GA-IRACE: Genetic Algorithm-Based Improved Resource Aware Cost-Efficient Scheduler for Cloud Fog Computing Environment

The ever-growing number of Internet Things (IoT) devices increases the amount data produced on daily basis. To handle such a massive data, cloud computing provides storage, processing, and analytical services. Besides this, real-time applications, i.e., online gaming, smart traffic management, healthcare, cannot tolerate high latency bandwidth consumption. fog paradigm brings services closer to network edge provide quality service (QoS) applications. However, efficient task scheduling becomes critical for improving performance due heterogeneous nature, resource-constrained, distributed environment resources. With an algorithm, response time application requests can be reduced along with resource costs. This paper presents genetic algorithm-based solution find approach mapping modules in environment. Our proposed is based execution as fitness function determine module available devices. has been evaluated compared against baseline algorithms terms time, monetary cost, bandwidth. Comprehensive simulation results show that offers better strategy than existing scheduler.