Browsing by Author "Kandasamy, A."

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A comprehensive survey on federated cloud computing and its future research directions
(Springer Science and Business Media Deutschland GmbH, 2021) Shishira, S.R.; Kandasamy, A.
The cloud computing paradigm is popular due to its pay-as-you-go model. Due to its increasing demand for service, the user has a huge advantage in paying for the service currently needed. In a federated cloud environment, there is one or more number of cloud service providers who share their servers to service the user request. It improves minimizing cost, utilization of services and improves performance. Clients will get benefited as there is a Service Level Agreement between both. In the present paper, survey is provided on the benefits of the federated environment, its architecture, provision of resources and future research directions. Paper also gives the comparative study on the above aspects. © Springer Nature Singapore Pte Ltd 2021.
A Conceptual Framework for Intelligent Management of Workloads in Cloud Environment
(Institute of Electrical and Electronics Engineers Inc., 2020) Shishira, S.R.; Kandasamy, A.
Cloud computing is an important paradigm for processing, computation, storage, and network bandwidth. Workloads are the amount of data given to the hardware resource for processing. Its behavior and properties play a major role in the efficient scheduling of requests to given resources. Also, it is very difficult to predict workloads nature if they are changing excessively. To address this issue, we propose a conceptual framework which can be used for efficient prediction and optimization of workloads in a cloud environment. Classification of optimization metrics based on the provider and consumer constraints are presented. In addition to this, some of the research gaps found during the study has been highlighted and also provided possible solutions in the cloud research domain. Â© 2020 IEEE.
A multiobjective phenomic algorithm for inference of gene networks
(2012) D'Souza, R.G.L.; Chandra Sekaran, K.C.; Kandasamy, A.
Reconstruction of gene networks has become an important activity in Systems Biology. The potential for better methods of drug discovery and of disease diagnosis hinge upon our understanding of the interaction networks between the genes. Evolutionary methods are proving to be successful in such problems and a number of such methods have been proposed. However, all these methods are based on processing of genotypic information. We have presented an evolutionary algorithm for reconstructing gene networks from expression data using phenotypic interactions, thereby avoiding the need for an explicit objective function. Specifically, we have also extended the basic phenomic algorithm to perform multiobjective optimization for gene network reconstruction. We have applied this novel algorithm to the yeast sporulation dataset and validated it by comparing the results to the links found between genes of the yeast genome at the SGD database. Â© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.
A Novel Feature Extraction Model for Large-Scale Workload Prediction in Cloud Environment
(Springer, 2021) Shishira, S.R.; Kandasamy, A.
In an enterprise cloud environment, it is difficult to handle an extensive number of loads. Serving the request in very less time leads to resource allocation problem. It is better to have prior knowledge of the incoming loads to auto-scale the resources. A novel architecture is proposed for the better prediction of workloads in the cloud environment. The proposed feature extraction model considers three essentials for managing cloud resources, i.e., CPU, Disk, and Memory. The model with the very nominal error achieved an accuracy of 98.72%. The proposed model is contrasted with other conventional predictive models for validation. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
A phenomic algorithm for inference of gene networks using S-systems and memetic search
(2012) D'Souza G, R.G.L.; Chandra Sekaran, K.C.; Kandasamy, A.
In recent years, evolutionary methods have seen unprecedented success in elucidation of gene networks, especially from microarray data. We have implemented the Phenomic Algorithm which is an evolutionary method for inference of gene networks based on population dynamics. We have used S-systems to model gene interactions and applied memetic search to fine tune the parameters of the inferred networks. We have tested the novel algorithm on artificial gene expression datasets obtained from simulated gene networks. We have also compared the results to those obtained from two other similar algorithms. Results showed that the new method, which we call as Phenomic Algorithm with Memetic Search (PAMS), is an effective method for inference of gene networks. Â© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.
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. Â© 2010 Springer-Verlag Berlin Heidelberg.
An Energy-Efficient Clustering Algorithm for Edge-Based Wireless Sensor Networks
(Elsevier B.V., 2016) Venkateswarlu, K.M.; Kandasamy, A.; Chandrasekaran, K.
To employee clustering algorithms in multi-hop data forwarding mechanism, Hot-spot problem will cause unbalanced energy dissipation among the cluster heads in the network. Unequal clustering technique promotes even energy dissipation only in inter-cluster communications not in intra-cluster communication. An Energy-efficient Clustering Algorithm (EECA) is introduced to avoid these problems in edge-based wireless sensor networks. The main aim of the presented algorithm is to avoid hot-spot problem by balancing uniform energy utilization among networked cluster heads. EECA constructs uneven size clusters in different levels to enable uniform energy expenditure among cluster heads. Data delivery is one of the important and unavoidable energy consuming operation in any sensor networks. To balance energy consumption load among data transmission routes, a multi-hop data forwarding protocol is introduced. Here, source node selects a relaying node who has minimum hop count to base station with more energy reserves and relayed less number of packets. Extensive experimental results prove that the presented algorithm overcome the congestion problem in the network by uniform distribution of energy consumption and enhances network's lifetime. Â© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
An Energy-Efficient Hybrid Clustering Mechanism for Wireless Sensor Network
(World Scientific Publishing Co. Pte Ltd wspc@wspc.com.sg, 2015) Muni Venkateswarlu, K.; Kandasamy, A.; Chandrasekaran, K.
