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UID:562@lincs.fr
DTSTART;TZID=Europe/Paris:20201021T140000
DTEND;TZID=Europe/Paris:20201021T150000
DTSTAMP:20201023T092127Z
URL:https://www.lincs.fr/events/seminar-presentation-by-a-new-lincs-associ
 ate-member-3/
SUMMARY:Seminar Presentation by a new LINCS associate member "Multi-Tenant
 Resource Allocation at the Network Edge"
DESCRIPTION:Edge Computing (EC) is a network paradigm in which
 computational resources\, e.g.\, CPU\, memory\, are distributed in the
 access networks\, close to the final users. Network elements at the edge\,
 e.g.\, base stations\, road side units\, access points\, become intelligent
 and can be used by multiple third party Service Providers (SPs) to run (a
 part of) their application at the edge. Such applications can be
 heterogeneous\, ranging from video-streaming to augmented reality\, to
 services needed to autonomous driving.\n\nIn order to enable this\, the
 Network Operator (NO)\, owning the physical infrastructure\, needs to
 virtualize edge resources and allocate them to the SPs\, which represent
 the tenants.\n\nUnlike Cloud Computing\, EC resources are scarce and
 contention arises between SPs. Therefore\, the NO needs to carefully
 allocate virtual resources among them\, in order to increase its utility\,
 e.g.\, reduction in bandwidth consumption\, user QoE. Such allocation is
 challenging\, since SPs must be kept isolated\, i.e.\, data and computation
 of each SP must be inaccessible to other SPs and to the NO. In other
 words\, SPs are black boxes for the NO\, which still must be able to
 properly allocate resources among them.\n\nA. Araldo will present some
 solutions to the problem of Multi-Tenant Resource Allocation in EC\,
 described above\, based on two approaches: explicit information exchange
 between SPs and NO or data-driven allocation algorithms\, as reinforcement
 learning and stochastic perturbation.\n\nFinally\, A. Araldo will present
 some preliminary results related to the performance of large scale
 augmented reality applications implemented via a distributed system of Edge
 AI boards\, which are small and cheap embedded devices equipped with GPU\,
 able to run deep neural networks.
CATEGORIES:Seminars,Youtube
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TZID:Europe/Paris
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