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Mobile edge computing (MEC) is one of the promising solutions to processcomputational-intensive tasks for the emerging time-critical Internet-of-Things(IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomousvehicle. The latency can be reduced further, when a task is partitioned andcomputed by multiple edge servers' (ESs) collaboration. However, thestate-of-the-art work studies the MEC-enabled offloading based on a staticframework, which partitions tasks at either the local user equipment (UE) orthe primary ES. The dynamic selection between the two offloading schemes hasnot been well studied yet. In this paper, we investigate a dynamic offloadingframework in a multi-user scenario. Each UE can decide who partitions a taskaccording to the network status, e.g., channel quality and allocatedcomputation resource. Based on the framework, we model the latency to completea task, and formulate an optimization problem to minimize the average latencyamong UEs. The problem is solved by jointly optimizing task partitioning andthe allocation of the communication and computation resources. The numericalresults show that, compared with the static offloading schemes, the proposedalgorithm achieves the lower latency in all tested scenarios. Moreover, bothmathematical derivation and simulation illustrate that the wireless channelquality difference between a UE and different ESs can be used as an importantcriterion to determine the right scheme.

Adaptive Task Partitioning at Local Device or Remote Edge Server for Offloading in MEC