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Distributed troubleshooting of edge-compute functions (with Technicolor)

Equipe et encadrants
Département / Equipe: 
Site Web Equipe: 
Directeur de thèse
François Taïani
Co-directeur(s), co-encadrant(s)
Neumann Christoph
NomAdresse e-mailTéléphone
François Taïani & Neumann Christoph
+33 (0) 2 99 84 75 04
Sujet de thèse

The rise of micro-services, fog-, and edge-computing are prompting a fresh rethink of the typical distribution of capabilities between servers and clients in a distributed applications. This is likely to lead to more services and computations being offloaded to geo-distributed devices, in particular within hybrid cloud/edge architectures.

In this context, this PhD thesis focuses on the opportunities that recent generations of end-user gateways (or more generally end-user devices) will offer to implement an edge-compute paradigm powered by user-side micro-services (as illustrated by Amazon's recent announcement of its Greengrass platform). In order for service and application providers to actually use the system and deploy applications, the system must ensure an appropriate level of reliability, while simultaneously requiring a very low level of maintenance in order to address the typical size and economics of gateway deployments (at least a few tens of million units).

The PhD thesis aims to build and evaluate models, algorithms and data-structures with a sufficient level of abstraction, in order to deliver results that remain generally applicable beyond any particular use case or application. However, we also plan to implement and evaluate on edge-devices some of the primitives designed within the course of the PhD thesis, in order to validate that the proposed primitives are able to execute in a resource-constrained environment.

More details available here. Send us your application by email.


[1] Valancius, Vytautas, et al. ”Greening the internet with nano data centers.” ACM CoNext 2009.

[2] Ibidunmoye, Olumuyiwa, Francisco Hernndez-Rodriguez, and Erik Elmroth. ”Performance anomaly detection and bottleneck identifica- tion.” ACM Computing Surveys (CSUR) 48.1 (2015): 4.

[3] Ahmed, Tarem, Boris Oreshkin, and Mark Coates. ”Machine learning approaches to network anomaly detection.” USENIX SysML, 2007.

[4] Arzani, Behnaz, et al. ”Taking the blame game out of data centers operations with NetPoirot.” ACM SIGCOMM 2016.

[5] Dimopoulos, Giorgos, et al. ”Identifying the root cause of video streaming issues on mobile devices.” ACM CoNext 2015.

Début des travaux: 
September 2017
Mots clés: 
edge computing, anomaly detection, distributed systems, gateways, microservices
IRISA - Campus universitaire de Beaulieu, Rennes