  With the fast growing quantity of data generated by smart devices and the
exponential surge of processing demand in the Internet of Things (IoT) era, the
resource-rich cloud centres have been utilised to tackle these challenges. To
relieve the burden on cloud centres, edge-cloud computation offloading becomes
a promising solution since shortening the proximity between the data source and
the computation by offloading computation tasks from the cloud to edge devices
can improve performance and Quality of Service (QoS). Several optimisation
models of edge-cloud computation offloading have been proposed that take
computation costs and heterogeneous communication costs into account. However,
several important factors are not jointly considered, such as heterogeneities
of tasks, load balancing among nodes and the profit yielded by computation
tasks, which lead to the profit and cost-oriented computation offloading
optimisation model PECCO proposed in this paper. Considering that the model is
hard in nature and the optimisation objective is not differentiable, we propose
an improved Moth-flame optimiser PECCO-MFI which addresses some deficiencies of
the original Moth-flame Optimiser and integrate it under the edge-cloud
environment. Comprehensive experiments are conducted to verify the superior
performance of the proposed method when optimising the proposed task offloading
model under the edge-cloud environment.
