  Mobile edge computing (MEC) has been regarded as a promising approach to deal
with explosive computation requirements by enabling cloud computing
capabilities at the edge of networks. Existing models of MEC impose some strong
assumptions on the known processing cycles and unintermittent communications.
However, practical MEC systems are constrained by various uncertainties and
intermittent communications, rendering these assumptions impractical. In view
of this, we investigate how to schedule task offloading in MEC systems with
uncertainties. First, we derive a closed-form expression of the average
offloading success probability in a device-to-device (D2D) assisted MEC system
with uncertain computation processing cycles and intermittent communications.
Then, we formulate a task offloading maximization problem (TOMP), and prove
that the problem is NP-hard. For problem solving, if the problem instance
exhibits a symmetric structure, we propose a task scheduling algorithm based on
dynamic programming (TSDP). By solving this problem instance, we derive a bound
to benchmark sub-optimal algorithm. For general scenarios, by reformulating the
problem, we propose a repeated matching algorithm (RMA). Finally, in
performance evaluations, we validate the accuracy of the closed-form expression
of the average offloading success probability by Monte Carlo simulations, as
well as the effectiveness of the proposed algorithms.
