  As mobile edge computing (MEC) finds widespread use for relieving the
computational burden of compute- and interaction-intensive applications on end
user devices, understanding the resulting delay and cost performance is drawing
significant attention. While most existing works focus on singletask offloading
in single-hop MEC networks, next generation applications (e.g., industrial
automation, augmented/virtual reality) require advance models and algorithms
for dynamic configuration of multi-task services over multi-hop MEC networks.
In this work, we leverage recent advances in dynamic cloud network control to
provide a comprehensive study of the performance of multi-hop MEC networks,
addressing the key problems of multi-task offloading, timely packet scheduling,
and joint computation and communication resource allocation. We present a fully
distributed algorithm based on Lyapunov control theory that achieves
throughput-optimal performance with delay and cost guarantees. Simulation
results validate our theoretical analysis and provide insightful guidelines on
the interplay between communication and computation resources in MEC networks.
