  Mobile Edge Computing (MEC) has recently emerged as a promising technology in
the 5G era. It is deemed an effective paradigm to support computation-intensive
and delay critical applications even at energy-constrained and
computation-limited Internet of Things (IoT) devices. To effectively exploit
the performance benefits enabled by MEC, it is imperative to jointly allocate
radio and computational resources by considering non-stationary computation
demands, user mobility, and wireless fading channels. This paper aims to study
the tradeoff between energy efficiency (EE) and service delay for multi-user
multi-server MEC-enabled IoT systems when provisioning offloading services in a
user mobility scenario. Particularly, we formulate a stochastic optimization
problem with the objective of minimizing the long-term average network EE with
the constraints of the task queue stability, peak transmit power, maximum
CPU-cycle frequency, and maximum user number. To tackle the problem, we propose
an online offloading and resource allocation algorithm by transforming the
original problem into several individual subproblems in each time slot based on
Lyapunov optimization theory, which are then solved by convex decomposition and
submodular methods. Theoretical analysis proves that the proposed algorithm can
achieve a $[O(1/V), O(V)]$ tradeoff between EE and service delay. Simulation
results verify the theoretical analysis and demonstrate our proposed algorithm
can offer much better EE-delay performance in task offloading challenges,
compared to several baselines.
