Conference Papers

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    IT service provisioning by passing hints in module interfaces
    (2012) Naik, N.N.; Veigas, J.P.; Chandrasekaran, K.
    Communication is a crucial aspect in the domain of IT service provisioning, where data is being transmitted from the sending entity to the receiving entity by the interaction of applications via an interface. When the data is transferred, large amount of time is consumed for its computation by the service at the receiving side. When two applications interact via an interface the communication can be made effective by enhancing the IT service provisioning system with inclusion of web services by some faster means. In this paper we discuss how IT service provisioning can be handled by making use of hints using web services that can be passed by the modules within the application via interfaces from a client to the service, which can avoid expensive computation by the service. The hint is passed in its request and if correct, a service can avoid the computation and communication overhead leading to better system performance. If the message or hint is incorrect it leads to erroneous computations. Passing hints between the modules within an application should take place in an effective, non-erroneous way by imparting quality and less computation by the service involved between two communicating entities contributing towards the promises of services provided by cloud computing. © 2012 IEEE.
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    Achieving green computing by effective utilization of cloud resources using a cloud OS
    (2013) Naik, N.N.; Kanagala, K.; Veigas, J.P.
    Cloud Computing is composed of amalgamation of different elements and solutions, namely operating systems running on a single virtualized computing environment, middleware layers that attempt to combine physical and virtualized resources from multiple operating systems, and specialized application engines that influence a substantial benefit of the cloud service provider (e.g. Amazon). In this platform, huge amount of resources are present in the cloud and this resources need to be managed effectively. When this resources are united, major problems are incorporated into the system as well as the application due to compatibility and consistency constraints, hence there does not exist a unified processing environment to carry out this operation. In this paper, we demonstrate the importance of cloud OS known as virtual distributed operating system that binds together this cloud resources in a single-unified processing environment that helps to manage the cloud resources in a flexible and accessible approach henceforth enhancing the systems performance. Several cloud OS are available to bridge the gap between this cloud resources. The experiment is being performed on several existing platforms that allows the user to access this resources with a user friendly GUI. By making use of a cloud OS the overhead in handling the cloud resources by the running machine can be successfully reduced for a particular cloud user hence directing towards efficient use of systems resources and contributing towards the features of green computing. © 2013 IEEE.
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    Deep Learning-Based Prediction, Classification, Clustering Models for Time Series Analysis: A Systematic Review
    (Springer Science and Business Media Deutschland GmbH, 2022) Naik, N.N.; Chandrasekaran, K.; Venkatesan, M.; Prabhavathy, P.
    Analysis of time series is a prominent issue in the field of data analysis. With large amount of existing data in time series, multiple algorithms for analyzing time series data are being proposed. A variety of deep learning models are being designed to enhance the diversity of datasets related to time series across different fields. In comparison with the existing methods, only few have incorporated deep neural networks to perform this task. In most of the cases, deep neural networks are being applied for image data but it can also be used for sequential data such as text and audio. Here, we throw light on the recent advancements in hybrid deep learning models which consist of combination of various frameworks of deep neural networks with statistical models that have led to an improvement in time series analysis. Deep learning models are categorized into discriminative, and generative models provide an insight into the data based on the perception of conditional or joint probability. In this paper, we have surveyed newly devised algorithms and limitations of prediction, classification and clustering for time series analysis which describes how the temporal information can be merged into the analysis of the time series data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.