A lot of people these days use cloud computing to handle their massive data sets. Clusters with many software instances and distributed processing capabilities are now simple for businesses to create, manage, and use. Scheduling is a crucial component of distributed computing because it helps users make better use of their resources and hence reduce the amount of time it takes to complete a calculation. The goal of a deadline scheduling method is to help a scheduler prioritize works in light of impending due dates, and to do so while taking into account deadline limits and utilizing a cost model to estimate the remaining work load. The remaining running time for tasks is calculated based on their completion using the cost model of the deadline scheduling method, which is compatible with requests for generic abstract resources like virtual cores, memory, and containers. In order to verify and quantify the scheduler’s performance, the Deadline scheduling algorithm executes many scenarios on a cluster. We tested the deadline scheduling method in a variety of settings, and it consistently delivered the predicted results.
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