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 explore different ways of delivering value, through emerging cloud-native applications that run on access devices (i.e. the Internet of Things (IoTs) and mobile devices). These cloud-native applications such Virtual Reality, Vehicle to everything communication (V2X), artificial intelligence, video analytics and so on however have strict performance requirements for extremely low latency (10 milliseconds and below) and higher bandwidth, that 5G alone cannot deliver. Unfortunately, the traditional cloud computing and radio access network setups do not sufficiently address these key communication needs which are crucial to the performance of these cloud-native applications. There is therefore an urgency to enhance and optimize the traditional mobile telecommunication network architecture, to meet these performance needs. This thesis seeks to demonstrate how we have developed a facility called Multi-Access Edge Computing with Cloud Radio Access Networks (MECRAN) to address this issue. MECRAN is based on the edge computing paradigm and uses machine learning to optimize the round-trip delivery of data at low latency, by dynamically scheduling cloud-native applications, to run in close proximity to a mobile user, at the edge of the radio access network.

Authors: Fletcher, JKA

• DOI: 10.5287/ora-gwr1mn24e
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: energy reduction; ceph; containers; task scheduling; machine learning; artificial intelligence; mobile edge computing; URLLC; federated cloud; cloud computing; eMBB; multi-access edge computing; cloud-based/ Centralized Radio Access Network (C-RAN); MEC orchestration; 5G; mMTC
• Subjects: Cloud computing; Edge computing; Task Scheduling; Machine learning; 5G mobile communication systems