Description
Existing radio access networks (RANs) allow only for very limited sharing of thecommunication and computation resources among wireless operators and heterogeneous wireless technologies. The introduced LayBack architecture facilitates communication and computation resource sharing among different wireless operators and technologies. LayBack organizes the RAN

Existing radio access networks (RANs) allow only for very limited sharing of thecommunication and computation resources among wireless operators and heterogeneous wireless technologies. The introduced LayBack architecture facilitates communication and computation resource sharing among different wireless operators and technologies. LayBack organizes the RAN communication and multiaccess edge computing (MEC) resources into layers, including a devices layer, a radio node (enhanced Node B and access point) layer, and a gateway layer. The layback optimization study addresses the problem of how a central SDN orchestrator can flexibly share the total backhaul capacity of the various wireless operators among their gateways and radio nodes (e.g., LTE enhanced Node Bs or Wi-Fi access points). In order to facilitate flexible network service virtualization and migration, network functions (NFs) are increasingly executed by software modules as so-called "softwarized NFs" on General-Purpose Computing (GPC) platforms and infrastructures. GPC platforms are not specifically designed to efficiently execute NFs with their typically intense Input/Output (I/O) demands. Recently, numerous hardware-based accelerations have been developed to augment GPC platforms and infrastructures, e.g., the central processing unit (CPU) and memory, to efficiently execute NFs. The computing capabilities of client devices are continuously increasing; at the same time, demands for ultra-low latency (ULL) services are increasing. These ULL services can be provided by migrating some micro-service container computations from the cloud and multi-access edge computing (MEC) to the client devices.
Reuse Permissions
  • Downloads
    pdf (8.1 MB)

    Details

    Title
    • SDN based Layered Backhaul Optimization and Hardware Acceleration
    Contributors
    Date Created
    2022
    Resource Type
  • Text
  • Collections this item is in
    Note
    • Partial requirement for: Ph.D., Arizona State University, 2022
    • Field of study: Computer Engineering

    Machine-readable links