Using machine learning for dynamic resource orchestration & task scheduling, in a radio access network based edge environment

Thesis

The evolution in mobile wireless communication generations, continues to gift the world with new telecommunication capabilities towards how people live and work. For instance, the roll out of the fifth generation (5G) promises a much-enhanced cloud-native application service, through improved communication speed, higher bandwidth, improved capacity for more connected devices and many more. These exciting new 5G features have given numerous vertical and horizontal industries a reason to explor...

Expand abstract

Authors: Fletcher, JKA

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: ceph; artificial intelligence; containers; task scheduling; mobile edge computing; cloud computing; energy reduction; federated cloud; mMTC; URLLC; 5G; machine learning; cloud-based/ Centralized Radio Access Network (C-RAN); multi-access edge computing; eMBB; MEC orchestration
• Subjects: Cloud computing; Machine learning; Task Scheduling; Edge computing; 5G mobile communication systems