Faculty Publications
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Publications by NITK Faculty
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Item A phenomic approach to genetic algorithms for reconstruction of gene networks(2010) D'Souza, R.G.L.; Chandra Sekaran, K.C.; Kandasamy, A.Genetic algorithms require a fitness function to evaluate individuals in a population. The fitness function essentially captures the dependence of the phenotype on the genotype. In the Phenomic approach we represent the phenotype of each individual in a simulated environment where phenotypic interactions are enforced. In reconstruction type of problems, the model is reconstructed from the data that maps the input to the output. In the phenomic algorithm, we use this data to replace the fitness function. Thus we achieve survival-of-the- fittest without the need for a fitness function. Though limited to reconstruction type problems where such mapping data is available, this novel approach nonetheless overcomes the daunting task of providing the elusive fitness function, which has been a stumbling block so far to the widespread use of genetic algorithms. We present an algorithm called Integrated Pheneto-Genetic Algorithm (IPGA), wherein the genetic algorithm is used to process genotypic information and the phenomic algorithm is used to process phenotypic information, thereby providing a holistic approach which completes the evolutionary cycle. We apply this novel evolutionary algorithm to the problem of elucidation of gene networks from microarray data. The algorithm performs well and provides stable and accurate results when compared to some other existing algorithms. © 2010 Springer-Verlag Berlin Heidelberg.Item Inference of Gene Networks from Microarray Data through a Phenomic Approach(2010) D'Souza, R.G.L.; Chandra Sekaran, K.C.; Kandasamy, A.The reconstruction of gene networks is crucial to the understanding of cellular processes which are studied in Systems Biology. The success of computational methods of drug discovery and disease diagnosis is dependent upon our understanding of the biological basis of the interaction networks between the genes. Better modelling of biological processes and powerful evolutionary methods are proving to be a key factor in the solution of such problems. However, most of these methods are based on processing of genotypic information. We present an evolutionary algorithm for inferring gene networks from expression data using phenotypic interactions. The benefit of this is that we avoid the need for an explicit objective function in the optimization process. In order to realize this, we have implemented a method called as the Phenomic algorithm and validated it for stability and accuracy in the reconstruction of gene networks. © Springer-Verlag Berlin Heidelberg 2010.Item Mobility aware autonomic approach for the migration of application modules in fog computing environment(Springer Science and Business Media Deutschland GmbH, 2020) Martin, J.P.; Kandasamy, A.; Chandrasekaran, K.The fog computing paradigm has emanated as a widespread computing technology to support the execution of the internet of things applications. The paradigm introduces a distributed, hierarchical layer of nodes collaboratively working together as the Fog layer. User devices connected to Fog nodes are often non-stationary. The location-aware attribute of Fog computing, deems it necessary to provide uninterrupted services to the users, irrespective of their locations. Migration of user application modules among the Fog nodes is an efficient solution to tackle this issue. In this paper, an autonomic framework MAMF, is proposed to perform migrations of containers running user modules, while satisfying the Quality of Service requirements. The hybrid framework employing MAPE loop concepts and Genetic Algorithm, addresses the migration of containers in the Fog environment, while ensuring application delivery deadlines. The approach uses the pre-determined value of user location for the next time instant, to initiate the migration process. The framework was modelled and evaluated in iFogSim toolkit. The re-allocation problem was also mathematically modelled as an Integer Linear Programming problem. Experimental results indicate that the approach offers an improvement in terms of network usage, execution cost and request execution delay, over the existing approaches. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.Item BeeM-NN: An efficient workload optimization using Bee Mutation Neural Network in federated cloud environment(Springer Science and Business Media Deutschland GmbH, 2021) Shishira, S.R.; Kandasamy, A.Cloud computing is an extensively implemented technique to handle enormous amount of data as it provides flexibility and scalability features. In an established cloud environment, users process their request to share the data that are stored in it. Under the dynamic cloud environment, multiple requests are processed in a short time, which leads to the problem of resource allocation. Virtual Machines or servers aid the cloud in maintaining the workflow active through proper distribution of resources. However, the accurate workload prediction model is necessary for effective resource management. In the present paper, a novel BeeM-NN framework is proposed through the integration of Workload Neural Network Algorithm (WNNA) and Novel Bee Mutation Optimization Algorithm (NBMOA) for optimized workload prediction in a cloud environment. The proposed model encloses the Fitness Feature Extraction Algorithm initially to extract the feature dataset from Azure public dataset and is provided to train the WNNA. The predicted workloads are optimized with the NBMOA in the cloud. The generated model is tested using the workload data traces from the federated cloud service provider and is evaluated and compared with the existing models. The outcome showed the prediction model achieved an accuracy of 99.98% better than the current models with optimum performance in the consumption of resources and cost. The future work is to use the predicted workloads for scheduling in the cloud. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.