  A vast and growing number of IoT applications connect physical devices, such
as scientific instruments, technical equipment, machines, and cameras, across
heterogenous infrastructure from the edge to the cloud to provide responsive,
intelligent services while complying with privacy and security requirements.
However, the integration of heterogeneous IoT, edge, and cloud technologies and
the design of end-to-end applications that seamlessly work across multiple
layers and types of infrastructures is challenging. A significant issue is
resource management and the need to ensure that the right type and scale of
resources is allocated on every layer to fulfill the application's processing
needs. As edge and cloud layers are increasingly tightly integrated, imbalanced
resource allocations and sub-optimally placed tasks can quickly deteriorate the
overall system performance. This paper proposes an emulation approach for the
investigation of task placements across the edge-to-cloud continuum. We
demonstrate that emulation can address the complexity and many
degrees-of-freedom of the problem, allowing us to investigate essential
deployment patterns and trade-offs. We evaluate our approach using a machine
learning-based workload, demonstrating the validity by comparing emulation and
real-world experiments. Further, we show that the right task placement strategy
has a significant impact on performance -- in our experiments, between 5% and
65% depending on the scenario.
