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.

CostEfficient Resource Provision for Multiple Mobile Users in Fog Computing Fog computing is an emerging paradigm that brings the computing capabilities close to distributed IoT devices, which provides networking services between end devices and traditional cloud data centers. One important mission is to further reduce the monetary cost of fog resources while meeting the evergrowing demand of multiple users. In this paper, we focus on minimizing the total cost for multiple mobile users to provide an efficient resource provisioning scheme in fog computing. The total cost includes two aspects: the replication cost and the transmission cost. We consider two cases for the resource provision problem by focusing on different cost models. First, one simple case where users can only upload one replication is discussed, and an optimal solution is proposed by converting the original problem into one of bipartite graph matching. Then we consider a more complicated case that each user can upload multiple replications on fog nodes in the resource provisioning. For different transmission cost models, the transmission cost is related to the distance of each pair of fog nodes. This problem is proven to be NPhard. We first propose a nonadaptive algorithm which is proved to be bounded by 23W13OPT. Another 3eapproximation algorithm is proposed based on local search, which has better performance with higher complexity. Extensive simulations also prove the efficiency of our schemes.