Valuable energy resources of sensor network should be utilized wisely to prolong network's lifetime. Clustering technique helps wireless sensor network (WSN) to enhance its lifetime by reducing energy consumption on every individual sensor node in the network. In multi-hop data forwarding model, difference in energy consumption among cluster heads (HS) causes hot-spot problem in the network. While data is being transferred, the CH close to base station are burdened with heavy relay traffic from several data routes and tend to die early. Unequal clustering avoids this hot-spot problem by establishing different sized clusters at various levels in the network. Since unequal clustering technique does not control number of CHs it creates, it forms huge number of clusters in the network. This increases hop count between source and destination, and leads to impose more over head on each data forwarding route in the network. Also, rapid variation in cluster size causes imbalance in energy dissipation among clustered nodes in the network. This uneven energy consumption influences network performance and lifetime. In this paper, we present an energy-efficient hybrid clustering mechanism for wireless sensor network using equal and unequal clustering techniques to create limited number of clusters in varied sizes at various level of the network. This avoids hot-spot problem with minimum hop count between the source and destination and achieves uniform energy dissipation between intra-and inter-cluster communication. Simulation results show that the proposed clustering mechanism balances the energy consumption among clusters with its hybrid cluster formation mechanism and elevates sensor network lifetime. © 2015 World Scientific Publishing Company.
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.
Comparative study of simulation tools and challenging issues in cloud computing
(2018) Shishira, S.R.; Kandasamy, A.; Chandrasekaran, K.
Resource Scheduling lays a key role in large-scale cloud applications. It is difficult for the developers to do an extensive research on all the issues in real time as it requires infrastructure which is beyond the control, also network condition cannot be predicted. Hence simulations are used which imitates the real time environment. There are various simulators developed for the research as it is difficult to maintain the infrastructure on premise. Thus to understand the tools in deep, we focused on five open source tools such as Cloudsim, CloudAnalyst, iCancloud, Greencloud and CloudSched. The above mentioned tools are compared based on the respective architecture, the process of simulation, structural elements and performance parameters. In the paper, we have also discussed some of the challenging issues among the tools for further research. � Springer Nature Singapore Pte Ltd. 2018.
Comparative study of simulation tools and challenging issues in cloud computing
(Springer Verlag service@springer.de, 2018) Shishira, S.R.; Kandasamy, A.; Chandrasekaran, K.
Resource Scheduling lays a key role in large-scale cloud applications. It is difficult for the developers to do an extensive research on all the issues in real time as it requires infrastructure which is beyond the control, also network condition cannot be predicted. Hence simulations are used which imitates the real time environment. There are various simulators developed for the research as it is difficult to maintain the infrastructure on premise. Thus to understand the tools in deep, we focused on five open source tools such as Cloudsim, CloudAnalyst, iCancloud, Greencloud and CloudSched. The above mentioned tools are compared based on the respective architecture, the process of simulation, structural elements and performance parameters. In the paper, we have also discussed some of the challenging issues among the tools for further research. Â© Springer Nature Singapore Pte Ltd. 2018.
Core variation in the entrance region flow of casson fluid in an annuli
(2013) Kandasamy, A.; Pai, R.G.
The entrance region flow of a Casson fluid in an annular cylinder has been investigated numerically without making prior assumptions on the form of velocity profile within the boundary layer region, which is determined by a cross sectional integration of the momentum differential equation for a given distance from the channel entrance. Using the macroscopic mass balance equation, the thickness of the core has been obtained at each cross section of entrance region of annuli for different values of Casson number and for various values of aspect ratio. � (2013) Trans Tech Publications, Switzerland.
Core variation in the entrance region flow of casson fluid in an annuli
(2013) Kandasamy, A.; Pai, R.G.
The entrance region flow of a Casson fluid in an annular cylinder has been investigated numerically without making prior assumptions on the form of velocity profile within the boundary layer region, which is determined by a cross sectional integration of the momentum differential equation for a given distance from the channel entrance. Using the macroscopic mass balance equation, the thickness of the core has been obtained at each cross section of entrance region of annuli for different values of Casson number and for various values of aspect ratio. Â© (2013) Trans Tech Publications, Switzerland.
