Intelligent Salp Swarm Scheduler With Fitness Based Quasi-Reflection Method for Scientific Workflows in Hybrid Cloud-Fog Environment

The burgeoning volume of data from the IoT applications and intelligent devices processed on the cloud data centers can lead to network congestion and transmission delay. Compared to cloud computing, fog computing focuses on ubiquitous connected heterogeneous devices and addresses the transmission latency by placing the fog nodes at the network edge. Concerning the limited resources of fog nodes enable the computationally intensive tasks to offload on the cloud resources. Scheduling of deadline-constrained workflows with minimum execution cost is challenging due to complex and uncertain computation offloading problems. Therefore, an intelligent fuzzy scheduler is designed to offload tasks characterized with uncertain parameters to the appropriate resources. A new salp swarm algorithm has been exploited to learn and optimize fuzzy task-resource allocation rules. In addition to this, to overcome the shortcomings of the salp swarm algorithm, it is employed with one of the best opposition methods named: Fitness-based quasi-reflection method. The inclusion of the opposition method enhances the proposed ISSS-FQR (Intelligent salp swarm scheduler with the fitness-based quasi-reflection method) approach and improves the learning process. Simulation studies on the benchmark workflows are carried out to demonstrate the efficacy of ISSS-FQR. ISSS-FQR has been compared with the classical algorithms, including chemical reaction optimization and ant colony optimization algorithms for workflow scheduling problems (CR-AC), Cost-Makespan aware scheduling (Deadline-based CMaS), and Directional and non-local convergent particle swarm optimization (DNCPSO). From the analyzed result, ISSS-FQR outperforms the rest of the classical algorithms, which proves the effectiveness of ISSS-FQR. Note to Practitioners—This paper provides a novel method called ISSS-FQR for minimizing the cost of execution of IoT applications while satisfying the deadline constraint. An intelligent fuzzy scheduler is designed to offload tasks characterized with uncertain parameters to the appropriate resources. The ISSS-FQR combines the Salp Swarm Algorithm and the OBL method named FQR to learn the task-resource allocation rules. It is compared with three state-of-the-art algorithms called CR-AC, Deadline-based CMaS, and DNCPSO. From the analyzed result, it has been observed that ISSS-FQR outperforms the previous algorithms.