Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Filters

20172

Settings

Author: search.filters.author.Guddeti, G.R.M.Subject: search.filters.subject.Genetic Algorithm

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    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.
  • No Thumbnail Available
    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.