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A Hybrid HS-Mean Technique for Efficient Load Balancing in Cloud Computing

  • Kainat Ansar
  • Nadeem Javaid
  • Maheen Zahid
  • Komal Tehreem
  • Hamida Bano
  • Momina Waheed
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 25)

Abstract

Cloud computing is concept of distributing computing for sharing, accessing and storing services through the Internet. It provides platform, software and hardware as service to end users. In this paper concept of fog environment is merged with smart grid (SG) for efficient allocation of resources. Major challenge in cloud computing environment is load balancing. In recent years, the authors have focused on implementing meta-heuristic techniques for load balancing in cloud computing. K-Mean clustering algorithms based on Unsupervised machine learning are designed and implemented for job scheduling in cloud environment. A hybrid scheme based on K-Mean and harmony search algorithm (HSA) algorithms has been designed and implemented in this paper. Performance parameters are optimized by the K-Mean, HSA and our proposed algorithm HS-Mean. We formulate delay problem as a single-objective optimization problem. We propose an hybrid algorithm to solve it. Finally, the results show the efficiency of our algorithm.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kainat Ansar
    • 1
  • Nadeem Javaid
    • 1
  • Maheen Zahid
    • 1
  • Komal Tehreem
    • 1
  • Hamida Bano
    • 1
  • Momina Waheed
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

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