Conference Papers

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    A gene expression based quality of service aware routing protocol for mobile ad hoc networks
    (2013) Kubusada, Y.; Mohan, G.; Manjappa, M.; Guddeti, G.
    Mobile Ad Hoc Network (MANET) is a collection of infrastructure less multi-hop wireless mobile nodes which communicate together to achieve the global task. Despite lack of centralized control these mobile nodes still coordinate together to deliver the message to the destination node. MANET is gaining its popularity due to its easy deployment and self-organizing ability. In spite of its unique characteristics, mobility of mobile nodes causes frequent link breakups in MANET and thus makes route setup and maintenance a critical and challenging task. As real time and multimedia applications are increasing, there is a need of an efficient Quality of Service (QoS) aware routing protocol for MANET to support such applications. In the present work, the authors proposed an efficient QoS aware routing protocol for MANET based on upcoming Gene Expression Programming. In the proposed work, the information regarding the availability of resources is managed by a resource management module, which assists in selecting the resource rich path. Further, a theoretical proof is given for the proposed model for its correctness. The results are compared with the state of art artificial neural network and support vector regression methods from the performance evaluation point of view and the results are encouraging. © 2013 Springer Science+Business Media.
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    Virtual Machine Migration Triggering using Application Workload Prediction
    (Elsevier, 2015) Raghunath, B.R.; Annappa, B.
    Dynamic provisioning of physical resources to Virtual Machines (VMs) in virtualized environments can be achieved by (i) vertical scaling-adding/removing attached resources from existing virtual machine and (ii) horizontal scaling-adding a new virtual machine with additional resources. The live migration of virtual machines across different Physical Machines (PMs) is a vertical scaling technique which facilitates resource hot-spot mitigation, server consolidation, load balancing and system level maintenance. It takes significant amount of resources to iteratively copy memory pages. Hence during the migration there may be too much overload which can affect the performance of applications running on the VMs on the physical server. It is better to predict the future workload of applications running on physical server for early detection of overloads and trigger the migration at an appropriate point where sufficient number of resources are available for all the applications so that there will not be performance degradation. This paper presents an intelligent decision maker to trigger the migration by predicting the future workload and combining it with predicted performance parameters of migration process. Experimental results shows that migration is triggered at an appropriate point such that there are sufficient amount of resources available (15-20% more resources than high valued threshold method) and no application performance degradation exists as compared to properly chosen threshold method for triggering the migration. Prediction with support vector regression has got decent accuracy with MSE of 0.026. Also this system helps to improve resource utilization as compared to safer threshold value for triggering migration by removing unnecessary migrations. © 2015 The Authors.
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    Accurate Estimation for Stability of Slope and Partition Over Old Underground Coal Workings Using Regression-Based Algorithms
    (Springer Science and Business Media Deutschland GmbH, 2022) Dorthi, K.; Kumar, A.; Ram Chandar, K.R.
    Numerical modeling simulation has found to be best solution for predicting slope and partition stability over old underground coal workings. But it has taken huge time to complete a single simulation model. In this regard, machine learning-based framework is used to predict the stability of old galleries. A case study is taken up in opencast mine and simulation is carried out using numerical model and machine learning-based framework. Framework has shown an overall accuracy of 94–95% for different slope and partition stability. Framework shows a speedup of 2366 × against numerical simulator. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.