Star-Quake: A New Operator in Multi-Objective Gravitational Search Algorithm for Task Scheduling in IoT-Based Cloud–Fog Computing System

The past decade has witnessed the advancement of Internet of Things (IoT). Task scheduling is the most important challenge in this system for managing makespan, energy, and cost. In this paper, a new multi-objective function is proposed to jointly minimize makespan, energy and monetary cost for task scheduling in fog-cloud system. Moreover, star-quake, a new operator, is defined and added to multi-objective version of the Gravitational Search Algorithm (MOGSA). This operator can balance the abilities of the algorithm such as selection pressure, exploitation and exploration, so this algorithm has the ability to avoid becoming trapped in local optima. and converge toward better solutions in complex problems. In this paper, the performance of the proposed algorithm is evaluated in two scenarios. First, the performance of the proposed method is compared with some popular multi-objective optimization methods on some standard test functions. Results show the proposed algorithm has better performance compared to other methods. Then, this algorithm is used to find good solution for the task scheduling problem. The proposed approach has improved makespan by 18%, energy consumption by 22% and processing cost by 40%. Statistical analysis illustrates that the algorithm has rank one among other approaches for task scheduling.