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Cloud-fog computing systems require task scheduling to efficiently manage resources and ensure timely task execution in a distributed environment. Fog computing extends computation, storage, and networking to the edge of the network, thereby improving the user experience, and reducing latency, and data transfer. Task scheduling and client-processing resource management in cloud-fog computing affect cloud system performance and client services. Currently, task scheduling methods mainly focus on assigning client tasks to the most suitable cloud resource or virtual machine (VM) to obtain the minimum scheduled time. However, these techniques do not consider a realistic communication model that, allows many parallel tasks to be sent and received simultaneously without delay. This paper introduces a heuristic workflow scheduling algorithm called Collision-Avoid based on Cloud task Scheduling (CACS) to assign client workflow represented by a directed acyclic graph (DAG) and execute the most appropriate cloud-fog nodes under the single client I/O port restriction to achieve minimum workflow completion time, least delay, and higher resource performance. FogWorkflowSim was used as the cloud-fog computing system simulation. The experimental results showed that the proposed method achieved much better scheduling results than the other compared algorithms.

The study introduces a methodology that utilizes data-driven approaches to optimize coffee drying operations. This is achieved through the integration of ambient sensor data and chemical analysis. This statement underscores the significance of temperature regulation, humidity levels, and light intensity within the context of coffee production. There exists a positive correlation between elevated temperatures and increased rates of drying, but humidity has a role in determining the duration of the drying process and the preservation of aromatic compounds. The significance of light intensity in dry processing is also crucial, since excessive exposure can compromise both the taste and quality of the product. The findings of chemical investigations demonstrate a correlation between environmental factors and the composition of coffee. Specifically, increased temperatures are associated with higher quantities of caffeine, while the concentration of chlorogenic acid is influenced by humidity levels. The research additionally underscores the variations in sensory characteristics among various processing techniques, underscoring the significance of procedure choice in attaining desirable taste profiles. The integration of weather monitoring, chemical analysis, and sensory assessments is a robust approach to augmenting quality control within the coffee sector, thereby facilitating the provision of great coffee products to discerning consumers.