The cloud computing paradigm has risen, during the last 20 years, to the task of bringing
powerful computational services to the masses. Centralizing the computer hardware to a few
large data centers has brought large monetary savings, but at the cost of a greater geographical
distance between the server and the client. As a new generation of thin clients have emerged,
e.g. smartphones and IoT-devices, the larger latencies induced by these greater distances,
can limit the applications that could benefit from using the vast resources available in cloud
computing. Not long after the explosive growth of cloud computing, a new paradigm, edge
computing has risen. Edge computing aims at bringing the resources generally found in cloud
computing closer to the edge where many of the end-users, clients and data producers reside.
In this thesis, I will present the edge computing concept as well as the technologies enabling
it. Furthermore I will show a few edge computing concepts and architectures, including multi-
access edge computing (MEC), Fog computing and intelligent containers (ICON). Finally, I
will also present a new edge-orchestrator, the ICON Python Orchestrator (IPO), that enables
intelligent containers to migrate closer to the users.
The ICON Python orchestrator tests the feasibility of the ICON concept and provides per-
formance measurements that can be compared to other contemporary edge computing im-
plementations. In this thesis, I will present the IPO architecture design including challenges
encountered during the implementation phase and solutions to specific problems. I will also
show the testing and validation setup. By using the artificial testing and validation network,
client migration speeds were measured using three different cases - redirection, cache hot ICON
migration and cache cold ICON migration. While there is room for improvements, the migration
speeds measured are on par with other edge computing implementations.
