Conference Papers

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

Browse

Filters

2017 - 20182

Settings

Subject: search.filters.subject.Data Center

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
    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.