Cost-Effective Utilization of Complementary Cloud Resources for the Scheduling of Real-Time Workflow Applications in a Fog Environment

Abstract

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 real-time 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 real-time, 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 real-time 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.