Core variation in the entrance region flow of herschel- Bulkley fluid in an annuli
(2014) Pai, R.G.; Kandasamy, A.
The entrance region flow in channels constitutes a problem of fundamental interest in engineering applications such as nuclear reactors, polymer processing industries, haemodialyzers and capillary membrane oxygenators. In such installations, the behavior of the fluid in the entrance region may play a significant part in the total length of the channel and the pressure drop may be markedly greater than for the case where the flow is regarded as fully developed throughout the channel. Recently, there has been an increasing interest in problems involving materials with variable viscosity such as Bingham materials, Casson fluids and Herschel-Bulkley fluids which are characterized by an yield value. The entrance region flow of a Herschel- Bulkley fluid in an annular cylinder has been investigated numerically without making prior assumptions on the form of velocity profile within the boundary layer region. This velocity distribution is determined as part of the procedure by cross sectional integration of the momentum differential equation for a given distance z from the channel entrance. Using the macroscopic mass balance equation the core thickness has been obtained at each cross section z of the annuli for specific values of Herschel -Bulkley Number, flow behavior index and various value of aspect ratio.
Core variation in the entrance region flow of herschel- Bulkley fluid in an annuli
(Newswood Limited publication@iaeng.org, 2014) Pai, R.G.; Kandasamy, A.
The entrance region flow in channels constitutes a problem of fundamental interest in engineering applications such as nuclear reactors, polymer processing industries, haemodialyzers and capillary membrane oxygenators. In such installations, the behavior of the fluid in the entrance region may play a significant part in the total length of the channel and the pressure drop may be markedly greater than for the case where the flow is regarded as fully developed throughout the channel. Recently, there has been an increasing interest in problems involving materials with variable viscosity such as Bingham materials, Casson fluids and Herschel-Bulkley fluids which are characterized by an yield value. The entrance region flow of a Herschel- Bulkley fluid in an annular cylinder has been investigated numerically without making prior assumptions on the form of velocity profile within the boundary layer region. This velocity distribution is determined as part of the procedure by cross sectional integration of the momentum differential equation for a given distance z from the channel entrance. Using the macroscopic mass balance equation the core thickness has been obtained at each cross section z of the annuli for specific values of Herschel -Bulkley Number, flow behavior index and various value of aspect ratio.
CREW: Cost and Reliability aware Eagle-Whale optimiser for service placement in Fog
(John Wiley and Sons Ltd cs-journals@wiley.co.uk, 2020) Paul Martin, J.; Kandasamy, A.; Chandrasekaran, K.
Integration of Internet of Things (IoT) with industries revamps the traditional ways in which industries work. Fog computing extends Cloud services to the vicinity of end users. Fog reduces delays induced by communication with the distant clouds in IoT environments. The resource constrained nature of Fog computing nodes demands an efficient placement policy for deploying applications, or their services. The distributed and heterogeneous features of Fog environments deem it imperative to consider the reliability performance parameter in placement decisions to provide services without interruptions. Increasing reliability leads to an increase in the cost. In this article, we propose a service placement policy which addresses the conflicting criteria of service reliability and monetary cost. A multiobjective optimisation problem is formulated and a novel placement policy, Cost and Reliability-aware Eagle-Whale (CREW), is proposed to provide placement decisions ensuring timely service responses. Considering the exponentially large solution space, CREW adopts Eagle strategy based multi-Whale optimisation for taking placement decisions. We have considered real time microservice applications for validating our approaches, and CREW has been experimentally shown to outperform the existing popular multiobjective meta-heuristics such as NSGA-II and MOWOA based placement strategies. © 2020 John Wiley & Sons Ltd
Elucidating the challenges for the praxis of fog computing: An aspect-based study
(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2019) Martin, J.P.; Kandasamy, A.; Chandrasekaran, K.; Joseph, C.T.
The evolutionary advancements in the field of technology have led to the instigation of cloud computing. The Internet of Things paradigm stimulated the extensive use of sensors distributed across the network edges. The cloud datacenters are assigned the responsibility for processing the collected sensor data. Recently, fog computing was conceptuated as a solution for the overwhelmed narrow bandwidth. The fog acts as a complementary layer that interplays with the cloud and edge computing layers, for processing the data streams. The fog paradigm, as any distributed paradigm, has its set of inherent challenges. The fog environment necessitates the development of management platforms that effectuates the orchestration of fog entities. Owing to the plenitude of research efforts directed toward these issues in a relatively young field, there is a need to organize the different research works. In this study, we provide a compendious review of the research approaches in the domain, with special emphasis on the approaches for orchestration and propose a multilevel taxonomy to classify the existing research. The study also highlights the application realms of fog computing and delineates the open research challenges in the domain. © 2019 John Wiley & Sons, Ltd.
