[FILLED] R&D Engineer position: Designing feedback mechanisms for Edge-to-Cloud Applications -
This position has been filled.*
Start: October 2023
Duration: 12 months (CDD)
Supervision: Daniel Balouek-Thomert, Inria, firstname.lastname@example.org and Baptiste Jonglez, Inria, email@example.com
Location: IMT Atlantique, Nantes
Salary: from 2800 € / month gross salary (depending on diploma and experience)
Benefits: social security coverage, 2 days of remote work per week, partial reimbursement of public transport costs, vocational training
To apply, send a CV and a cover letter to firstname.lastname@example.org and email@example.com, we will then schedule an interview.
Computing is shifting from the traditionally centralized cloud to a distributed set of heterogenous resources located between the edge, the cloud and in-between. As computing has moved to this Computing Continuum, the tradeoffs between performance, availability and cost have become increasingly complicated.
Emerging data-driven applications rely on distributed data sources and AI-enabled workflows to provide results and facilitate decision-making. The life-cycle of data-driven analytics application can be simplified as data collection, data filtering, data processing, and data products delivery. These steps need to be tuned at runtime according to the content of data, cost of computations, and urgency of the results. For example, processing all video streams from a geographical region involves a query without the knowledge of the exact number and locations of active cameras. The requested data might be bigger in size or richer in content than expected, and consequently, will trigger a resource selection to find appropriate computational resources across the continuum.
This position aims at proposing feedback mechanisms for data-driven edge-to-cloud applications. The motivating use-case targets distributed analytics that are time-sensitive and error-tolerant. The work can be decomposed into two main objectives
- The first objective is to build and validate a set of models describing the dynamics of the computing continuum. The target is to obtain a predictive engine that could determine the availability and efficiency of the infrastructure under different stressing load scenarios.
- The second objective is to augment the common operations of data-driven analytics (e.g. collection, filtering, processing, delivery) with tunable parameters. This will provide software abstractions that will be able to better orchestrate operations by considering application context and real-time infrastructure metrics.
Through these two objectives, the successful candidate is expected to propose novel approaches for building intelligent services that manage the availability and efficiency of the infrastructure. Building on an analytical model and tunable software abstractions, this project will inform infrastructure managers and application developers with insights on what data and services to run, where to run them, and how to run them across the Computing Continuum.
This work takes place in the ecosystem of the Stack team, which focuses on the management and advanced usage of large-scale IT infrastructure. This work will be validated by performing real-world experiments using EnOSlib, and create repeatable processes and artifacts that will be used to develop urgent analytics on the Grid5000 and SILECS platforms.
The OTPaaS project aims at the massive digitization of companies by offering a Cloud suitable for the digitization of the field that is compatible with Gaia-X and easy to use by companies including SMEs. The consortium brings together national end-users and technology providers from large corporations (Atos/Bull, Schneider Electric, Valeo) and SMEs (Agileo Automation, Mydatamodels, Dupliprint, Solem, Tridimeo, Prosyst, Twinswheel), with strong support from major French research institutes (CEA, Inria, IMT).
In the context of the OTPaaS project, the STACK research team from IMT Atlantique and Inria has several opening research positions for a duration between 18 and 36 months. Our goal: addressing the Cloud to IoT continuum for the industry sector through a dedicated software stack.
Profile and skills
The R&D Engineer must already hold a master degree or a PhD degree in Computer Science, with a solid background in Software Engineering and Distributed Systems. She/he must also have a (very) good knowledge and interest in Cloud computing, and streaming systems. Knowledge of modeling approaches based on graphs or Petri Nets is an asset. In addition, strong programming skills are highly recommended.
Ideally, she/he must have good oral and written communication skills in English, with the aim to publish and present research results in high-level international journals and conferences. Autonomous, curious and strongly motivated candidates are expected.