Faculty Publications
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Publications by NITK Faculty
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Item A fuzzy sectional real-time scheduling algorithm based on system load(Springer Verlag service@springer.de, 2013) Annappa, B.Earliest Deadline First (EDF) Algorithm is one of the most widely known dynamic real-time task scheduling algorithms. However, when a real-time system is overloaded, experiments and analysis have proved that EDF algorithm is ineffective. Considering the algorithm's instability during the practical task executing environment in an overloaded state, it is necessary to apply a few decision making techniques to ensure a good overall performance. In this paper, we propose a dynamic sectional real-time scheduling algorithm called Fuzzy Sectional Scheduling (FSS), which identifies the system load and employs suitable scheduling techniques to improve overall performance. The simulation results show that the Fuzzy Sectional Scheduling Algorithm could improve the real-time system performance to a considerably greater extent compared to the classical algorithms such as EDF, HVF (Highest Value First) and HDF (Highest Density First) algorithms; under all workload conditions. © 2013 Springer.Item A Workflow Scheduling Approach With Modified Fuzzy Adaptive Genetic Algorithm in IaaS Clouds(Institute of Electrical and Electronics Engineers Inc., 2023) Rizvi, N.; Ramesh, D.; Wang, L.; Annappa, B.The emergence of the cloud platform with substantial resources to offer on-demand instigated the researchers to migrate the scientific workflows to the cloud environment. The scheduling of workflows with diverse QoS parameters is not a trivial task, but an NP-Complete problem. Several heuristics for QoS constrained workflows have been investigated. However, most of them focus only on time and cost and do not guarantee high resource utilization. The scheduling of the workflow tasks over the minimum cloud resources under the defined time limit is a grave concern. In this article, an algorithm named MFGA (Modified Fuzzy Adaptive Genetic Algorithm) has been formulated to minimize the makespan and improve resource utilization under both deadline and budget constraints. A fuzzy logic controller has also been devised to control the crossover and mutation rates that prevent MFGA from getting stuck in a local optimum. MFGA has a novel crossover technique that adds the fittest solutions in the population. Additionally, a new mutation technique has also been introduced, which minimizes the makespan and increases the reusability of the resources. The simulation experiments with the real workflows show that the proposed MFGA outperforms other state-of-the-art algorithms. © 2008-2012 IEEE.