  While edge computing is envisioned to superbly serve latency sensitive
applications, the implementation-based studies benchmarking its performance are
few and far between. To address this gap, we engineer a modular edge cloud
computing system architecture that is built on latest advances in
containerization techniques, including Kafka, for data streaming, Docker, as
application platform, and Firebase Cloud, as realtime database system. We
benchmark the performance of the system in terms of scalability, resource
utilization and latency by comparing three scenarios: cloud-only, edge-only and
combined edge-cloud. The measurements show that edge-only solution outperforms
other scenarios only when deployed with data located at one edge only, i.e.,
without edge computing wide data synchronization. In case of applications
requiring data synchronization through the cloud, edge-cloud scales around a
factor 10 times better than cloud-only, until certain number of concurrent
users in the system, and above this point, cloud-only scales better. In terms
of resource utilization, we observe that whereas the mean utilization increases
linearly with the number of user requests, the maximum values for the memory
and the network I/O heavily increase when with an increasing amount of data.
