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DTSTAMP:20220306T211321Z
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DESCRIPTION:In the 5G era\, wireless networks are anticipated to provide co
 nnectivity for massive mobile devices and to enable a variety of innovativ
 e applications\, which generate enormous computing service demands with di
 verse and stringent Quality of Service requirements. To support the emergi
 ng computing service demands\, Mobile Edge Computing (MEC)\, as a cutting-
 edge technology in 5G\, utilizes computing resources on the network edge t
 o provide computing services for mobile devices within a radio access netw
 ork. In this talk\, we will investigate computing resource management for 
 MEC to satisfy diverse computing requirements in wireless networks. We wil
 l introduce three computation offloading and task scheduling schemes tailo
 red for supporting representative use cases and network scenarios in 5G\, 
 including autonomous driving\, Unmanned Aerial Vehicle (UAV) assisted netw
 orks\, and highly dense vehicular networks. Machine learning algorithms ar
 e applied to facilitate low-latency and reliable computing services in com
 plex and dynamic network environments.\n\nSpeaker(s): Mushu Li\, \n\nVirtu
 al: https://events.vtools.ieee.org/m/305769
LOCATION:Virtual: https://events.vtools.ieee.org/m/305769
ORGANIZER:l5zhao@ryerson.ca
SEQUENCE:8
SUMMARY:Computation Offloading and Task Scheduling at Network Edge
URL;VALUE=URI:https://events.vtools.ieee.org/m/305769
X-ALT-DESC:Description: <br /><p>In the 5G era\, wireless networks are anti
 cipated to provide connectivity for massive mobile devices and to enable a
  variety of innovative applications\, which generate enormous computing se
 rvice demands with diverse and stringent Quality of Service requirements. 
 To support the emerging computing service demands\, Mobile Edge Computing 
 (MEC)\, as a cutting-edge technology in 5G\, utilizes computing resources 
 on the network edge to provide computing services for mobile devices withi
 n a radio access network. In this talk\, we will investigate computing res
 ource management for MEC to satisfy diverse computing requirements in wire
 less networks. We will introduce three computation offloading and task sch
 eduling schemes tailored for supporting representative use cases and netwo
 rk scenarios in 5G\, including autonomous driving\, Unmanned Aerial Vehicl
 e (UAV) assisted networks\, and highly dense vehicular networks. Machine l
 earning algorithms are applied to facilitate low-latency and reliable comp
 uting services in complex and dynamic network environments.</p>
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