Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item BER performance and energy efficiency of Luby Transform codes with varying BPSK thresholds over the Free Space Optical channel(2013) Prakash, G.; Kulkarni, M.; Sripati, U.In this paper we analyse the Bit Error Rate(BER) performance and energy efficiency of Luby Transform (LT) codes when the signals are Binary Phase shift Keying (BPSK )modulated with varying thresholds and transmitted over the Free Space Optical (FSO)Channel in a wireless sensor network. We model the FSO channel using the Gamma-Gamma distribution function and compare the performance for varied turbulence parameters. We also show how the distribution of the BPSK symbols varies as the distribution varies when the turbulence changes from strong to weak. Use of forward error correction helps in the recovery of the original signal even if some transmitted signals are corrupted during transmission. We analyse the FSO system, which employs Luby Transform Codes, which do not have a fixed code rate. They are considered Rateless, and as many codewords are generated as required to recover all the message bits. We show an increase in SNR and reduced energy per bit for the same BER with LT codes as the BPSK threshold increases. © 2013 IEEE.Item On demand Virtual Machine allocation and migration at cloud data center using Hybrid of Cat Swarm Optimization and Genetic Algorithm(Institute of Electrical and Electronics Engineers Inc., 2017) Sharma, N.K.; Guddeti, G.R.M.This paper deals with the energy saving at the data center using energy aware Virtual Machines (VMs) allocation and migration. The multi-objective based VMs allocation using Hybrid Genetic Cat Swarm Optimization (HGACSO) algorithm saves the energy consumption as well as also reduces resource wastage. Further consolidating VMs onto the minimal number of Physical Machines (PMs) using energy efficient VMs migration, we can shut down idle PMs for enhancing the energy efficiency at a cloud data center. The experimental results show that our proposed HGACSO VM allocation and energy efficient VM migration techniques achieved the energy efficiency and minimization of resource wastage. © 2016 IEEE.Item Energy efficient quality of service aware virtual machine migration in cloud computing(Institute of Electrical and Electronics Engineers Inc., 2018) Sharma, N.; Sharma, P.; Guddeti, R.M.This paper deals with mulit-objective (network aware, energy efficient, and Service Level Agreement (SLA) aware) Virtual Machines (VMs) migration at the cloud data center. The proposed VMs migration technique migrate the VMs from the underutilized PMs to the energy efficient Physical Machines (PMs) at the cloud data center. Further, the multi-objective VMs migration technique not only reduces the power consumption of PMs and switches but also guarantees the quality of service by maintaining the SLA at the cloud data center. Our proposed VMs migration approach can find the good balance between three conflict objectives as compared to other algorithms. Further, the cloudsim based experimental results demonstrate the superiority of our proposed multi-objective VMs migration technique in terms of energy efficiency and also reduces the SLA violation over state-of-the-art VMs migration techniques such as Interquartile Range (IQR), and Random VMs migration techniques at the cloud data center. © 2018 IEEE.Item Experimental Study on Impact of Appliance ID-Based Normalization on SimDataset for Anomalous Power Consumption Classification(Institute of Electrical and Electronics Engineers Inc., 2024) Nayak, R.; Jaidhar, C.D.In terms of annual worldwide energy consumption, buildings use more energy than any other sector. Enhancing buildings' energy efficiency and ensuring security of the appliances requires iden-tifying abnormal power usage. Identifying anomalous power usage is essential for energy conservation. This study suggests an experimental analysis of SimDataset used for detecting micro-moment-based abnormal power usage. Five machine learning-based classifiers-Random Forest (RF), Support Vector Ma-chine (SVM), K Nearest Neighbors (KNN), Naive Bayes (NB), and Decision Tree (DT)-are used to detect unusual consumption of electricity. The Sim-Dataset has undergone binary and multi-class classi-fication. Effect on the performance of the classifiers after the inclusion of new features is examined. Computational complexity of the classifiers is also analyzed. Experimental results showed, the binary and multi-class classification using the RF model with the original dataset, with Min-Max Normalized Power feature and Appliance Id-based Normalized Power feature, produced identical and maximum accuracy, precision, recall, and F1-Score. © 2024 IEEE.Item Optimization of Resource and Energy in Distributed Systems Using Unified Genetic Algorithm(Institute of Electrical and Electronics Engineers Inc., 2025) Dhruthi, G.; Sinchana, N.M.; Annappa, B.; Kumar, N.M.R.Cloud and large distributed systems must ensure resource scheduling, energy management, and resource allocation. However, there exist complex and dynamic workloads, which may cause inefficient resource distribution, increased energy consumption, cost of operation and time delays which ultimately lead to reduced Quality of Experience (QoE). To address these issues the Unified Genetic Algorithm (UGA) is proposed, a proactive approach in optimization which helps achieve relatively better balance between CPU and memory usage across multiple nodes in a distributed system. UGA, was tested using the Materna workload trace and subjected to comparison with other existing load balancing algorithms such as Firefly, Coral Reef Optimization and Novel Family. It is found that UGA is superior with regard to efficiency in scheduling as it has revealed an improvement of 6.72% in average when compared to state of the art algorithms and proved to be beneficial in optimal resource allocation and improvement in system performance. © 2025 IEEE.