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UID:800@lincs.fr
DTSTART;TZID=Europe/Paris:20231130T140000
DTEND;TZID=Europe/Paris:20231130T150000
DTSTAMP:20231211T114348Z
URL:https://www.lincs.fr/events/phd-thesis-defense-artificial-intelligence
 -for-resource-allocation-in-multi-tenant-edge-computing/
SUMMARY:Phd Thesis Defense : Artificial Intelligence for Resource
 Allocation in Multi-Tenant Edge Computing
DESCRIPTION:\n\n\n\n\nWe consider in this thesis Edge Computing (EC) as a
 multi-tenant environment  where Net- work Operators (NOs) own edge
 resources deployed in base stations\, central oces and/or smart boxes\,
 virtualize them and let third party Service Providers (SPs) - or tenants -
 dis- tribute part of their applications in the edge in order to serve the
 requests sent by the users. SPs with heterogeneous requirements coexist in
 the edge\, ranging from Ultra-Reliable Low Latency Communications (URLLC)
 for controlling cars or robots\, to massive Machine Type Communication
 (mMTC) for Internet of Things (IoT) requiring a massive number of connected
 devices\, to media services\, such as video streaming and Augmented/Virtual
 Reality (AR/VR)\, whose quality of experience is strongly dependant on the
 available re- sources. SPs independently orchestrate their set of
 microservices\, running on containers\, which can be easily replicated\,
 migrated or stopped. Each SP can adapt to the resources allocated by the
 NO\, deciding whether to run microservices in the devices\, in the edge
 nodes or in the cloud. We aim in this thesis to advance the emergence of
 real deployments of the “true” EC in real networks\, by showing the
 utility that NOs can collect thanks to EC. We believe that this can
 contribute to encourage concrete engagement and investments engagement of
 NOs in EC. For this\, we point to design novel data-driven strategies that
 efficiently allocate resources between heterogeneous SPs\, at the edge
 owned by the NO\, in order to optimize its relevant objectives\, e.g.\,
 cost reduction\, revenue maximization and better Quality of Service (QoS)
 perceived by end users\, in terms of latency\, reliability and throughput\,
 while satisfying the SPs requirements.\n\nThis thesis presents a
 perspective on how NOs\, the sole owners of resources at the far edge
 (e.g.\, at base stations)\, can extract value through the implementation of
 EC within a multi-tenant environment. By promoting this vision of EC and by
 supporting it via quantitative results and analysis\, this thesis
 provides\, mainly to NOs\, findings that can influence decision strategies
 about the future deployment of EC. This might foster the emergence of novel
 low-latency and data-intensive applications\, such as high resolution
 augmented reality\, which are not feasible in the current Cloud Computing
 (CC) setting.\n\nAnother contribution of the thesis it that it provides
 solutions based on novel methods that harness the power of data-driven
 optimization. We indeed adapt cutting-edge tech- niques from Reinforcement
 Learning (RL) and sequential decision making to the practical problem of
 resource allocation in EC. In doing so\, we succeed in reducing the
 learning time of the adopted strategies up to scales that are compatible
 with the EC dynamics\, via careful design of estimation models embedded in
 the learning process. Our strategies are conceived in order not to violate
 the confidentiality guarantees that are essential for SPs to accept running
 their computation at the EC\, thanks to the multi-tenant setting.\n\n\n\n\n
CATEGORIES:PhD Defense,Youtube
LOCATION:Amphi 2\, 19 place Marguerite Perey\, Palaiseau\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=19 place Marguerite Perey\,
 Palaiseau\, France;X-APPLE-RADIUS=100;X-TITLE=Amphi 2:geo:0,0
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TZID:Europe/Paris
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DTSTART:20231029T020000
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TZOFFSETTO:+0100
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