Faculty Publications

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Author: search.filters.author.Chandrasekaran, K.Subject: search.filters.subject.Bioinformatics

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    Membrane-based models for service selection in cloud
    (Elsevier Inc., 2021) Raghavan, S.; Chandrasekaran, K.
    Cloud service selection is one of the prime areas of research within the ambit of cloud computing, which has gained wide attention in the recent past. The service selection algorithm primarily involves selecting the best service from a set of available services, based on Quality of Service (QoS) attributes. The QoS attributes are the parameters which allow the users to ascertain the actual quality of the service, often quantitatively. Over the years, there have been several methods designed for service selection in the cloud that are primarily sequential, with many being sensitive to changes. Thus, the aim is to propose multiple robust and parallel models for cloud service selection. The proposed models are designed using Membrane Computing paradigm, which is an inherently parallel computing model, combined with the Improved Technique for Order of Preference by Similarity to Ideal Solution (ITOPSIS), a popular Multi-Criteria Decision Making Method. Two methods based on a tactical amalgamation of ITOPSIS and Enzymatic Numerical P System (A membrane computing device variant) structure are proposed here. The proposed parallel models are implemented, tested, and the obtained results are analyzed. The results indicate one model to be robust (less sensitive) and the other to be moderately sensitive. © 2020 Elsevier Inc.
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    ENPS-IPROMETHEE: Enzymatic Numerical P System-based Improved Preference Ranking Organization Method for Enrichment Evaluation
    (Springer, 2022) Raghavan, S.; Chandrasekaran, K.
    Membrane Computing is a natural computing paradigm inspired by the structure and activity of a biological cell. Membrane-based models can be realized using P System and these models have multiple applications. Here, it is applied to solve a Multi-criteria Decision-Making (MCDM) problem. MCDM is one of the important area in Decision-Making. It involves ranking items from a given set of items based on multiple criteria and it has several applications in different broad arenas which include Economics, Engineering and Management. MCDM includes several clusters of techniques that have been divided based on its modes of operation. All the techniques available till now consider sequential computing paradigm as the base for computation but in this work a parallel technique is used. Here, Enzymatic Numerical P System (ENPS)-based MCDM technique is designed. ENPS is a variant of P System used specifically for numerical problems. The proposed model, ENPS-IPROMETHEE is based on Improved Preference Ranking Organization Method for Enrichment Evaluation (IPROMETHEE), a popular outranking-based MCDM method. The designed model is verified and tested using PeP and GPUPeP simulators which are used for simulating ENPS models. A membrane file generator tool called as P-Generator is developed for automatic membrane generation. Two standard, existing datasets are considered and the model is studied for its sensitivity. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.