  While mobile edge computing (MEC) alleviates the computation and power
limitations of mobile devices, additional latency is incurred when offloading
tasks to remote MEC servers. In this work, the power-delay tradeoff in the
context of task offloading is studied in a multi-user MEC scenario. In contrast
with current system designs relying on average metrics (e.g., the average queue
length and average latency), a novel network design is proposed in which
latency and reliability constraints are taken into account. This is done by
imposing a probabilistic constraint on users' task queue lengths and invoking
results from extreme value theory to characterize the occurrence of
low-probability events in terms of queue length (or queuing delay) violation.
The problem is formulated as a computation and transmit power minimization
subject to latency and reliability constraints, and solved using tools from
Lyapunov stochastic optimization. Simulation results demonstrate the
effectiveness of the proposed approach, while examining the power-delay
tradeoff and required computational resources for various computation
intensities.