An Energy-Efficient Clustering Algorithm for Edge-Based Wireless Sensor Networks
(2016) Venkateswarlu, K.M.; Kandasamy, A.; Chandrasekaran, K.
To employee clustering algorithms in multi-hop data forwarding mechanism, Hot-spot problem will cause unbalanced energy dissipation among the cluster heads in the network. Unequal clustering technique promotes even energy dissipation only in inter-cluster communications not in intra-cluster communication. An Energy-efficient Clustering Algorithm (EECA) is introduced to avoid these problems in edge-based wireless sensor networks. The main aim of the presented algorithm is to avoid hot-spot problem by balancing uniform energy utilization among networked cluster heads. EECA constructs uneven size clusters in different levels to enable uniform energy expenditure among cluster heads. Data delivery is one of the important and unavoidable energy consuming operation in any sensor networks. To balance energy consumption load among data transmission routes, a multi-hop data forwarding protocol is introduced. Here, source node selects a relaying node who has minimum hop count to base station with more energy reserves and relayed less number of packets. Extensive experimental results prove that the presented algorithm overcome the congestion problem in the network by uniform distribution of energy consumption and enhances network's lifetime. � 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
Energy-Efficient Clustering Algorithms for Edge-Based Wireless Sensor Networks
(National Institute of Technology Karnataka, Surathkal, 2016) K, Muni Venkateswarlu; Kandasamy, A.; Chandrasekaran, K.
A wireless sensor network (WSN) is a spatially distributed autonomous sensor nodes to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location. Sensor nodes’ resources have been a primary concern in designing any wireless sensor network application, since they are limited and non-renewable. Most of the current efforts on sensor network research have limited their design space solely to the sensor nodes themselves. Under such an approach, the burden of achieving complex networking functions all rests upon the sensor nodes. Thus, the search for alternative resources got much attention in sensor networks. Base station is one such resource abundant and constraint-free network component in wireless sensor network. By exploring base station’s capabilities, functional complexities in existing and upcoming algorithms can be simplified. A Base station Assisted Novel Network Design Space (BANDS) is proposed to exploit edge-base-station capabilities to offer new possibilities to meet up-to-minute requirements. Experimental results prove that the proposed work conserves network resources by shifting control overhead from sensor nodes to the base station. Based on the proposed network design space, a Zone-Based Routing Protocol (ZBRP) is introduced to enhance sensor network lifetime. ZBRP uses random back-off timers having communication cost and neighborhood count as primary parameters to select cluster heads for each data forwarding round. From the simulation results, it is observed that the proposed routing protocol improves network lifetime by distributing energy consumption evenly among clusters. To overcome the problems that arise with uneven energy dissipation, a novel Energy-efficient UnEqual Clustering algorithm (EUEC) is proposed. It creates limited and equivalent number of clusters in each level, which allows energy to be consumed evenly among cluster heads. Also, a disjoint multi-hop routing mechanism is proposed to balance network routing load among data forwarding paths. Experimental results prove that the proposed algorithm overcomes hot-spot problem with uniform energy dissipation among clusters and elevates network lifetime. iA novel and extended scale-free clustering technique called, Energy-efficient Hybrid Clustering Mechanism (EHCM) is proposed to overcome hot-spot problem without scalability issues. EHCM creates dynamic number of clusters in different sizes based on sensor node’s location information, which distributes energy dissipation uniformly among sensor nodes. From the simulation results, it is realized that the proposed work achieves hot-spot free network and prolongs network lifetime. Since the number of clusters are generated dynamically, the proposed algorithm is easily scalable.
Energy-efficient edge-based network partitioning scheme for wireless sensor networks
(2013) Muni, V.K.; Kandasamy, A.; Chandrasekaran, K.
The easy use of Wireless Sensor Networks has attracted applications from various fields. Day to day rise in wireless sensor network applications introduce new challenges to researchers. One such critical challenge is, optimal usage of network resources. Energy is one of the most important concerns in wireless sensor networks. Even though there has been an extensive research work done on this issue, the problem is still open with new requirements emerging every day. Exchange of control information consumes most of network resources to carry out network operations. An attempt has been made in the recent past to avoid this wastage of resources, by exploiting the properties of resource abundant sources in the network. Base station is one such source in wireless sensor network. The base station is resource abundant and less constrained network component in wireless sensor networks. The recent research works have focused more in this direction to explore the benefits of base station characteristics. In this perspective, a novel network partitioning mechanism is proposed here, to build energy efficient wireless sensor networks. The system proposed, distributes network load uniformly with little control overhead on energy resources in the network. The uniform distribution of sensor nodes in every part helps the network to distribute the load uniformly. From simulation results, it is noted that, the proposed system elevates the average lifetime of sensor nodes. � 2013 IEEE.