Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Improving the efficiency of genetic algorithm approach to virtual machine allocation(Institute of Electrical and Electronics Engineers Inc., 2014) Joseph, C.T.; Chandrasekaran, K.; Cyriac, R.Virtual machine (VM) allocation is the process of allocating virtual machines to suitable hosts. This problem is an NP-Hard problem. It can be considered as a variation of the bin-packing problem. Among various solutions that attempt to solve this problem, several approaches that apply Genetic Algorithm have been proposed. This paper proposes a method to improve the efficiency of such approaches. Implementation of the proposed approach shows significant improvements in the runtime, memory used, energy efficiency and SLA violations. © 2014 IEEE.Item Service optimization in cloud using family gene technology(Institute of Electrical and Electronics Engineers Inc., 2014) Ananth, A.; Chandra Sekaran, K.C.Cloud computing is the upcoming technology in current day scenario. It has emerged as a solution for providing resources to the consumers in the form of software, infrastructure or platform as a service. Cloud Service Storage enables users to synchronize their files across devices and also allows them to backup online. The main aim of this paper is to provide service optimization. Scheduling of services is a NP hard problem. Thus exhaustive approaches are not suitable for these kinds of algorithms. This paper presents a genetic algorithm based approach for optimization of services by using family gene technology. Family gene technology is used to classify individuals to different families based on gene parameters and evaluate the fitness function for each individual in that family. Optimization is achieved by mapping the service requests to appropriate service instances which satisfy the request and then by applying family gene based genetic algorithm to those mapped service requests. © 2014 IEEE.Item Novel energy efficient virtual machine allocation at data center using Genetic algorithm(Institute of Electrical and Electronics Engineers Inc., 2015) Sharma, N.K.; Guddeti, G.Increased resources utilization from clients in a smart computing environment poses a greater challenge in allocating optimal energy efficient resources at the data center. Allocation of these optimal resources should be carried out in such a manner that we can save the energy of data center as well as avoiding the service level agreement (SLA) violation. This paper deals with the design of an energy efficient algorithm for optimized resources allocation at data center using combined approach of Dynamic Voltage Frequency Scaling (DVFS) and Genetic algorithm (GA). The performance of the proposed energy efficient algorithm is compared with DVFS. Experimental results demonstrate that the proposed energy efficient algorithm consumes 22.4% less energy over a specified workload with 0% SLA violation. © 2015 IEEE.Item A hybrid bioinspired algorithm for facial emotion recognition using CSO-GA-PSO-SVM(Institute of Electrical and Electronics Engineers Inc., 2015) Vivek, T.V.; Guddeti, G.Human-Computer Interaction gets more natural when the machine can detect human emotions faster and accurate. A lot of research is being carried out in the field of affective computing in order to improve the accuracy with speed. Bio-inspired algorithms for feature extraction and classification stages, has improved accuracy and speed further. In this paper, we propose a hybrid algorithm using CSO (Cat Swarm Optimization) with PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) for emotion recognition (ER). This bio inspired algorithm in conjunction with the support vector machine (SVM) will find an optimal feature set from a bigger set. Results from CK+ (Cohn Kanade) [1] dataset demonstrate that our proposed method using CSO-GA-PSOSVM outperforms Emotion Recognition System with CSOSVM by 10.5% in accuracy. This paper also proposes a new E-Learning [2] system to demonstrate its effectiveness and efficiency in real-time scenario. The proposed algorithm is applied over the facial characteristics captured from students in teaching-learning environment. The optimized feature vector obtained is passed to the SVM classifier for classification. Experimental results yield 99% classification accuracy in a person dependent mode with six basic emotions namely Happy, Sad, Anger, Disgust, Surprise and Neutral. © 2015 IEEE.Item A novel energy efficient resource allocation using hybrid approach of genetic DVFS with bin packing(Institute of Electrical and Electronics Engineers Inc., 2015) Sharma, N.K.; Guddeti, G.Increased resources utilization from several clients in a smart computing environment poses a key challenge in allocating optimal energy efficient resources at the data center. Allocation of these optimal resources should be carried out in such a manner that we can reduce the energy consumption of the data center and also avoid the service level agreement (SLA) violation. This paper deals with the development of an energy efficient algorithm for optimal resources allocation at the data center using hybrid approach of the Dynamic Voltage Frequency Scaling (DVFS), Genetic algorithm (GA) and Bin