Faculty Publications
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Publications by NITK Faculty
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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 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 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 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 A novel approach for multi-dimensional variable sized virtual machine allocation and migration at cloud data center(Institute of Electrical and Electronics Engineers Inc., 2017) Sharma, N.K.; Guddeti, G.R.M.In this paper, we propose a branch-and-bound based exact algorithm for allocating multi-dimensional variable sized VMs at the cloud data center. Further, an energy efficient VMs migration technique is proposed to reduce the energy consumption and thus avoids the Service Level Agreement (SLA) violation at the cloud data center. © 2017 IEEE.Item Memory-based load balancing algorithm in structured peer-to-peer system(Springer Verlag service@springer.de, 2018) Raghu, G.; Sharma, N.K.; Domanal, S.G.; Guddeti, G.There are several load balancing techniques which are popular used in Structured Peer-to-Peer (SPTP) systems to distribute the load among the systems. Most of the protocols are concentrating on load sharing in SPTP Systems that lead to the performance degeneration in terms of processing delay and processing time due to the lack of resources utilization. The proposed work is related to the sender-initiated load balancing algorithms which are based on the memory. Further to check the performance of the proposed load balancing algorithm, the experimental results carried out in the real-time environment with different type of network topologies in distributed environment. The proposed work performed better over existing load balancing algorithm such as Earliest Completion Load Balancing (ECLB) and First Come First Serve (FCFS) in terms of processing delay and execution time. © Springer Nature Singapore Pte Ltd. 2018.Item Multi-Objective Energy Efficient Virtual Machines Allocation at the Cloud Data Center(Institute of Electrical and Electronics Engineers, 2019) Sharma, N.K.; Guddeti, R.M.R.Due to the growing demand of cloud services, allocation of energy efficient resources (CPU, memory, storage, etc.) and resources utilization are the major challenging issues of a large cloud data center. In this paper, we propose an Euclidean distance based multi-objective resources allocation in the form of virtual machines (VMs) and designed the VM migration policy at the data center. Further the allocation of VMs to Physical Machines (PMs) is carried out by our proposed hybrid approach of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) referred to as HGAPSO. The proposed HGAPSO based resources allocation and VMs migration not only saves the energy consumption and minimizes the wastage of resources but also avoids SLA violation at the cloud data center. To check the performance of the proposed HGAPSO algorithm and VMs migration technique in the form of energy consumption, resources utilization and SLA violation, we performed the extended amount of experiment in both heterogeneous and homogeneous data center environments. To check the performance of proposed HGAPSO with VM migration, we compared our proposed work with branch-and-bound based exact algorithm. The experimental results show the superiority of HGAPSO and VMs migration technique over exact algorithm in terms of energy efficiency, optimal resources utilization, and SLA violation. © 2019 IEEE.