Resource Optimization in MEC-based B5G Networks for Indoor Robotics Environment

The deployment of fifth-generation (5G) and beyond 5G (B5G) networks is the ambitious objective of modern research on future mobile networks that are evolving to support computation-intensive and communication-sensitive applications. Such applications (e.g., autonomous vehicles, industrial automation, and remote surgery) impose diverse quality-of-service (QoS) requirements on the network in terms of processing, latency, reliability, and bandwidth, and will require ultra-reliable low-latency communication (URLLC), paving the way for multi-access edge computing (MEC). Our work considers a dynamic indoor B5G network in a robotic scenario where agents continuously need MEC services and migrate from one cell to another to perform their tasks in an ultra-dense cell environment. Assuming that every MEC service is a virtual machine (VM) to execute in one of the cells with the possibility of migrating the VM to another cell by paying some cost, we formalize the joint problem of (1) placing/migrating the VMs to respect their end-to-end communication latency requirements and (2) allocating their computation and communication bandwidth as a mixed-integer linear program (MILP). An MILP solver is then used to find the optimal VM placements/migrations and bandwidth allocations over a time horizon.