An Analysis of Low-Overhead and High Efficiency Task Scheduling in the Cloud and Fog Environments

Cloud-fog computing refers to a diverse range of service environments created to provide customers with prompt and flexible services in response to the massive amount of data produced daily by the Internet of Things (IoT). The provider allots enough resources and uses scheduling algorithms in fog or cloud settings to guarantee that incoming Internet of Things (IoT) jobs are completed on time and that service level agreements (SLAs) are met. Yet, a lot of the existing methods overlook important aspects like cost and energy use, which both have a big impact on how effective cloud services are. The solution to these issues is a reliable scheduling technique that maximises scheduling of the diverse workload and improves QoS. We implement a nature-inspired technique called electric earthworm optimisation (EEOA) to schedule Internet of Things (IoT) queries in a cloud-fog environment. After it was discovered that combining the earthworm optimisation algorithm (EOA) with the electric fish optimisation algorithm (EFO) may improve its performance in solving problems, this technique was created. Benchmarks generated from large-scale, real-world workloads like CEA-CURIE and HPC2N were used to test the execution time, cost, makespan, and energy use of the proposed scheduling technique. According to simulation results, the proposed method performs significantly better than state-of-the-art algorithms in terms of efficiency (89%), energy consumption (94%), and total cost (87%). The suggested method saves time as compared to the current state of affairs, according to extensive simulations.