cloud Applications Version 1.0. 25 November 2013.
OASIS Standard. http://docs.oasis-open.org/tosca/TOSCA/v1.0/os/TOSCA-v1.0-os.html
[2] The Cloud Application Modelling and Execution Language (CAMEL). Rossini, Alessandro & Kritikos,
Kiriakos & Nikolov, Nikolay & Domaschka, Jörg & Griesinger, Frank & Seybold, Daniel & Romero, Daniel
& Orzechowski, Michal & Kapitsaki, Georgia & Achilleos, Achilleas. (2017). 10.18725/OPARU-4339.
[3] Aeolus: A component model for the cloud. Roberto Di Cosmo, Jacopo Mauro, Stefano Zacchiroli,
Gianluigi Zavattaro, Information and Computation, Volume 239, 2014, Pages 100-121, ISSN 0890-5401,
https://doi.org/10.1016/j.ic.2014.11.002.
[4] High Performance Computing: From Grids and Clouds to Exascale. Julien Bigot, and Christian Pérez.,
chap. On High Performance Composition Operators in Component Models. – Advances in Parallel
Computing, vol 20, pp 182-201, IOS Press, 2011.