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.

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Existing burst scheduling techniques could be improved in terms of bandwidth utilization and QoS support. In this paper, we introduced a new partially preemptive scheduling technique with QoS support. The technique is capable of handling data bursts in parts and may use preemption due to the priorities of data bursts in a multi-service OBS network environment. Simulation studies suggest that more than 50% reduction in dropping probability and approximately 40% improvement in channel utilization is reachable at 0.8 load. The studies also reveal that the new scheduling technique has the ability to predict and control service performance differentiation among defined service classes.

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