Novel orchestration architecture for Fog computing Cloud computing technologies such as virtualization and orchestration have revolutionized a great number of different domains, from the industry to healthcare. However, there are still certain applications that can not take advantage of this paradigm due to stricter requirements such as endtoend response times. A new paradigm called Fog computing is arising to answer the needs of these applications. Fog nodes have lower hardware resources that their Cloud counterparts and are distributed over a wider geographical area. Porting technologies designed for the Cloud, specifically orchestration, to this new paradigm requires to handle these peculiarities. A novel orchestration architecture is proposed evolving on current ones, directly focusing Fog characteristics. Multiple schedulers are dynamically deployed in order to achieve a better scheduling throughput and spread the resources required by the scheduler among multiple lowerresource nodes. It uses an applicationcentric approach at the scheduler level in order to enable new scheduling parameters. The scheduling throughput of this architecture is compared to the most common Cloud orchestration architecture.

CostEffective Utilization of Complementary Cloud Resources for the Scheduling of RealTime Workflow Applications in a Fog Environment According to the fog computing paradigm, the main processing of Internet of Things (IoT) data is typically performed in the fog layer, close to where the data are generated. However, the computational capacity of the fog resources is usually limited. On the other hand, the computational demands and realtime requirements of IoT applications continue to grow at a staggering rate. Consequently, it is imperative to explore alternative strategies that involve the collaboration between the fog and cloud resources. Towards this direction, in this paper we propose a strategy for the utilization of complementary cloud resources, in order to assist in the processing of realtime, computationally intensive IoT workflow jobs that arrive dynamically in a fog environment. As the cloud involves higher data transfer latency and monetary cost, our approach takes into account these two factors, in addition to the realtime constraints of the workload. The proposed scheduling approach is based on the tradeoff between performance and monetary cost. During resource selection, different contribution factors of these two parameters are investigated. Furthermore, the proposed scheduling heuristic is compared against a baseline policy that utilizes only the fog resources, under different sizes of workflow input data.