  Mobile edge computing (MEC) is one of the promising solutions to process
computational-intensive tasks for the emerging time-critical Internet-of-Things
(IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous
vehicle. The latency can be reduced further, when a task is partitioned and
computed by multiple edge servers' (ESs) collaboration. However, the
state-of-the-art work studies the MEC-enabled offloading based on a static
framework, which partitions tasks at either the local user equipment (UE) or
the primary ES. The dynamic selection between the two offloading schemes has
not been well studied yet. In this paper, we investigate a dynamic offloading
framework in a multi-user scenario. Each UE can decide who partitions a task
according to the network status, e.g., channel quality and allocated
computation resource. Based on the framework, we model the latency to complete
a task, and formulate an optimization problem to minimize the average latency
among UEs. The problem is solved by jointly optimizing task partitioning and
the allocation of the communication and computation resources. The numerical
results show that, compared with the static offloading schemes, the proposed
algorithm achieves the lower latency in all tested scenarios. Moreover, both
mathematical derivation and simulation illustrate that the wireless channel
quality difference between a UE and different ESs can be used as an important
criterion to determine the right scheme.
