Department of Computer Science and Information Systems

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Author: search.filters.author.K., Pradheep KumarSubject: search.filters.subject.Algorithm

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    Scheduling Algorithm for Asymmetric Multicore Platforms using Non-uniform Laxity based Clustering and Duplication
    (European Journal of Scientific Research, 2012-10) K., Pradheep Kumar
    In this paper, a new scheduling algorithm using task clustering and non-uniform laxity is proposed. This algorithm forms task clusters after analyzing the task dependencies. For each task cluster comprising of a parent and dependent tasks, a cluster parameter is computed. Based on this parameter, a rank is assigned to each cluster. The clusters are scheduled in the order of their rank, so as to minimize the communication cost and reduce the utilization of slack resources. Clusters are scheduled as far as possible on the same core. If a cluster cannot be scheduled completely on the same core, the parent task is either migrated or duplicated depending on the associated costs involved. The algorithm has been tested using 15 different programs as tasks.
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    Modified Backward Chaining Algorithm Using Artificial Intelligence Planning IoT Applications
    (IGI Global, 2019) K., Pradheep Kumar
    In this chapter, an automated planning algorithm has been proposed for IoT-based applications. A plan is a sequence of activities that leads to a goal or sub-goals. The sequence of sub-goals leads to a particular goal. The plans can be formulated using forward chaining where actions lead to goals or by backward chaining where goals lead to actions. Another method of planning is called partial order planning where all actions and sub-goals are not illustrated in the plan and left incomplete. When many IoT devices are interconnected, based on the tasks and activities involved resource allocation has to be optimized. An optimal plan is one where the total plan length is minimum, and all actions consume similar quantum of resources to achieve a goal. The scheduling cost incurred by way of resource allocation would be minimum. Compared to the existing algorithms L2-Plan (Learn to Plan) and API, the algorithm developed in this work improves optimality of resources by 14% and 36%, respectively