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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20211207T055403Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20211116T083000
DTEND;TZID=America/Chicago:20211116T170000
UID:submissions.supercomputing.org_SC21_sess278_rpost148@linklings.com
SUMMARY:Learning-Based Content Delivery in 5G-Enabled Multi-Access Edge Co
 mputing
DESCRIPTION:Posters, Research Posters\n\nLearning-Based Content Delivery i
 n 5G-Enabled Multi-Access Edge Computing\n\nFarhangi Maleki, Ma, Mashayekh
 y, La Roche\n\nThe demand for content such as multimedia services with hig
 h performance (e.g., ultra-low latency) requirements has increased signifi
 cantly, posing heavy backhaul congestion in mobile networks. The integrati
 on of multi-access edge computing (MEC) and 5G network is an emerging solu
 tion that alleviates the backhaul congestion to meet the required network 
 performance for user equipment (UE). Uncertainties due to user mobility, h
 owever, cause the most challenging barrier in deciding optimal content rou
 tes from edge application servers (EASs) to UEs, defined as the 5G compone
 nt selection problem. To this aim, we propose a novel learning-based compo
 nent selection solution for high-performance content delivery in 5G-enable
 d MEC that leads to minimum latency by reducing frequent handovers.\n\nReg
 istration Category: Tech Program Reg Pass, Exhibit Hall Only
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