  Recent advances in the areas of Internet of Things (IoT), Big Data, and
Machine Learning have contributed to the rise of a growing number of complex
applications. These applications will be data-intensive, delay-sensitive, and
real-time as smart devices prevail more in our daily life. Ensuring Quality of
Service (QoS) for delay-sensitive applications is a must, and fog computing is
seen as one of the primary enablers for satisfying such tight QoS requirements,
as it puts compute, storage, and networking resources closer to the user. In
this paper, we first introduce FogPlan, a framework for QoS-aware Dynamic Fog
Service Provisioning (QDFSP). QDFSP concerns the dynamic deployment of
application services on fog nodes, or the release of application services that
have previously been deployed on fog nodes, in order to meet low latency and
QoS requirements of applications while minimizing cost. FogPlan framework is
practical and operates with no assumptions and minimal information about IoT
nodes. Next, we present a possible formulation (as an optimization problem) and
two efficient greedy algorithms for addressing the QDFSP at one instance of
time. Finally, the FogPlan framework is evaluated using a simulation based on
real-world traffic traces.
