Gerçek zamanlı hizmetler sunmak için sis-bulut ortamında ıot kaynak kullanımının artırılması

When resources and computers are made available on demand over the internet, this is called fog-cloud computing. This makes it easy to offer people a variety of integrated computing services without being limited by local resources. This means giving people more than just places to store and back up their data and ways to sync their own files. but it also has processing power and a simple software interface that lets the user control when it's connected to the network. This makes things easier by ignoring many details and internal processes. When it comes to the cloud, scheduling algorithms are necessary to provide services that meet goals like high performance, low prices, minimal energy use, and so on. It is an NP-hard problem to come up with scheduling methods that meet more than one of these goals. We introduce some new heuristic scheduling algorithms in this thesis that allow for multi-objective optimization. We then compare their effectiveness with some well-known scheduling algorithms to study how well they work. The first algorithm, the BDA, looks at the Make-span and the period it takes to complete jobs by the due date. Our algorithm takes into account a task's due date by giving the most weight for the job with the earlier due date and send it to the resource that can complete it in the shortest amount of time to achieve the shortest Makespan. Fog Max-Cloud Min is the name of the first part of our method. Ant Colony Optimization is the name of the second part. We looked at how well our suggested algorithm methods met deadlines and how long the system took to make. Compared to the tools we have now. The study results show that our algorithm is better at getting things done with the lowest Makespan and the best deadline satisfaction. In the second algorithm, We develop improvements to the performance and cost (PC) algorithm in order to give more weight Considering the significant expenses involved, reduce energy consumption, and reduce the duration of create a product. In this work, we describe an approach that is a combination of the PCA and GWO techniques. This algorithm is called the Performance and Cost-Gray Wolf Optimization (PC-GWO) algorithm. The results of the test indicate that the PC-GWO The algorithm decreases the average total energy usage by 12.17 percent, 11.5 percent, and 7.19 percent, as well as the Makespan by 16.72 percent, 16.38 percent, and 14.10 percent. When compared to the GWO algorithm, the PCA method, and the PSO algorithm, it also improves the best average resource consumption by 13.2 percent, 12.05%, and 10.9 percent respectively.