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UID:695@lincs.fr
DTSTART;TZID=Europe/Paris:20220420T150000
DTEND;TZID=Europe/Paris:20220420T160000
DTSTAMP:20220421T073241Z
URL:https://www.lincs.fr/events/cache-allocation-in-multi-tenant-edge-comp
 uting-via-online-reinforcement-learning/
SUMMARY:Cache Allocation in Multi-Tenant Edge Computing via online
 Reinforcement Learning
DESCRIPTION:\nWe consider in this work Edge Computing (EC) in a
 multi-tenant environment: the resource owner\, i.e.\, the Network Operator
 (NO)\, virtualizes the resources and lets third party Service Providers
 (SPs - tenants) run their services\, which can be diverse and with
 heterogeneous requirements. Due to confidentiality guarantees\, the NO
 cannot observe the nature of the traffic of SPs\, which is encrypted. This
 makes resource allocation decisions challenging\, since they must be taken
 based solely on observed monitoring information.\nWe focus on one specific
 resource\, i.e.\, cache space\, deployed in some edge node\, e.g.\, a base
 station. We study the decision of the NO about how to partition cache among
 several SPs in order to minimize the upstream traffic. Our goal is to
 optimize cache allocation using purely data-driven\, model-free
 Reinforcement Learning (RL). Differently from most applications of RL\, in
 which the decision policy is learned offline on a simulator\, we assume no
 previous knowledge is available to build such a simulator. We thus apply RL
 in an online fashion\, i.e.\, the policy is learned by directly perturbing
 the actual system and monitoring how its performance changes. Since
 perturbations generate spurious traffic\, we also limit them. We show in
 simulation that our method rapidly converges toward the theoretical
 optimum\, we study its fairness\, its sensitivity to several scenario
 characteristics and compare it with a method from the state-of-the-art.\n
CATEGORIES:Seminars,Youtube
LOCATION:LINCS + Zoom\, 23 avenue d'Italie\, Paris\, 75013\, France
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 Paris\, 75013\, France;X-APPLE-RADIUS=100;X-TITLE=LINCS + Zoom:geo:0,0
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