Packing techniques. The performance of the proposed hybrid approach is compared with Genetic Algorithm, DVFS with Bin Packing, DVFS without Bin Packing techniques. Experimental results demonstrate that the proposed energy efficient algorithm consumes 22.4% less energy as compared to the DVFS with Bin Packing technique over a specified workload with 0% SLA violation. © 2015 IEEE.Item A hybrid community detection based on evolutionary algorithms in social networks(Institute of Electrical and Electronics Engineers Inc., 2016) Jami, V.; Guddeti, G.R.In social network analysis, community detection is an optimization problem of finding out partitions of maximum modularity density from a network. It is a NP-hard problem which can be done using evolutionary algorithms such as Particle Swarm Optimization, Cat Swarm Optimization, Genetic Algorithm and Genetic Algorithm with Simulated Annealing. In this work, we proposed an algorithm based on Genetic Algorithm with Simulated annealing for not being trapped into local optimal solution which is giving more better results. The main motto of our work is to get better communities with low computation cost. We tested our proposed algorithm on three standard datasets such as Zachary's Karate Club Dataset, American College Football and Dolphin Social Network Dataset. Experimental results demonstrate that our proposed algorithm outperforms state of the art approaches. © 2016 IEEE.Item Multi-Objective Resources Allocation Using Improved Genetic Algorithm at Cloud Data Center(Institute of Electrical and Electronics Engineers Inc., 2017) Sharma, N.K.; Guddeti, G.R.M.In this paper, a new novel Improved Genetic Algorithm (IGA) is proposed to determine the near optimal solution for multi-objective resources allocation at the green cloud data center of smart grid. However, instead of randomly generating the initial chromosomes for crossover and mutation operations the modified first decreasing (MFD) technique generates better solution for the initial population. The proposed work saves the energy consumption, minimizes the resource wastage, and reduce the algorithm's computation time at the cloud data center. The Cloud-sim simulator based experimental results show that our proposed approach improves the performance of the data center in terms of energy efficiency and average resources utilization when compared to the state-of-the-art VMs allocation approaches i.e. First Fit, Modified First Decreasing (MFD) and, Grouping Genetic Algorithm (GGA). © 2016 IEEE.Item GA based optimal location and size of the distributed generators in distribution system for different load conditions(Institute of Electrical and Electronics Engineers Inc., 2017) Shivarudraswamy, R.; Gaonkar, D.N.; Sabhahit, J.N.In the recent past factors such as apprehensions over impacts of environmental aspects, distribution network improvement conditions, and other subsidised programs of the government have affected the distributed generators (DG) units count in commercial and domestic electrical power output. It is known that the optimal size and optimal placement of DG units may lead to low power losses, high voltage profiles. In real time scenario identifying an appropriate DG location and size is hard because of various system constraints. Therefore a method which can identify a optimum DG location and size is necessary. Using the method a power system with an acceptable reliability level and voltage profile can be designed. To serve this purpose in this paper a procedure/method which can calculate the optimum location for DG placement and appropriate DG size has been proposed. This method has been evaluated using a 14 bus distribution system. The optimization method has been designed using genetic algorithm (GA) and also for time varying loads. © 2016 IEEE.Item A bio-inspired model to provide data security in cloud storage(Institute of Electrical and Electronics Engineers Inc., 2017) Hitaswi, N.; Chandrasekaran, K.The demand for cloud computing is increasing rapidly because of the advantages it provides to the customers like, pay as you use, self-serving, elastic, sharing of resources, ease of use, and accessibility. Due to the increase in the usage of the technology, there exists a high chance of compromising the security of the data being stored on the cloud. The major hindrance in the usage of the technology is the security concerns which accompany it. This increases the demand for a robust security mechanism to protect the data on the cloud. So as to overcome this drawback of cloud computing, encrypting the data to be stored on the cloud is one of the solutions. As part of this paper, a security mechanism to improve the security of data in cloud storage is suggested. The security mechanism used is inspired by the bio-inspired genetic algorithm. The inspiration behind the proposed security model is an amalgamation of genetic algorithm and attribute based encryption. As per the methodology proposed the data need to be encrypted before being stored on the cloud. This way the cloud service provider is unaware of the data being stored and even if the data is compromised to some third party, there is no information leakage. © 2016 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.